diff --git a/.github/workflows/config.yml b/.github/workflows/config.yml index a31c84f7cc..956a686c1e 100644 --- a/.github/workflows/config.yml +++ b/.github/workflows/config.yml @@ -364,6 +364,7 @@ jobs: with: working_directory: docs/technical_reference_guide root_file: ResStockTechnicalReferenceGuide.tex + args: -pdf -latexoption=-file-line-error -latexoption=-interaction=nonstopmode -output-directory=_build - name: Save documentation uses: actions/upload-artifact@v4 diff --git a/.gitignore b/.gitignore index 2aa1b19a41..b8d223496d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,19 +1,5 @@ /docs/technical_development_guide/_build -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.aux -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.bcf -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.lof -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.log -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.lot -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.out -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.pdf -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.run.xml -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.toc -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.upa -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.bbl -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.blg -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.fdb_latexmk -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.fls -/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.upb +/docs/technical_reference_guide/_build /lib /measures/UpgradeCosts/tests/in* /measures/UpgradeCosts/tests/*.xml diff --git a/docs/technical_reference_guide/1_Introduction.tex b/docs/technical_reference_guide/1_Introduction.tex index 3d2117246d..e649653797 100644 --- a/docs/technical_reference_guide/1_Introduction.tex +++ b/docs/technical_reference_guide/1_Introduction.tex @@ -12,7 +12,7 @@ \section{Overview and Primary Use Applications} ResStock answers two primary questions: (1) How and when is energy used in the U.S.~residential building stock? and (2) What are the impacts of technological and behavioral changes in U.S.~homes? Specifically, ResStock quantifies energy use across geographical locations, demographic groups, building types, fuels, end uses, and time of day. Additionally, it details the impact of efficiency, fuel changes, or flexibility measures: total changes in the amount of energy used by measure; where or in what use cases efficiency or technology change measures save energy; when or at what times of day savings occur; and which building stock or demographic segments have the biggest savings potential. -This type of building stock energy model can be conducted using a range of approaches, varying on a spectrum from simple representation and fast execution or complex representation and slow execution. Each approach has benefits and trade-offs. The National Energy Modeling System used by the EIA is an example of a simple, fast method. This system models the entire U.S.~energy system at the census region level, and its results for the building stock have low spatial, temporal, and subsector granularity. On the other hand, modeling each individual building within the building stock is an example of a complex, slow method. This approach is impossible to implement in practice due to the lack of building-level data necessary to develop the model, and can lead to false confidence in results if not underpinned by appropriate data. Additionally, if appropriate data did exist and the model could be developed, this approach would offer a high granularity of results, but gives more detail than is needed for most applications and is highly impractical to update or run frequently. +This type of building stock energy model can be conducted using a range of approaches, varying on a spectrum from simple representation and fast execution or complex representation and slow execution. Each approach has benefits and trade-offs. The National Energy Modeling System used by the EIA is an example of a simple, fast method. This system models the entire U.S.~energy system at the census region level, and its results for the building stock have low spatial, temporal, and subsector granularity. On the other hand, modeling each individual building within the building stock is an example of a complex, slow method. This approach is impossible to implement in practice due to the lack of building-level data necessary to develop the model, and can lead to false confidence in results if not underpinned by appropriate data. Additionally, if appropriate data did exist and the model could be developed, this approach would offer a high granularity of results, but would provide more detail than needed for most applications and would be highly impractical to update or run frequently. The ResStock approach is positioned between these two extremes, providing highly granular housing stock data to capture the diversity of housing and occupants while maintaining a usable execution speed. Three advantages of the ResStock approach are: (1) subhourly detail; (2) modeling of upgrade measure interaction, controls, and demand flexibility; and (3) the ability to post-process the data to slice results (e.g., by location, household income, fuel types, building size) and extract a wide array of insights from the simulations, including distributional impacts---how costs and benefits are distributed across different groups of households. This approach strikes a balance by presenting enough information to answer its two driving questions while remaining computationally tractable. diff --git a/docs/technical_reference_guide/2_ResStockStructureAndSampling.tex b/docs/technical_reference_guide/2_ResStockStructureAndSampling.tex index 0c735e920c..c52d028f7f 100644 --- a/docs/technical_reference_guide/2_ResStockStructureAndSampling.tex +++ b/docs/technical_reference_guide/2_ResStockStructureAndSampling.tex @@ -8,19 +8,19 @@ \chapter{ResStock Workflow} \label{sec:workflow} \section{Overview} -ResStock is an interconnected set of modeling assumptions, workflows, and published datasets within the software ecosystem of DOE's flagship building energy modeling software \href{https://energyplus.net/}{EnergyPlus}, \href{https://openstudio.net/}{OpenStudio}, and \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/}{OpenStudio-HPXML}. EnergyPlus is open-source software used to simulate the physics-based energy performance of individual buildings, including heating, cooling, lighting, appliances, and ventilation systems. It is widely used by engineers and architects to simulate, optimize, and evaluate building designs for energy efficiency, fuel changes, and comfort. EnergyPlus is the building energy simulation engine that ultimately performs the physics-based simulations within ResStock. OpenStudio is an open-source software development kit that allows for programmatic creation and management of building energy models in EnergyPlus. It simplifies the process of simulating building energy performance through software automation, making it easier for users to simulate and interact with the building energy model and results. OpenStudio-HPXML (OS-HPXML) is a tool that bridges the OpenStudio platform with the Home Performance XML (HPXML) data standard, enabling accurate and consistent modeling and simulation of residential building energy performance. It automates the process of creating HPXML files, which describe residential building characteristics commonly used during energy audits, and converts them into EnergyPlus-compatible models, facilitating the evaluation of energy efficiency measures in homes. This OS-HPXML foundation makes ResStock compatible with other software within the residential modeling ecosystem such as \href{https://www.nrel.gov/buildings/beopt.html}{BEopt\textsuperscript{TM}}, \href{https://www.energy.gov/eere/buildings/articles/home-energy-score}{Home Energy Score\textsuperscript{TM}}, \href{https://docs.urbanopt.net/}{URBANopt\textsuperscript{TM}}, and \href{https://github.com/NREL/OCHRE}{OCHRE\textsuperscript{TM}}. On top of the core building energy modeling, ResStock adds a synthesized U.S.~housing stock and demographic characterization, batch processing of a large number of EnergyPlus models, and post-processing workflows to add emissions, utility cost, and energy burden data. The housing building stock characterization sits on top of EnergyPlus, OpenStudio, and OpenStudio-HPXML to automate the creation, simulation, and processing of the representative building energy models generated through this characterization, and a large database of published simulation results from the stock model. +ResStock is an interconnected set of modeling assumptions, workflows, and published datasets within the software ecosystem of DOE's flagship building energy modeling software \href{https://energyplus.net/}{EnergyPlus}, \href{https://openstudio.net/}{OpenStudio}, and \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/}{OpenStudio-HPXML}. EnergyPlus is open-source software used to simulate the physics-based energy performance of individual buildings, including heating, cooling, lighting, appliances, and ventilation systems. It is widely used by engineers and architects to simulate, optimize, and evaluate building designs for energy efficiency, fuel changes, and comfort. EnergyPlus is the building energy simulation engine that ultimately performs physics-based simulations within ResStock. OpenStudio is an open-source software development kit that allows for programmatic creation and management of building energy models in EnergyPlus. It simplifies the process of simulating building energy performance through software automation, making it easier for users to simulate and interact with the building energy model and results. OpenStudio-HPXML (OS-HPXML) is a tool that bridges the OpenStudio platform with the Home Performance XML (HPXML) data standard, enabling accurate and consistent modeling and simulation of residential building energy performance. It automates the process of creating HPXML files, which describe residential building characteristics commonly used during energy audits, and converts them into EnergyPlus-compatible models, facilitating the evaluation of energy efficiency measures in homes. This OS-HPXML foundation makes ResStock compatible with other software within the residential modeling ecosystem such as \href{https://www.nrel.gov/buildings/beopt.html}{BEopt\textsuperscript{TM}}, \href{https://www.energy.gov/eere/buildings/articles/home-energy-score}{Home Energy Score\textsuperscript{TM}}, \href{https://docs.urbanopt.net/}{URBANopt\textsuperscript{TM}}, and \href{https://github.com/NREL/OCHRE}{OCHRE\textsuperscript{TM}}. On top of the core building energy modeling, ResStock adds a synthesized U.S.~housing stock and demographic characterization, batch processing of a large number of EnergyPlus models, and post-processing workflows to add emissions, utility cost, and energy burden data. The housing building stock characterization sits on top of EnergyPlus, OpenStudio, and OpenStudio-HPXML to automate the creation, simulation, and processing of the representative building energy models generated through this characterization, and a large database of published simulation results from the stock model. -ResStock is an archetype-based building stock model of the U.S.~residential building stock and is classified as a Q4 physics-simulation model by the building stock energy model classification framework~\citep{Langevin2020}. The model has five major steps (Figure \ref{fig:workflow_overview}): (1) Residential Building Stock Characterization, (2) Sampling, (3) Building Energy Model Articulation, (4) Batch Simulation, and (5) Results and Publication. The next few subsections briefly introduce each of these topics. +ResStock is an archetype-based building stock model of the U.S.~residential building stock and is classified as a Q4 physics-simulation model by the building stock energy model classification framework~\citep{Langevin2020}. The model has five major steps (Figure \ref{fig:workflow_overview}): (1)Stock Characterization, (2) Sampling, (3) Building Energy Model Articulation, (4) Batch Simulation, and (5) Results and Publication. The next few subsections briefly introduce each of these topics. % Workflow overview \begin{figure} \centering - \includegraphics[width=1\linewidth]{images/ResStock-Workflow-Graphic-Simple.pdf} + \includegraphics[width=1\linewidth]{images/Figure 1.pdf} \caption{A high-level overview of the ResStock workflow steps and what occurs during those steps} \label{fig:workflow_overview} \end{figure} -\section{Residential Building Stock Characterization} +\section{Stock Characterization} ResStock characterizes the U.S.~residential building stock and the associated occupants using a probabilistic representation of building and household characteristics developed using the best available data. Much of the underlying data for the U.S.~residential stock comes from national survey data. These surveys include the \href{https://data.census.gov/}{U.S.~Census}, the \href{https://www.census.gov/programs-surveys/acs/microdata.html}{Public Use Microdata Sample} (a microdata version of the American Community Survey [ACS]), the \href{https://www.census.gov/programs-surveys/ahs.html}{American Housing Survey (AHS)}, and the EIA's \href{https://www.eia.gov/consumption/residential/}{Residential Energy Consumption Survey (RECS)}. These surveys provided weighted survey samples with different building characteristics (for example: heating fuel, vintage, number of occupants, floor area, etc.) that ResStock leverages. ResStock takes this survey microdata, processes it, and connects it to other surveys to develop housing characteristic probability distributions. @@ -43,6 +43,7 @@ \section{Residential Building Stock Characterization} The input distributions also capture important correlations between building characteristics, sometimes referred to as conditional dependencies. For example, in Los Angeles, CA, in the 1960s, many residential buildings were constructed, while other cities may have seen growth at different periods. This is captured in ResStock by making the Vintage characteristic conditionally dependent on the location of the housing unit, so different locations will have different distributions of housing age. Another example is that energy codes became more widespread in the late 1970s, causing minimum insulation values in new homes to increase. This relationship is captured by making the Insulation characteristics, like wall insulation, conditionally dependent on vintage. Through these correlations taken from the survey data, a network of characteristics and conditional dependencies are assigned through ResStock characteristic variables. It is these conditional dependent distributions of the characteristics that create the residential building stock characterization in ResStock. Information about how these distributions are created can be found in Section \ref{hc_overview}. Detailed information about each characteristic, assumptions, dependencies, and data sources can be found in Section \ref{sec:resstock_inputs}. +\section{Sampling} ResStock does not model actual buildings (for example: the apartment complex at 123 Main Street). Instead, the housing characteristic distributions are sampled hundreds of thousands of times---typically 550,000---to create a synthetic stock representation of U.S.~housing units. Each sampled housing unit is assigned an option for each of the ResStock housing characteristics. Within the ResStock workflow the full set of sampled housing units and their associated characteristics is referred to as the \texttt{buildstock.csv}. An illustrative example of some characteristics of a ResStock model is shown in Figure \ref{fig:illustrative_sample}. More information about how the sampling is performed can be found in Section \ref{sec:sampling_methodology}. Each of the 550,000 representative samples in the \texttt{buildstock.csv} can be thought of as an archetype residential housing unit description, meaning that each synthetic building represents approximately 250 real U.S.~housing units. \begin{figure} @@ -53,7 +54,7 @@ \section{Residential Building Stock Characterization} \end{figure} \section{Building Energy Model Articulation} \label{sec:bem_articulation} -After sampling is complete, the \texttt{buildstock.csv} file contains the synthetic housing stock, with each row representing a sampled housing unit and each column containing a different ResStock housing characteristic. Each housing characteristic has an option assigned for each sampled housing unit in the synthetic stock. The table itself is a set of string values that need to be transformed into a building energy model for each sampled housing unit. The transformation of a single line of characteristic options, (see Figure \ref{fig:illustrative_sample} for an example) into an EnergyPlus building energy model is referred to as the model articulation process. +After sampling is complete, the \texttt{buildstock.csv} file contains the synthetic housing stock, with each row representing a sampled housing unit and each column corresponding to a characteristics and the field within that column representing a sampled option. Each housing characteristic has an option assigned for each sampled housing unit in the synthetic stock. The table itself is a set of string values that need to be transformed into a building energy model for each sampled housing unit. The transformation of a single line of characteristic options (see Figure \ref{fig:illustrative_sample} for an example) into an EnergyPlus building energy model is referred to as the model articulation process. ResStock can be run either just for ``baseline'' energy use---i.e., energy use in the present-day housing stock---or with ``upgrades'' that will simulate both the baseline as well as a technical potential of different technologies to change energy use and associated metrics. The foundation of each of these workflows is the model articulation process. This document discusses primarily the ResStock baseline state since each public data release of upgrade measures has its own accompanying detailed technical documentation. @@ -62,7 +63,7 @@ \section{Building Energy Model Articulation} \label{sec:bem_articulation} Many housing characteristic options are directly used in the creation of the EnergyPlus models, but some are just structural in developing the probability distributions. For example, the ASHRAE IECC Climate Zone 2004 characteristics set the \texttt{site\_iecc\_zone} and \texttt{site\_type} ResStock arguments (Section \ref{sec:ashrae_2004_tsv}), but Location Region is just used as a dependency to define other characteristics that impact the energy modeling. Housing characteristics that do not assign an argument are called meta-parameters, for example Federal Poverty Level. These meta-parameters are often used as intermediate dependencies in other characteristics to separate key housing characteristics that influence the energy simulation. For example, the Federal Poverty Level characteristic and options are used to correlate income to appliance ownership and efficiency. The options and arguments for each ResStock housing characteristic are discussed in detail in Section \ref{sec:resstock_inputs}. -\section{OpenStudio--HPXML} +\subsection{OpenStudio--HPXML} % ResStock arguments to OpenStudio-HPXML arguments, the HPXML file, class abstraction of the HPXML schema, creation of HPXML file, and the translation of the OpenStudio model. The next step in the workflow is converting ResStock arguments to the OpenStudio-HPXML arguments to create the HPXML file. OpenStudio-HPXML uses a series of OpenStudio measures to generate an EnergyPlus model for each sample based on the building and occupant characteristics defined by ResStock (Figure \ref{fig:os-hpxml}). In many cases, ResStock relies upon the OpenStudio-HPXML default arguments and calculations. The OpenStudio measures called in the workflow are: @@ -95,7 +96,7 @@ \section{Batch Simulation} A successful run of 550,000 samples with no upgrades, a 15-minute time step, no errors, and no queue time can typically be run on NREL's HPC system within a few hours and creates about 500 GB of output. -\section{Upgrade Specification}\label{sec:upgrades} +\subsection{Upgrade Specification}\label{sec:upgrades} In ResStock, most of the details of upgrade specification occur directly in the project file under the \href{https://buildstockbatch.readthedocs.io/en/v2023.10.0/project_defn.html#upgrade-scenarios}{upgrades key}, using fields from the \texttt{options\_lookup.tsv} file specified in logic blocks. Options specified for upgrades include which segment of the baseline the upgrade should be applied to, cost multipliers, and the ``reference'' case, which is important if doing a comparison against a business-as-usual scenario (especially for costs). If the upgrades section is not specified, only the building stock baseline will be simulated. Details of the upgrades associated with each ResStock data release can be found in the supporting upgrade measure documentation. ResStock upgrades are deterministic, not probabilistic, similar to how the baseline is constructed. You can specify, for example, that all housing units with a specific existing air conditioner in baseline get a specific new air conditioner in upgrade. Or you can use more complex logic and specify 10 different air conditioners in upgrade, based on any characteristic or combination of characteristics. But each housing unit will deterministically receive a specific new air conditioner based on the logic. This can cause challenges. One example is if specifying a new air conditioner for housing units that don't already have air conditioning, you might ideally specify a new, probabilistic range of cooling setpoints for those homes. However, this is not possible. This is why ResStock specifies cooling setpoints for every housing unit, whether or not the unit has air conditioning: so that if an upgrade run assigns air conditioning to that housing unit, the resulting setpoints are appropriately diverse. One can think of this situation as the housing unit's preference of a cooling setpoint if one had a cooling system. There are several other similar parameter option specifications in baseline that are not used to model the baseline but are available in case of certain upgrade option assignments. @@ -150,9 +151,9 @@ \section{Methods for Creating Housing Characteristic Distributions}\label{hc_met ResStock updates housing characteristic distributions to the latest release of longitudinal surveys whenever possible and incorporates new data sources when a need and a data source are both identified. The housing characteristics captured in \href{https://resstock.readthedocs.io/en/v3.3.0/}{ResStock release v.3.3.0} represent the existing U.S.~housing stock as of approximately 2019 (housing stock: {\raise.17ex\hbox{$\scriptstyle\sim$}}2019, weather: 2018 or TMY3, and appliances: {\raise.17ex\hbox{$\scriptstyle\sim$}}2020). ResStock models all 50 states and the District of Columbia. ResStock does not currently model U.S.~territories such as Puerto Rico or Guam. -To generate a housing characteristic's distribution, we generate distributions as normalized cross tabulations of the variables and their dependencies using the sample weight in the source data. We select dependencies from the available variables in the surveys based on a combination of engineering judgment, empirical evidence of correlation, and the need to balance between data fidelity and variability. For example, we know there is likely to be a relationship between having natural gas as a space heating fuel and having natural gas as a water heating fuel since there is already natural gas service to the home, so we set Heating Fuel as a dependency for Water Heating Fuel. Engineering judgment can help pre-select a set of variables to correlate with the parameter. Then, the correlation is verified with empirical evidence that may include correlation matrices, statistical tests, and plots or tabulations demonstrating the significance of dependency variables to the output distributions. Input characteristics are constantly being evaluated and updated as better data are identified. +To generate a housing characteristic's distribution, we generate distributions as normalized cross tabulations of the variables and their dependencies using the sample weight in the source data. We select dependencies from the available variables in the surveys based on a combination of engineering judgment, empirical evidence of correlation, and the need to balance between data fidelity and variability. For example, we know there is likely to be a relationship between having natural gas as a space heating fuel and having natural gas as a water heating fuel since there is already natural gas service to the home, so we set Heating Fuel as a dependency for Water Heating Fuel. Engineering judgment can help pre-select a set of variables to correlate with the parameter. The correlation is then verified with empirical evidence that may include correlation matrices, statistical tests, and plots or tabulations that demonstrate the significance of dependency variables in the output distributions. Input characteristics are constantly being evaluated and updated as better data are identified. -To ensure data fidelity and representativeness, each row in a distribution is generally informed by at least 10 samples in the source data. The number of dependencies to include is limited by the size of the source data, since the data will be sliced over many parameters to generate each row of the distribution. For example, smaller source datasets can afford splitting over fewer or less granular dependencies before the data is spread too thin. In such cases, it becomes necessary to choose variables that best capture the variability in the parameter. To do this, we use graph theory and Bayesian inference to calculate the incremental information gain by each candidate variable, which ranks them for selection. Sometimes the dependency selection is further limited to keep the distribution to a manageable file size for workflow purposes. For example, a distribution with a dependency on County will likely have few other dependencies, as doing so will result in an oversized distribution that cannot be stored in the GitHub repository and is otherwise difficult to work with since there are over 3,100 counties in the United States. +To ensure data fidelity and representativeness, each row in a distribution is generally informed by at least 10 samples in the source data. The number of dependencies to include is limited by the size of the source data, since the data will be sliced over many parameters to generate each row of the distribution. For example, smaller source datasets can afford to split over fewer or less granular dependencies before the data is spread too thin. In such cases, it becomes necessary to choose variables that best capture the variability in the parameter. To do this, we use graph theory and Bayesian inference to calculate the incremental information gain by each candidate variable, which ranks them for selection. Sometimes the dependency selection is further limited to keep the distribution to a manageable file size for workflow purposes. For example, a distribution with a dependency on County will likely have few other dependencies, as doing so will result in an oversized distribution that cannot be stored in the GitHub repository and is otherwise difficult to work with since there are over 3,100 counties in the United States. In addition to strategic dependency selection, ResStock has two other approaches for dealing with low samples or missing dimensionality in the source data: fallback rules for dimensional coarsening and dimensional blending. Some characteristics in ResStock have several granularity options available, e.g., Vintage (housing unit age grouped into 10 bins) vs.~Vintage ACS (housing unit age grouped into 6 bins). These granularity options help bridge between source data that have different native resolutions to connect the derived distributions. They are also used in fallback rules and dimensional coarsening to address low samples. A common practice in ResStock is to fill the cross tabulation using the native resolution of the dependency variables. Then where there’s insufficient sample count, ResStock pulls the distribution from higher granularity variables to fill the rows. For example, state-level tabulation can be used to fill or supplement the rows with low samples that are natively at the county level. This dimensional coarsening may result in some rows sharing similar probability distributions but at the benefit of filled data gaps and higher sample confidence. The fallback rules are what define these processing sequences so that some or all dependency variables can be coarsened incrementally until all rows reach enough samples. Dimensional coarsening is commonly done over geography, climate zone, vintage, building type, floor area, and income by grouping together similar options or options believed to influence energy consumption similarly (e.g., neighboring geographies). In Section \ref{sec:resstock_inputs} we discuss in the assumptions section for each variable if dimensional coarsening is used. @@ -160,23 +161,23 @@ \section{Methods for Creating Housing Characteristic Distributions}\label{hc_met The full cross-tabulation of a parameter and its dependencies can sometimes give rise to impossible or highly improbable combinations of characteristics, e.g., single-family houses that are over 8 stories tall. These combinations are assigned a parameter value of “void,” and prune rules are used in the distribution development to ensure that such combination will never be sampled. If such combination is accidentally sampled (perhaps due to error in upstream housing characteristics), then this will be caught immediately since ``voids'' are supposed to be un-sample-able. Some characteristic combinations are realistic but may be pruned due to limitations in the upstream modeling workflow. For example, in ResStock, single-family detached houses that are 0--1,499 ft\textsuperscript{2} with attached garages can currently only have a single-car garage. This is due to ResStock assuming a specific aspect ratio for building footprint and modeling constraints restricting that the garage cannot be larger or deeper than the livable space. -Many of the housing characteristic distributions are validated by comparing their marginal distribution by each dependency with those of the source data. This is to ensure that any special handling of the data to address low samples or missing dimensionality do not deviate the distributions significantly from the source data. The parity maintained with the source data also means the housing characterization in ResStock inherits the same level of survey biases or uncertainty as those that exist in the source datasets. For example, ResStock’s characterization of Mobile Homes has higher uncertainty than any other housing types as mobile homes are the least common of the major housing types (single-family, multifamily 2--4 units, etc.), and fewer data points exist for them in the source datasets. While using the survey sample weight to construct the distributions helps ensure they are representative of the U.S.~housing stock, ResStock does compare the effect of using different types of sample weight when they are available in certain surveys, such as RECS. +Many of the housing characteristic distributions are validated by comparing their marginal distribution by each dependency with those of the source data. This is to ensure that any special handling of the data to address low samples or missing dimensionality does not deviate the distributions significantly from the source data. The parity maintained with the source data also means the housing characterization in ResStock inherits the same level of survey biases or uncertainty as those that exist in the source datasets. For example, ResStock’s characterization of Mobile Homes has higher uncertainty than any other housing types as mobile homes are the least common of the major housing types (single-family, multifamily 2--4 units, etc.), and fewer data points exist for them in the source datasets. While using the survey sample weight to construct the distributions helps ensure they are representative of the U.S.~housing stock, ResStock does compare the effect of using different types of sample weight when they are available in certain surveys, such as RECS. \section{Sampling Methodology} \label{sec:sampling_methodology} - With the full conditional probability network of inputs defined, ResStock samples the inputs to create the synthetic housing stock. The input file structure and dependency network determines the order in which each characteristic is sampled. Sampling starts with housing features that have no dependencies and next moves to characteristics that have dependencies on the first level of characteristics sampled. This process proceeds until all inputs are sampled and defined. For example, Figure \ref{fig:ex_build_char_distrs} shows an example set of housing characteristic distributions that are interconnected by dependencies. To create a building model in this hypothetical network, the census division is sampled first (and Middle Atlantic is chosen). Then the vintage of the model is sampled based on the distribution of vintage for the chosen census division (1980s is chosen). Next, the heating fuel is sampled according to the distribution for the chosen vintage (natural gas is chosen), and this process repeats until all housing characteristics are determined. + With the full conditional probability network of inputs defined, ResStock samples the inputs to create the synthetic housing stock. The input file structure and dependency network determine the order in which each characteristic is sampled. Sampling starts with housing features that have no dependencies and next moves to characteristics that have dependencies on the first level of characteristics sampled. This process proceeds until all inputs are sampled and defined. For example, Figure \ref{fig:ex_build_char_distrs} shows an example set of housing characteristic distributions that are interconnected by dependencies. To create a building model in this hypothetical network, the census division is sampled first (and Middle Atlantic is chosen). Then the vintage of the model is sampled based on the distribution of vintage for the chosen census division (1980s is chosen). Next, the heating fuel is sampled according to the distribution for the chosen vintage (natural gas is chosen), and this process repeats until all housing characteristics are determined. \begin{figure} \centering - \includegraphics[width=1\linewidth]{images/cond_prob.png} + \includegraphics[width=1\linewidth]{images/Figure 4.pdf} \caption{Example of interconnected building characteristic distributions} \label{fig:ex_build_char_distrs} \end{figure} To create a full representative synthetic housing stock for the United States, ResStock employs quota-based sampling. In quota-based sampling, building models are created until the specified number of samples (i.e., the quota) is reached. Sampling starts with the most likely characteristics or most common housing unit in the United States, and then continues filling out increasingly less-likely combinations of characteristics until the quota is reached. This approach creates building models with equal sample weight, meaning each sampled housing unit represents the same number of housing units in the real housing stock. This is a product of quota-based sampling where the likelihood of a building characteristic is directly reflected in the number of times that characteristic is sampled instead of being included in the sample weight. -The quota-based sampling approach is different from purely random sampling (e.g., Monte Carlo) where the samples can come from anywhere in the distributions. Random samples thus may not be representative until many samples are drawn. In quota-based sampling, the quota is multiplied by the probability distributions to determine how many samples will have certain characteristics. If natural gas accounts for 50\% of space heating in the marginal distribution, then one in two samples will be decidedly heated by gas, and this holds true for a quota of two or a quota of a million. However, larger quotas are required to sample uncommon characteristics due to the discretization effect (i.e., a characteristic of 0.1\% probability will not show up in a sampling quota of 500 as 0.1\%*500 = 0.5, which is less than one sample). It's worth noting that the characteristics doesn't need to be uncommon at the national scale for this problem to occur. Even if a characteristic has 1\% probability nationwide, we will not get the expected 0.01 * 550,000 = 5,500 samples in a national run and in fact may get zero samples if the characteristic has dependencies that will cause it to be sampled within thousands of slices of (quota of) less than 100 samples. While such extreme cases are uncommon, most characteristics do have biases on their national-scale saturation over what one would expect based on the housing characteristic distribution due to this quirk of quota sampling. +The quota-based sampling approach is different from purely random sampling (e.g., Monte Carlo) where the samples can come from anywhere in the distributions. Random samples thus may not be representative until many samples are drawn. In quota-based sampling, the quota is multiplied by the probability distributions to determine how many samples will have certain characteristics. If natural gas accounts for 50\% of space heating in the marginal distribution, then one in two samples will be decidedly heated by gas, and this holds true for a quota of two or a quota of a million. However, larger quotas are required to sample uncommon characteristics due to the discretization effect (i.e., a characteristic of 0.1\% probability will not show up in a sampling quota of 500 as 0.1\%*500 = 0.5, which is less than one sample). It is worth noting that the characteristics do not need to be uncommon at the national scale for this problem to occur. Even if a characteristic has 1\% probability nationwide, we will not get the expected 0.01 * 550,000 = 5,500 samples in a national run and in fact may get zero samples if the characteristic has dependencies that will cause it to be sampled within thousands of slices of (quota of) less than 100 samples. While such extreme cases are uncommon, most characteristics do have biases on their national-scale saturation over what one would expect based on the housing characteristic distribution due to this quirk of quota sampling. While the diversity in the samples scales with quota in both sampling methods, the rate of reaching a reasonable diversity or converging to the population mean is much faster for quota-based sampling than random sampling. The convergence rate is proportional to the square-root of the quota for quota-based and to the quota for random sampling. The ability to sample for characteristics proportional to their distributions makes quota-based sampling effective as the representativeness of the sampled stock is better maintained even at smaller sampling quotas. @@ -232,13 +233,13 @@ \section{Schedule Creation}\label{occupancy_model} \end{table} -In ResStock, schedules are used to define a variety of building system operations (Table \ref{tab:schedules}). For example, the space heating and cooling system maintains the indoor air temperature according to a detailed schedule of heating and cooling setpoint temperatures. Interior lighting turns on according to occupancy, while exterior lighting is set to turn on at a specific time frame between the evening and the early morning. These schedules represent either preset equipment schedules, typical usage patterns, or the stochastic time use behaviors of all occupants living within a household. Occupant-driven schedules are typically heterogeneous to represent a diversity of behaviors and preferences. Many of the schedules capture not only the timing of use but also the intensity of use as fractional values, with diversity for every day of the year. These fractional value timeseries are then multiplied by the annual end-use energy or hot water use (calculated separately according to building simulation standards such as ANSI/RESNET/ICC 301 standard or those developed by \cite{bahsp_2010}, \cite{bahsp_2014}) to generate the respective end-use load profiles or hot water draw profiles. The schedules are modified for vacant units and vacancy periods (i.e., an occupied household goes on vacation). When a unit is unoccupied for either reason, all schedules are set to zero except for HVAC setpoint temperature schedules designed to keep pipes from freezing. See Section \ref{vacant_units} for more information. In ResStock, schedules are generated either using a stochastic occupancy generator (inherited from OS-HPXML) or through more simplistic defined schedules. %For power outage simulations, all schedules are set to zero except occupancy and general water draws during the periods of the outage. ResStock sets the outage periods via options\_lookup. +In ResStock, schedules are used to define a variety of building system operations (Table \ref{tab:schedules}). For example, the space heating and cooling system maintains the indoor air temperature according to a detailed schedule of heating and cooling setpoint temperatures. Interior lighting turns on according to occupancy, while exterior lighting is set to turn on at a specific time frame between the evening and the early morning. These schedules represent either preset equipment schedules, typical usage patterns, or the stochastic time use behaviors of all occupants living within a household. Occupant-driven schedules are typically heterogeneous to represent a diversity of behaviors and preferences. Many of the schedules capture not only the timing of use but also the intensity of use as fractional values, with diversity for every day of the year. These fractional value timeseries are then multiplied by the annual end-use energy or hot water use (calculated separately according to building simulation standards such as ANSI/RESNET/ICC 301 standard or those developed by \cite{bahsp_2010} and \cite{Wilson2014}) to generate the respective end-use load profiles or hot water draw profiles. The schedules are modified for vacant units and vacancy periods (i.e., an occupied household goes on vacation). When a unit is unoccupied for either reason, all schedules are set to zero except for HVAC setpoint temperature schedules designed to keep pipes from freezing. See Section \ref{vacant_units} for more information. In ResStock, schedules are generated either using a stochastic occupancy generator (inherited from OS-HPXML) or through more simplistic defined schedules. %For power outage simulations, all schedules are set to zero except occupancy and general water draws during the periods of the outage. ResStock sets the outage periods via options\_lookup. \textbf{Stochastic Schedules}. ResStock uses a stochastic schedule generator to produce representative and heterogeneous schedules for occupancy and a number of appliances and hot water end uses. Developed using the \href{https://www.bls.gov/tus/}{American Time Use Survey (ATUS) data from 2013--2017}, submetered appliance energy data, and a supplemental hot water model, the generator combines Markov chain and probability-sampling for schedule simulation. At a high level, the generator uses Markov chain models built from the ATUS data to produce occupant activity schedules for seven different activities: sleeping, personal hygiene, laundry, cooking, dish washing, absent, and active-at-home. These schedules are then processed and combined with appliance information to form household-level appliance and hot water schedules. For example, both clothes washer and clothes dryer events are scheduled to occur during laundry activity, whereas sink events are scheduled to occur during active-at-home activity. More details of the stochastic occupant model can be found in \citet{Chen2022}. -One of the housing characteristics in ResStock is the number of occupants (see Section \ref{occupants} documentation). The generator starts by randomly assigning each occupant within a ResStock model to one of the four preset occupant types that roughly correspond to day-away-evening-home, day-away-evening-away, mostly-home-early-risers, mostly-home-late-risers. These preset occupant types were created by clustering the ATUS data and picking the number of clusters that provides a good balance between clustering performance and diversity of behavior. There is one Markov Chain model for each occupant type and for weekday and weekend separately. Each Markov Chain model is built from a cluster of respondents sharing a similar occupancy pattern and models their activity progression throughout the day using a time-inhomogeneous activity transition probability. This means what activity happens next depends on both the current activity and the time of day. +One of the housing characteristics in ResStock is the number of occupants (see Section \ref{occupants} documentation). The generator starts by randomly assigning each occupant within a ResStock model to one of the four preset occupant types that roughly correspond to day-away-evening-home, day-away-evening-away, mostly-home-early-risers, mostly-home-late-risers. These preset occupant types were created by clustering the ATUS data and picking the number of clusters that provides a good balance between clustering performance and diversity of behavior. There is one Markov chain model for each occupant type and for weekday and weekend separately. Each Markov chain model is built from a cluster of respondents sharing a similar occupancy pattern and models their activity progression throughout the day using a time-inhomogeneous activity transition probability. This means what activity happens next depends on both the current activity and the time of day. -Once the appropriate Markov Chain model is picked for an occupant, the schedule generation proceeds with sampling of the starting activity at midnight at the beginning of the weather year and sampling of activity at each time step based on the transition probability given the activity of the previous time step using the Markov-chain transition probability matrix. This process repeats until the full-year schedule is generated for each occupant in the household. Next, the occupant schedules are split into end uses and then merged as a household. The occupant schedules are combined for activities with shareable appliances (e.g., two or more occupants cooking at the same time is one cooking event) and aggregated for individualized activities (e.g., personal hygiene for each occupant is added together for hot water fixtures). While each occupant can only engage in one activity at a time, the activities can overlap after aggregating to the household level. +Once the appropriate Markov chain model is picked for an occupant, the schedule generation proceeds with sampling of the starting activity at midnight at the beginning of the weather year and sampling of activity at each time step based on the transition probability given the activity of the previous time step using the Markov-chain transition probability matrix. This process repeats until the full-year schedule is generated for each occupant in the household. Next, the occupant schedules are split into end uses and then merged as a household. The occupant schedules are combined for activities with shareable appliances (e.g., two or more occupants cooking at the same time is one cooking event) and aggregated for individualized activities (e.g., personal hygiene for each occupant is added together for hot water fixtures). While each occupant can only engage in one activity at a time, the activities can overlap after aggregating to the household level. Next, the generator converts the household activity schedules into appliance power and hot water schedules. For laundry machines, dishwashers, and range ovens, the generator uses the activity schedules for onset only and samples separately for the duration and power consumption of the appliance, which comes from the 2011 Residential Building Stock Assessment Metering Study (RBSAM) by NEEA. For laundry, the dryer is modeled to start immediately after the washer. For appliance hot water, the activity schedules similarly provide the draw onset while the duration and flow rate are sampled using NREL's Domestic Hot Water Event Schedule Generator~\citep{Hendron2010}. In this way, the hot water schedule and power schedule for the clothes washer and dishwasher are only aligned in terms of the onset and not necessarily the duration. This is consistent with real hot water appliance cycles in which hot water is drawn typically at the beginning. Once an appliance cycle completes with a minimum time gap, the generator finds the next activity onset from the activity schedules and the process repeats until all appliance schedules are created. @@ -248,9 +249,9 @@ \section{Schedule Creation}\label{occupancy_model} % For example, the laundry schedule from each occupant is combined to form a single laundry schedule for the household. The laundry schedule provides onset for clothes washer power draw and hot water draw. The power and water draw duration is each sampled separately from supplemental data. The clothes dryer is modeled to start immediately after the clothes washer, with its duration sampled similarly from supplemental data. Once a laundry cycle completes discretely, sampling finds the next onset in the household laundry schedule and the process repeats until the washer and dryer schedules are constructed for the full year. -\textbf{Non-Stochastic Schedules}. For non-stochastic schedules, ResStock defines various options for 24-hour setback periods (in 1-hour resolution) for HVAC heating and cooling setpoints in options\_lookup. For range spot ventilation (see Section \ref{range_spot_vent_hour}), the schedule is generated on the fly using inputs that specifies the start hour and the number of hours in operation. +\textbf{Non-Stochastic Schedules}. For non-stochastic schedules, ResStock defines various options for 24-hour setback periods (in 1-hour resolution) for HVAC heating and cooling setpoints in options\_lookup. For range spot ventilation (see Section \ref{range_spot_vent_hour}), the schedule is generated on the fly using inputs that specify the start hour and the number of hours in operation. -There are two types of OS-HPXML schedule inputs---simple schedule input or detailed schedule input. Simple schedule inputs are available as weekday/weekend fractions and monthly multipliers for a variety of building characteristics. Detailed schedule inputs allow schedule values for every hour or time step of the simulation. They can be used to reflect real-world or stochastic occupancy and must consist of a full year of data, even if the simulation is part-year. The schedule inputs do not need to have the same time resolution as the simulation. They can be more or less granular than the simulation time step. When the schedules are not specified, default OS-HPXML schedules are used. Default schedules can be simple or detailed and are typically smooth, averaged, hourly, and homogeneous schedules mostly derived from Building America House Simulation Protocols (\cite{bahsp_2010}, \cite{bahsp_2014}). +There are two types of OS-HPXML schedule input: simple schedule input or detailed schedule input. Simple schedule inputs are available as weekday/weekend fractions and monthly multipliers for a variety of building characteristics. Detailed schedule inputs allow schedule values for every hour or time step of the simulation. They can be used to reflect real-world or stochastic occupancy and must consist of a full year of data, even if the simulation is part-year. The schedule inputs do not need to have the same time resolution as the simulation. They can be more or less granular than the simulation time step. When schedules are not specified, the default OS-HPXML schedules are used. Default schedules can be simple or detailed and are typically smooth, averaged, hourly, and homogeneous schedules mostly derived from Building America House Simulation Protocols (\cite{bahsp_2010}, \cite{Wilson2014}). diff --git a/docs/technical_reference_guide/3a_ResStockInputs.tex b/docs/technical_reference_guide/3a_ResStockInputs.tex index dcb9cff48e..3fa92e2c80 100644 --- a/docs/technical_reference_guide/3a_ResStockInputs.tex +++ b/docs/technical_reference_guide/3a_ResStockInputs.tex @@ -9,9 +9,9 @@ \section{Geography} All the geography fields are compiled into a geography lookup table that contains census block-level resolution. For reference, the Geography Hierarchy Diagram for Census geographies can be found on the \href{https://www2.census.gov/geo/pdfs/reference/geodiagram.pdf}{Geography Hierarchy Diagram} U.S.~Census website. This diagram shows that the fundamental geography is a census block. All other census geographies stem from this definition. This hierarchy is used to create a lookup table for all geography characteristics in ResStock. -Added to this table are occupied and vacant unit counts for each census block from the \href{https://www.census.gov/programs-surveys/decennial-census/about/rdo.html}{2020 U.S.~Census Redistricting Data} and ACS 5-yr 2016. The ACS number of units is specified by census tract and downscaled to the census block level using the 2020 Redistricting data. The 2020 census block data are converted to 2010 census blocks using the National Historical Geographic Information System (NHGIS) \href{https://www.nhgis.org/geographic-crosswalks}{Geographic Crosswalks}. All the characteristics and distributions of housing characteristics are pivoted from this lookup table relating the geography definitions and housing unit counts. Also in this lookup file is the NHGIS GISJOIN codes that help join this file to other geography fields not in the lookup table or the ResStock outputs. +Added to this table are occupied and vacant unit counts for each census block from the \href{https://www.census.gov/programs-surveys/decennial-census/about/rdo.html}{2020 U.S.~Census Redistricting Data} and ACS 5-year 2016 data. The ACS number of units is specified by census tract and downscaled to the census block level using the 2020 Redistricting data. The 2020 census block data are converted to 2010 census blocks using the National Historical Geographic Information System (NHGIS) \href{https://www.nhgis.org/geographic-crosswalks}{Geographic Crosswalks}. All the characteristics and distributions of housing characteristics are pivoted from this lookup table relating the geography definitions and housing unit counts. Also in this lookup file is the NHGIS GISJOIN codes that help join this file to other geography fields not in the lookup table or the ResStock outputs. -The ACS housing unit data are typically used by ResStock to specify in the project file the number of housing units in the United States. The ACS data are a 5-yr average compared to the single-year 2020 Redistricting data. Consistency for using ACS for unit counts at the census geographies is also achieved except for the ``City'' characteristic. The City characteristic uses the downscaled data from the ACS to census block, because City boundaries are specified by census blocks. +The ACS housing unit data are typically used by ResStock to specify in the project file the number of housing units in the United States. The ACS data are a 5-year average compared to the single-year 2020 Redistricting data. Consistency for using ACS for unit counts at the census geographies is also achieved except for the ``City'' characteristic. The City characteristic uses the downscaled data from the ACS to census block, because City boundaries are specified by census blocks. The census geographies are set to be consistent with the U.S.~Census Bureau's definitions as of July 1, 2015. The 2010 Census geography definitions and changes between the 2010 Census and July 1, 2015, can be found on the \href{https://www.census.gov/programs-surveys/decennial-census/decade.html}{U.S.~Census Bureau Decennial Census} website. @@ -46,7 +46,7 @@ \subsubsection{Census Region} Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. \item - Unit counts are from the American Community Survey 5-yr 2016. + Unit counts are from the American Community Survey 5-year 2016. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -70,7 +70,7 @@ \subsubsection{Census Division} Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. \item - Unit counts are from the American Community Survey 5-yr 2016. + Unit counts are from the American Community Survey 5-year 2016. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -94,7 +94,7 @@ \subsubsection{State} Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. \item - Unit counts are from the American Community Survey 5-yr 2016. + Unit counts are from the American Community Survey 5-year 2016. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -134,7 +134,7 @@ \subsubsection{County} Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. \item - Unit counts are from the American Community Survey 5-yr 2016. + Unit counts are from the American Community Survey 5-year 2016. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -194,7 +194,7 @@ \subsubsection{Public Use Microdata Area} Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. \item - Unit counts are from the American Community Survey 5-yr 2016. + Unit counts are from the American Community Survey 5-year 2016. \end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -239,7 +239,7 @@ \subsubsection{County and PUMA} Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. \item - Unit counts are from the American Community Survey 5-yr 2016. + Unit counts are from the American Community Survey 5-year 2016. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -263,12 +263,12 @@ \subsubsection{Metropolitan and Micropolitan Statistical Area} Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. \item - Unit counts are from the American Community Survey 5-yr 2016. + Unit counts are from the American Community Survey 5-year 2016. \item County-MSA crosswalk comes from the Quarterly Census of Employment and Wages NAICS-based data between 2013 and 2022 by the U.S.~Bureau of Labor - Statistics. - (\url{https://www.bls.gov/cew/classifications/areas/county-msa-csa-crosswalk.htm}) + Statistics + (\url{https://www.bls.gov/cew/classifications/areas/county-msa-csa-crosswalk.htm}). \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -296,7 +296,7 @@ \subsubsection{City} Cities are defined by Census blocks by their Census Place in the 2010 Census. \item - Unit counts are from the American Community Survey 5-yr 2016. + Unit counts are from the American Community Survey 5-year 2016. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -332,9 +332,9 @@ \subsubsection{City} \item The threshold for including a Census Place in the City characteristic is 15,000 housing units. \item - The value ``In another census Place'' designates the fraction of housing units in a Census Place with fewer total housing units than the threshold. + The value ``In Another Census Place'' designates the fraction of housing units in a Census Place with fewer total housing units than the threshold. \item - The value ``Not in a census Place'' + The value ``Not in a Census Place'' designates the fraction of housing units not in a Census Place according to the 2010 Census. \end{itemize} @@ -356,7 +356,7 @@ \subsubsection{AIANNH Area} \begin{itemize} \item 2010 Census Tract to American Indian Area (AIA) Relationship File provides the percent housing units in the census tract that belong to AIA. \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. - \item Unit counts are from the American Community Survey 5-yr 2016. + \item Unit counts are from the American Community Survey 5-year 2016. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -379,8 +379,8 @@ \subsubsection{County Metro Status} \paragraph{Distribution Data Source(s)} \begin{itemize} \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. - \item Unit counts are from the American Community Survey 5-yr 2016. - \item County-MSA crosswalk comes from the Quarterly Census of Employment and Wages NAICS-based data between 2013 and 2022 by the U.S.~Bureau of Labor Statistics \href{https://www.bls.gov/cew/classifications/areas/county-msa-csa-crosswalk.htm}{U.S.~Bureau of Labor Statistics}. + \item Unit counts are from the American Community Survey 5-year 2016. + \item County-MSA crosswalk comes from the Quarterly Census of Employment and Wages NAICS-based data between 2013 and 2022 by the \href{https://www.bls.gov/cew/classifications/areas/county-msa-csa-crosswalk.htm}{U.S.~Bureau of Labor Statistics}. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -401,7 +401,7 @@ \subsubsection{PUMA Metro Status} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University of Minnesota, www.ipums.org. + \item 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -418,7 +418,7 @@ \subsubsection{PUMA Metro Status} \end{itemize} \subsection{Climate Zones} -This section of ResStock characteristics is a set of Climate Zone definitions. There are five input files to ResStock that specify climate zones: +This section of ResStock characteristics is a set of climate zone definitions. There are five input files to ResStock that specify climate zones: \begin{itemize} \item ASHRAE IECC Climate Zone 2004 \item ASHRAE IECC Climate Zone 2004---2A Split @@ -440,7 +440,7 @@ \subsubsection{ASHRAE IECC Climate Zone 2004} \label{sec:ashrae_2004_tsv} \paragraph{Distribution Data Source(s)} \begin{itemize} \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. - \item Unit counts are from the American Community Survey 5-yr 2016. + \item Unit counts are from the American Community Survey 5-year 2016. \item Climate zone data are from ASHRAE 169 2004, IECC 2012, and \href{https://www.energy.gov/sites/prod/files/2015/10/f27/ba_climate_region_guide_7.3.pdf}{M.C. Baechler 2015}. \end{itemize} @@ -473,7 +473,7 @@ \subsubsection{ASHRAE IECC Climate Zone 2004---2A Split} \paragraph{Distribution Data Source(s)} \begin{itemize} \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. - \item Unit counts are from the American Community Survey 5-yr 2016. + \item Unit counts are from the American Community Survey 5-year 2016. \item Climate zone data are from ASHRAE 169 2004, IECC 2012, and \href{https://www.energy.gov/sites/prod/files/2015/10/f27/ba_climate_region_guide_7.3.pdf}{M.C. Baechler 2015}. \end{itemize} @@ -492,7 +492,7 @@ \subsubsection{ASHRAE IECC Climate Zone 2004---2A Split} \subsubsection{Building America Climate Zones} \paragraph{Description} -The Building America Climate Zone where the sample is located. See Figure \ref{fig:building_america_cz} for a map of the climate zones. \footnote{The Subarctic climate zone is not shown and is only found in Alaska.} +The Building America Climate Zone where the sample is located. See Figure \ref{fig:building_america_cz} for a map of the climate zones.\footnote{The Subarctic climate zone is not shown and is only found in Alaska.} \begin{figure} \centering @@ -503,7 +503,7 @@ \subsubsection{Building America Climate Zones} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item Unit counts are from the American Community Survey 5-yr 2016. + \item Unit counts are from the American Community Survey 5-year 2016. \item Spatial definitions are from U.S.~Census 2010. \item Climate zone data are from ASHRAE 169 2004, IECC 2012, and \href{https://www.energy.gov/sites/prod/files/2015/10/f27/ba_climate_region_guide_7.3.pdf}{M.C. Baechler 2015}. \end{itemize} @@ -534,7 +534,7 @@ \subsubsection{California Energy Commission Climate Zones} \begin{itemize} \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. \item Zip code definitions are from the end of Q2 2020. - \item The climate zone to zip codes in California is from the CEC Website. + \item The climate zone to zip codes in California are from the CEC website. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -547,9 +547,9 @@ \subsubsection{California Energy Commission Climate Zones} \paragraph{Distribution Assumption(s)} \begin{itemize} - \item CEC Climate zones are defined by Zip Codes. + \item CEC Climate zones are defined by zip codes. \item The dependency selected is County and PUMA as zip codes are not modeled in ResStock. - \item The mapping between Census Tracts and Zip Codes is approximate and some discrepancies may exist. + \item The mapping between Census Tracts and zip codes is approximate and some discrepancies may exist. \end{itemize} \subsubsection{ENERGY STAR Climate Zone 2023} @@ -574,7 +574,7 @@ \subsubsection{ENERGY STAR Climate Zone 2023} \end{itemize} \paragraph{Options} -The options for the ENERGY STAR climate Zone 2023 characteristic are the same as climate zones: North-Central, Northern, South-Central, and Southern. +The options for the ENERGY STAR Climate Zone 2023 characteristic are the same as climate zones: North-Central, Northern, South-Central, and Southern. \paragraph{Distribution Assumption(s)} \begin{itemize} @@ -605,13 +605,15 @@ \subsubsection{ReEDS Balancing Area} \paragraph{Distribution Data Source(s)} \begin{itemize} \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. - \item Unit counts are from the American Community Survey 5-yr 2016. - \item Brown, Maxwell, Wesley Cole, Kelly Eurek, Jon Becker, David Bielen, Ilya Chernyakhovskiy, Stuart Cohen et al. 2020. Regional Energy Deployment System (ReEDS) Model Documentation: Version 2019. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-74111. https://www.nrel.gov/docs/fy20osti/74111.pdf. + \item Unit counts are from the American Community Survey 5-year 2016. + \item Regional Energy Deployment System (ReEDS) Model Documentation: Version 2019 (\cite{Brown2019}) + + \end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} - \item County + \item County. \end{itemize} \paragraph{Options} The options for the ReEDS Balancing Area characteristic is a set of integers 1-134 based on Figure \ref{fig:reeds_ba_map}. Alaska and Hawaii do not have a ReEDS balancing area and are labeled with the None option. @@ -625,18 +627,19 @@ \subsubsection{Generation and Emissions Assessment (GEA) Region} \begin{figure} \centering \includegraphics[width=1\linewidth]{images/Cambium_GEAs_2021.png} - \caption{ Map of the \href{https://www.nrel.gov/analysis/cambium.html}{Cambium} 2021 Generation and Emission Assessment Regions.} + \caption{ Map of the \href{https://www.nrel.gov/analysis/cambium.html}{Cambium} 2021 Generation and Emission Assessment Regions} \label{fig:cambium_gea_map} \end{figure} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item Pieter Gagnon, Will Frazier, Wesley Cole, and Elaine Hale. 2021. Cambium Documentation: Version 2021. Golden, CO.: National Renewable Energy Laboratory. NREL/TP-6A40-81611. https://www.nrel.gov/docs/fy22osti/81611.pdf + \item Cambium Documentation: Version 2021 (\cite{Gagnon2021}). + \end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} - \item REEDS Balancing Area + \item REEDS Balancing Area. \end{itemize} \paragraph{Options} @@ -652,7 +655,7 @@ \subsubsection{ISO RTO Region} \paragraph{Distribution Data Source(s)} \begin{itemize} \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. - \item Unit counts are from the American Community Survey 5-yr 2016. + \item Unit counts are from the American Community Survey 5-year 2016. \item ISO and RTO regions are from EIA Form 861, 2018. \end{itemize} @@ -670,7 +673,7 @@ \subsubsection{ISO RTO Region} \item California ISO (CAISO) \item New York ISO (NYISO) \item Southwest Power Pool (SPP) - \item ISO New England (NEISO) + \item ISO New England (NEISO). \end{itemize} If the county is not in any of these regions, the option is listed as the None option. @@ -684,7 +687,7 @@ \subsection{Other Geographies} \item Census Division RECS \item Custom State \item Location Region - \item American Housing Survey Region + \item American Housing Survey Region. \end{itemize} \subsubsection{Census Division RECS} @@ -694,13 +697,13 @@ \subsubsection{Census Division RECS} \paragraph{Distribution Data Source(s)} \begin{itemize} \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. - \item Unit counts are from the American Community Survey 5-yr 2016. - \item U.S.~EIA 2015 Residential Energy Consumption Survey (RECS) codebook. + \item Unit counts are from the American Community Survey 5-year 2016. + \item U.S.~EIA 2015 RECS codebook. \end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} - \item State + \item State. \end{itemize} \paragraph{Options} @@ -718,10 +721,10 @@ \subsubsection{Custom State} \paragraph{Direct Conditional Dependencies} \begin{itemize} - \item State + \item State. \end{itemize} \paragraph{Options} -The options for the Custom State characteristic are "AK" and "Others". The characteristic was added during the calibration of Alaska to integrate the Alaska Retrofit Information System data. +The options for the Custom State characteristic are ``AK'' and ``Others.'' The characteristic was added during the calibration of Alaska to integrate the Alaska Retrofit Information System data. \paragraph{Distribution Assumption(s)} No assumptions were made. @@ -741,17 +744,17 @@ \subsubsection{Location Region} \paragraph{Distribution Data Source(s)} \begin{itemize} \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. - \item Unit counts are from the American Community Survey 5-yr 2016. - \item U.S.~EIA 2009 Residential Energy Consumption Survey (RECS) microdata. + \item Unit counts are from the American Community Survey 5-year 2016. + \item U.S.~EIA 2009 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} - \item State + \item State. \end{itemize} \paragraph{Options} -A list of custom regions (CRs) that range from CR02 - CR11. These numbered CRs are the historical options of the contiguous United States. When Alaska and Hawaii were added, CRAK and CRHI options were added, respectively. +A list of custom regions (CRs) that range from CR02--CR11. These numbered CRs are the historical options of the contiguous United States. When Alaska and Hawaii were added, CRAK and CRHI options were added, respectively. \paragraph{Distribution Assumption(s)} No assumptions are made. @@ -763,14 +766,14 @@ \subsubsection{American Housing Survey Region} \paragraph{Distribution Data Source(s)} \begin{itemize} \item Spatial definitions are from the U.S.~Census Bureau as of July 1, 2015. - \item Unit counts are from the American Community Survey 5-yr 2016. + \item Unit counts are from the American Community Survey 5-year 2016. \item Core Based Statistical Area (CBSA) data based on the Feb 2013 CBSA delineation file. \item 2013 American Housing Survey microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} - \item County + \item County. \end{itemize} \paragraph{Options} @@ -789,12 +792,12 @@ \subsection{Weather Data} \subsubsection{Weather File Development} -Since ResStock is a composite model of many EnergyPlus models, it employs the standard EnergyPlus Weather (EPW) files (\cite{BigLadderSoftware2015}). The EPW weather format provides a timeseries dataset of a wide array of weather variables across all 8,760 hours of a non-leap year. These weather variables provide the climatic inputs for simulating heat transfer at each time step for each model within ResStock. For the TMY files, we use the most recent release, TMY3 from \citet{Wilcox2008}.~\footnote{In review of the TMY3 data, we have identified some outliers in the initially published TMY3 data (e.g., erroneous temperature spikes). We have corrected those for ResStock use and published our corrected versions \citep{Bianchi2021}. } For AMY, we construct our own EPW files for internal use that are not available to the public. Some of the weather variables needed to construct an EPW are available on the \href{https://data.openei.org/submissions/4520}{Load Profiles OEDI submission}. +Since ResStock is a composite model of many EnergyPlus models, it employs the standard EnergyPlus Weather (EPW) files (\cite{BigLadderSoftware2015}). The EPW weather format provides a timeseries dataset of a wide array of weather variables across all 8,760 hours of a non-leap year. These weather variables provide the climatic inputs for simulating heat transfer at each time step for each model within ResStock. For the TMY files, we use the most recent release, TMY3 from \citet{Wilcox2008}.\footnote{In review of the TMY3 data, we have identified some outliers in the initially published TMY3 data (e.g., erroneous temperature spikes). We have corrected those for ResStock use and published our corrected versions \citep{Bianchi2021}. } For AMY, we construct our own EPW files for internal use that are not available to the public. Some of the weather variables needed to construct an EPW are available on the \href{https://data.openei.org/submissions/4520}{Load Profiles OEDI submission}. We develop custom AMY weather data files by pulling historic hourly temperature, humidity, wind speed/direction, and atmospheric pressure from the \href{https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database}{Integrated Surface Database}, developed by the National Oceanic and Atmospheric Administration's National Climatic Data Center. Additionally, we add in satellite-derived solar radiation data from NREL’s National Solar Radiation Database~\citep{nsrdb}. Ground-based solar radiation data are not widely collected, so using satellite-derived solar radiation data is standard practice for both the solar industry and building energy modelers using historical weather data. Caveats and further information on the data compilation and gap filling of this custom AMY approach can be found in Section 2.4 of \citet{Wilson2022}. \subsubsection{Mapping Weather Files to ResStock Samples} -To produce weather files for ResStock, we develop AMY EPWs for approximately 1,200 weather stations pulling data from the year 2018---with the 2018 AMY roughly mapping to the locations of the TMY3 data.~\footnote{Occasionally nearby stations are used if data are missing from the target weather station.} In ResStock, each county is assigned one of these available 1,200 weather stations. Each county will receive a weather station that is located in the county if one is available; if not, the county will be assigned a weather station closest to the county's population centroid, with prioritization of stations in the same climate zone. Timestamps are shifted if the chosen weather file is in a different time zone. All housing units within a given county will use the assigned weather data for that county for simulations. Within the model, actual weather file assignment occurs in the options\_lookup.tsv as a parameter input into the ResStockArguments script. +To produce weather files for ResStock, we develop AMY EPWs for approximately 1,200 weather stations pulling data from the year 2018---with the 2018 AMY roughly mapping to the locations of the TMY3 data.\footnote{Occasionally nearby stations are used if data are missing from the target weather station.} In ResStock, each county is assigned one of these available 1,200 weather stations. Each county will receive a weather station that is located in the county if one is available; if not, the county will be assigned a weather station closest to the county's population centroid, with prioritization of stations in the same climate zone. Timestamps are shifted if the chosen weather file is in a different time zone. All housing units within a given county will use the assigned weather data for that county for simulations. Within the model, actual weather file assignment occurs in the options\_lookup.tsv as a parameter input into the ResStockArguments script. \subsubsection{Weather Files and Equipment Sizing} In addition to the 8,760 timeseries of weather variables in the timeseries energy simulation, EPW files also provide a header with basic information on the weather location. ResStock uses some of this header information for sizing HVAC equipment---see Table \ref{Tab:Packages}. @@ -892,7 +895,7 @@ \subsubsection{Orientation} \label{sec:orientation} \bottomrule\noalign{} \endlastfoot \texttt{geometry\_unit\_orientation} & true & degrees & Double & The -unit\textquotesingle s orientation is measured clockwise from north +unit's orientation is measured clockwise from north (e.g., North=0, East=90, South=180, West=270). \\ \end{longtable} @@ -905,7 +908,7 @@ \subsubsection{Geometry Stories} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item U.S.~EIA 2009 Residential Energy Consumption Survey (RECS) microdata. + \item U.S.~EIA 2009 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -977,7 +980,7 @@ \subsubsection{Geometry Story Bin} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item U.S.~EIA 2009 Residential Energy Consumption Survey (RECS) microdata. + \item U.S.~EIA 2009 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1008,7 +1011,7 @@ \subsubsection{Geometry Story Bin} \subsubsection{Geometry Building Type Height} \paragraph{Description} -The 2009 U.S.~EIA Residential Energy Consumption Survey building type with multifamily buildings split out by low-rise, mid-rise, and high-rise. +The 2009 U.S.~EIA RECS building type with multifamily buildings split out by low-rise, mid-rise, and high-rise. \paragraph{Distribution Data Source(s)} \begin{itemize} @@ -1070,7 +1073,7 @@ \subsubsection{Geometry Building Number Units MF} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item U.S.~EIA 2009 Residential Energy Consumption Survey (RECS) microdata. + \item U.S.~EIA 2009 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1118,7 +1121,7 @@ \subsubsection{Geometry Building Number Units MF} 116 & 1.2\% & 116 \\ \hline 183 & 0.62\% & 183 \\ \hline 326 & 1\% & 326 \\ \hline -None & 74\% & \\ \hline +None & 74\% & \\ \end{longtable} For the argument definitions, see Table~\ref{table:hc_arg_def_geom_build_units_mf}. See the OpenStudio-HPXML \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#whole-sfa-mf-buildings}{Whole-SFA-MF-Buildings} documentation for the available HPXML schema elements, default values, and constraints. @@ -1138,7 +1141,7 @@ \subsubsection{Geometry Building Number Units MF} \paragraph{Distribution Assumption(s)} \begin{itemize} - \item Uses NUMAPTS field in EIA RECS 2009 + \item Uses NUMAPTS (number of apartments) field in EIA RECS 2009 \item EIA RECS 2009 does not report NUMAPTS for Multifamily 2--4 units, so assumptions are made based on the number of stories \item Data were sampled from the following bins of Geometry Stories: 1, 2, 3, 4-7, 8+. \end{itemize} @@ -1150,7 +1153,7 @@ \subsubsection{Geometry Building Number Units Single-Family Attached} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item U.S.~EIA 2009 Residential Energy Consumption Survey (RECS) microdata. + \item U.S.~EIA 2009 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1494,7 +1497,7 @@ \subsubsection{Geometry Building Type ACS} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University of Minnesota, www.ipums.org. + \item 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1525,11 +1528,11 @@ \subsubsection{Geometry Building Type ACS} \subsubsection{Geometry Building Type RECS} \paragraph{Description} -The building type classification according to the U.S.~EIA Residential Energy Consumption Survey. +The building type classification according to the U.S.~EIA RECS. \paragraph{Distribution Data Source(s)} \begin{itemize} - \item 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University of Minnesota, www.ipums.org. + \item 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1601,7 +1604,7 @@ \subsubsection{Vintage} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University of Minnesota, www.ipums.org. + \item 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1663,7 +1666,7 @@ \subsubsection{Vintage ACS} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University of Minnesota, www.ipums.org. + \item 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1674,21 +1677,16 @@ \subsubsection{Vintage ACS} \paragraph{Options} The options for Vintage ACS are the same vintage bins as ACS, \ref{table:hc_vintage_acs}. They are roughly 20-year bins. No arguments are set based on this input file. -\begin{longtable}[]{ |p{4.cm}|p{4cm}|p{4cm}| } -\caption{Option and saturation for Vintage ACS} \label{table:hc_vintage_acs} \\ -\toprule\noalign{} -Option name & Stock saturation \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot -\textless1940 & 13\% \\ -1940--59 & 15\% \\ -1960--79 & 26\% \\ -1980--99 & 27\% \\ -2000--09 & 14\% \\ +\begin{customLongTable}{ |p{4.cm}|p{4cm}|p{4cm}| } +{Option and saturation for Vintage ACS} {table:hc_vintage_acs} +{Option name & Stock saturation} +\textless1940 & 13\% \\ \hline +1940--59 & 15\% \\ \hline +1960--79 & 26\% \\ \hline +1980--99 & 27\% \\ \hline +2000--09 & 14\% \\ \hline 2010s & 5.1\% \\ -\end{longtable} +\end{customLongTable} \paragraph{Distribution Assumption(s)} \begin{itemize} @@ -1786,18 +1784,18 @@ \subsubsection{Geometry Floor Area} \item Due to low sample count, the characteristic distributions are constructed by downscaling a core input file with 4 sub-input files of different dependencies. -\item Sub-input file 1 has dependencies : \textquotesingle Census +\item Sub-input file 1 has dependencies: \textquotesingle Census Division\textquotesingle, \textquotesingle PUMA Metro Status\textquotesingle, \textquotesingle Geometry Building Type RECS\textquotesingle, \textquotesingle Income RECS2020\textquotesingle{} \item - Sub-input file 2 has dependencies : \textquotesingle Census Division\textquotesingle, + Sub-input file 2 has dependencies: \textquotesingle Census Division\textquotesingle, \textquotesingle PUMA Metro Status\textquotesingle, \textquotesingle Geometry Building Type RECS\textquotesingle, \textquotesingle Tenure\textquotesingle{} \item - Sub-input file 3 has dependencies : \textquotesingle Census Division\textquotesingle, + Sub-input file 3 has dependencies: \textquotesingle Census Division\textquotesingle, \textquotesingle PUMA Metro Status\textquotesingle, \textquotesingle Geometry Building Type RECS\textquotesingle, \textquotesingle Vintage ACS\textquotesingle{} @@ -1844,7 +1842,7 @@ \subsubsection{Bedrooms} \paragraph{Distribution Data Source(s)} \begin{itemize} \item 2017 and 2019 American Housing Survey microdata. - \item Building type categorization based on U.S.~EIA 2009 Residential Energy Consumption Survey (RECS). + \item Building type categorization based on U.S.~EIA 2009 RECS. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1902,7 +1900,7 @@ \subsubsection{Geometry Attic Type} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1967,7 +1965,7 @@ \subsubsection{Geometry Foundation Type} \label{geometry_foundation_type} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item The sample counts and sample weights are constructed using U.S.~EIA 2009 Residential Energy Consumption Survey (RECS) microdata. + \item The sample counts and sample weights are constructed using U.S.~EIA 2009 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2047,7 +2045,7 @@ \subsubsection{Geometry Garage} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2125,7 +2123,7 @@ \subsubsection{Geometry Space Combination} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2232,7 +2230,7 @@ \subsubsection{Geometry Wall Exterior Finish}\label{geometry_wall_exterior_finis Stucco, Light & stucco & light & 0.2 \\\hline Stucco, Medium/Dark & stucco & medium dark & 0.2 \\\hline Vinyl, Light & vinyl siding & light & 0.6 \\\hline -Wood, Medium/Dark & wood siding & medium dark & 1.4 \\\hline +Wood, Medium/Dark & wood siding & medium dark & 1.4 \\ \end{longtable} For the argument definitions, see Table \ref{table:hc_arg_def_ext_finish}. See the OpenStudio-HPXML \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-walls}{Walls} documentation for the available HPXML schema elements, default values, and constraints. @@ -2280,17 +2278,13 @@ \subsubsection{Insulation Wall}\label{insulation_wall} \begin{itemize} \item - Ritschard et al. Single-Family Heating and Cooling Requirements: - Assumptions, Methods, and Summary Results 1992 + \textit{Single-Family Heating and Cooling Requirements: Assumptions, Methods, and Summary Results} (\cite{Ritschard1992}). \item - Nettleton, G. - \item Edwards, J. (2012). Data Collection-Data Characterization Summary, - NorthernSTAR Building America Partnership, Building Technologies - Program. Washington, D.C.: U.S.~Department of Energy, as described in - Roberts et al., \textquotesingle Assessment of the U.S.~Department of - Energy\textquotesingle s Home Energy Score Tool\textquotesingle, 2012, - and Merket Building America Field Data Repository, Webinar, 2014 + \textit{Data Collection-Data Characterization Summary} from the NorthernSTAR Building America Partnership (\cite{Nettleton2012}), as described in + Roberts et al., \textit{Assessment of the U.S.~Department of + Energy's Home Energy Score Tool} (2012), + and Merket et al., \textit{Building America Field Data Repository} webinar (2014). \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2436,7 +2430,7 @@ \subsubsection{Radiant Barrier}\label{radiant_barrier} \paragraph{Direct Conditional Dependencies} \begin{itemize} - \item Geometry Building Type RECS + \item Geometry Building Type RECS. \end{itemize} \paragraph{Option(s)} Three options for radiant barriers are available in ResStock: ``Yes,'' ``No,'' and ``None''; see Table \ref{table:hc_opt_rad_bar}. ``No'' is assigned to homes with attics but without radiant barriers, while ``None'' is assigned to homes without attics. No homes in ResStock currently have the ``Yes'' option assigned. @@ -2483,7 +2477,7 @@ \subsubsection{Roof Material}\label{roof_material} \begin{itemize} \item - U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2499,14 +2493,14 @@ \subsubsection{Roof Material}\label{roof_material} {Roof Material options and arguments that vary for each option} {table:hc_opt_roof_mat} {Option name & \texttt{roof\_material\_type} & -\texttt{roof\_color}} \\ +\texttt{roof\_color}} Asphalt Shingles, Medium & asphalt or fiberglass shingles & -medium \\ -Composition Shingles & asphalt or fiberglass shingles & medium \\ -Metal, Dark & metal surfacing & dark \\ -Slate & slate or tile shingles & medium \\ -Tile, Clay or Ceramic & slate or tile shingles & medium \\ -Tile, Concrete & slate or tile shingles & medium \\ +medium \\ \hline +Composition Shingles & asphalt or fiberglass shingles & medium \\ \hline +Metal, Dark & metal surfacing & dark \\ \hline +Slate & slate or tile shingles & medium \\ \hline +Tile, Clay or Ceramic & slate or tile shingles & medium \\ \hline +Tile, Concrete & slate or tile shingles & medium \\ \hline Wood Shingles & wood shingles or shakes & medium \\ \end{customLongTable} @@ -2534,8 +2528,8 @@ \subsubsection{Roof Material}\label{roof_material} \begin{itemize} \item - Multifamily with 5+ Units is assigned \textquotesingle Asphalt - Shingles, Medium\textquotesingle{} only. + Multifamily with 5+ Units is assigned `Asphalt + Shingles, Medium' only. \item Due to low samples, Vintage ACS is progressively grouped into: pre-1960, 1960--1999, and 2000+. @@ -2554,19 +2548,15 @@ \subsubsection{Insulation Ceiling}\label{insulation_ceiling} \begin{itemize} \item - NEEA Residential Building Stock Assessment, 2012 -\item - Nettleton, G. + NEEA Residential Building Stock Assessment, 2012. \item - Edwards, J. (2012). Data Collection-Data Characterization Summary, - NorthernSTAR Building America Partnership, Building Technologies - Program. Washington, D.C.: U.S.~Department of Energy, as described in - Roberts et al., \textquotesingle Assessment of the U.S.~Department of - Energy\textquotesingle s Home Energy Score Tool\textquotesingle, 2012, - and Merket \textquotesingle Building America Field Data - Repository\textquotesingle, Webinar, 2014 + \textit{Data Collection-Data Characterization Summary} from the NorthernSTAR Building America Partnership (\cite{Nettleton2012}), as described in + Roberts et al., \textit{Assessment of the U.S.~Department of + Energy's Home Energy Score Tool} (2012) + and Merket et al., \textit{Building America Field Data + Repository} webinar, 2014. \item - Derived from Home Innovation Research Labs 1982-2007 Data + Derived from Home Innovation Research Labs 1982-2007 Data. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2645,7 +2635,7 @@ \subsubsection{Insulation Floor}\label{insulation_floor} \item Derived from Home Innovation Research Labs 1982--2007 Data \item - (pre-1980) Engineering judgment. + Pre-1980 uses engineering judgment. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2709,7 +2699,7 @@ \subsubsection{Insulation Slab}\label{insulation_slab} \item Derived from Home Innovation Research Labs 1982--2007 Data \item - (pre-1980) Engineering judgment. + Pre-1980 uses engineering judgment. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2728,13 +2718,13 @@ \subsubsection{Insulation Slab}\label{insulation_slab} {Option name & \texttt{slab\_perimeter\_insulation\_r} & \texttt{slab\_perimeter\_depth} & \texttt{slab\_under\_insulation\_r} & \texttt{slab\_under\_width}} -None & 0 & 0 & 0 & 0 \\ -Uninsulated & 0 & 0 & 0 & 0 \\ -2ft R5 Under, Horizontal & 0 & 0 & 5 & 2 \\ -2ft R10 Under, Horizontal & 0 & 0 & 10 & 2 \\ -4ft R5 Under, Horizontal & 0 & 0 & 5 & 4 \\ -2ft R5 Perimeter, Vertical & 5 & 2 & 0 & 0 \\ -2ft R10 Perimeter, Vertical & 10 & 2 & 0 & 0 \\ +None & 0 & 0 & 0 & 0 \\ \hline +Uninsulated & 0 & 0 & 0 & 0 \\ \hline +2ft R5 Under, Horizontal & 0 & 0 & 5 & 2 \\ \hline +2ft R10 Under, Horizontal & 0 & 0 & 10 & 2 \\ \hline +4ft R5 Under, Horizontal & 0 & 0 & 5 & 4 \\ \hline +2ft R5 Perimeter, Vertical & 5 & 2 & 0 & 0 \\ \hline +2ft R10 Perimeter, Vertical & 10 & 2 & 0 & 0 \\ \hline R10 Whole Slab, Horizontal & 0 & 0 & 10 & 999 \\ \end{customLongTable} % Template starting-place header swap: % @@ -2745,22 +2735,22 @@ \subsubsection{Insulation Slab}\label{insulation_slab} {Name & Required & Units & Type & Choices & Description} \texttt{slab\_perimeter\_insulation\_r} & true & h-ft\textsuperscript{2}-R/Btu & Double & & Nominal R-value of the vertical slab perimeter insulation. -Applies to slab-on-grade foundations and basement/crawlspace floors. \\ +Applies to slab-on-grade foundations and basement/crawlspace floors. \\ \hline \texttt{slab\_perimeter\_depth} & true & ft & Double & & Depth from grade to bottom of vertical slab perimeter insulation. Applies to -slab-on-grade foundations and basement/crawlspace floors. \\ +slab-on-grade foundations and basement/crawlspace floors. \\ \hline \texttt{slab\_under\_insulation\_r} & true & h-ft\textsuperscript{2}-R/Btu & Double & & Nominal R-value of the horizontal under slab insulation. Applies to -slab-on-grade foundations and basement/crawlspace floors. \\ +slab-on-grade foundations and basement/crawlspace floors. \\ \hline \texttt{slab\_under\_width} & true & ft & Double & & Width from slab edge inward of horizontal under-slab insulation. Enter 999 to specify that the under slab insulation spans the entire slab. Applies to -slab-on-grade foundations and basement/crawlspace floors. \\ +slab-on-grade foundations and basement/crawlspace floors. \\ \hline \texttt{slab\_thickness} & false & in & Double & auto & The thickness of the slab. Zero can be entered if there is a dirt floor instead of a -slab. \\ +slab. \\ \hline \texttt{slab\_carpet\_fraction} & false & Frac & Double & auto & -Fraction of the slab floor area that is carpeted. \\ +Fraction of the slab floor area that is carpeted. \\ \hline \texttt{slab\_carpet\_r} & false & h-ft\textsuperscript{2}-R/Btu & Double & auto & R-value of the slab carpet. \\ \end{customLongTable} @@ -2786,7 +2776,7 @@ \subsubsection{Insulation Foundation Wall}\label{insulation_foundation_wall} \item Derived from Home Innovation Research Labs 1982--2007 Data \item - (pre-1980) Engineering judgment. + Pre-1980 uses engineering judgment. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2958,7 +2948,7 @@ \subsubsection{Ground Thermal \begin{itemize} \item - Data from the Southern Methodist University Geothermal Laboratory. The data are from the Thermal Conductivity Observation in Content Model Format dataset. The data are available at \url{http://geothermal.smu.edu/static/DownloadFilesButtonPage.htm}. + Data from the Southern Methodist University Geothermal Laboratory. The data are from the Thermal Conductivity Observation in Content Model Format dataset. The data are available at \url{https://www.smu.edu/dedman/academics/departments/earth-sciences/research/geothermallab/datamaps/ngds-project}. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3008,10 +2998,10 @@ \subsubsection{Ground Thermal wet, loam, dry, loam, mixed, loam, wet, sand, dry, sand, mixed, sand, wet, silt, dry, silt, mixed, silt, wet, unknown, dry, unknown, mixed, unknown, wet & Type of soil and moisture. This is used to inform ground -conductivity and diffusivity. \\ +conductivity and diffusivity. \\ \hline \texttt{site\_ground\_conductivity} & false & Btu/hr-ft-F & Double & & Conductivity of the ground soil. If provided, overrides the previous -soil and moisture type input. \\ +soil and moisture type input. \\ \hline \texttt{site\_ground\_diffusivity} & false & ft\textsuperscript{2}/hr & Double & & Diffusivity of the ground soil. If provided, overrides the previous soil and moisture type input. \\ @@ -3158,7 +3148,7 @@ \subsubsection{Windows}\label{windows} \begin{itemize} \item - U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3185,19 +3175,19 @@ \subsubsection{Windows}\label{windows} {Windows options and arguments that vary for each option} {table:hc_opt_window} {Option name & \texttt{window\_ufactor} & \texttt{window\_shgc}} -Single, Clear, Metal & 1.16 & 0.76 \\ +Single, Clear, Metal & 1.16 & 0.76 \\ \hline Single, Clear, Metal, Exterior Clear Storm & 0.67 & 0.56 - \\ -Single, Clear, Non-metal & 0.84 & 0.63 \\ + \\ \hline +Single, Clear, Non-metal & 0.84 & 0.63 \\ \hline Single, Clear, Non-metal, Exterior Clear Storm & 0.47 & -0.54 \\ -Double, Clear, Metal, Air & 0.76 & 0.67 \\ +0.54 \\ \hline +Double, Clear, Metal, Air & 0.76 & 0.67 \\ \hline Double, Clear, Metal, Air, Exterior Clear Storm & 0.55 & -0.51\\ -Double, Clear, Non-metal, Air & 0.49 & 0.56 \\ +0.51\\ \hline +Double, Clear, Non-metal, Air & 0.49 & 0.56 \\ \hline Double, Clear, Non-metal, Air, Exterior Clear Storm & -0.34 & 0.49 \\ -Double, Low-E, Non-metal, Air, M-Gain & 0.38 & 0.44 \\ +0.34 & 0.49 \\ \hline +Double, Low-E, Non-metal, Air, M-Gain & 0.38 & 0.44 \\ \hline Triple, Low-E, Non-metal, Air, L-Gain & 0.29 & 0.26 \\ \end{customLongTable} @@ -3216,7 +3206,7 @@ \subsubsection{Windows}\label{windows} \hline \texttt{window\_exterior\_shading\_winter} & false & Frac & Double & auto & Exterior shading coefficient for the winter season. 1.0 indicates -no reduction in solar gain, 0.85 indicates 15\% reduction, etc \\ +no reduction in solar gain, 0.85 indicates 15\% reduction, etc. \\ \hline \texttt{window\_exterior\_shading\_summer} & false & Frac & Double & auto & Exterior shading coefficient for the summer season. 1.0 indicates @@ -3257,7 +3247,7 @@ \subsubsection{Windows}\label{windows} \item Vintage data are grouped into: (1) \textless1960, (2) 1960--79, - (3) 1980--99, 4) 2000s, 5) 2010s. + (3) 1980--99, (4) 2000s, (5) 2010s. \item Building Type data are grouped into: (1) Single-Family Detached, Single-Family Attached, and Mobile homes, and (2) @@ -3285,11 +3275,9 @@ \subsubsection{Windows}\label{windows} bins are combined. \end{itemize} \item - Storm window saturations are based on D\&R International, Ltd. - \textquotesingle Residential Windows and Window Coverings: A Detailed - View of the Installed Base and User Behavior\textquotesingle{} 2013. - \url{https://www.energy.gov/sites/prod/files/2013/11/f5/residential_windows_coverings.pdf}. - Cut the \% storm windows by factor of 55\% because only 55\% of storms + Storm window saturations are based on D\&R International, Ltd. \href{https://www.energy.gov/sites/prod/files/2013/11/f5/residential_windows_coverings.pdf}{2013}. + \textit{Residential Windows and Window Coverings: A Detailed + View of the Installed Base and User Behavior}. Cut the \% storm windows by factor of 55\% because only 55\% of storms are installed year-round. \item Due to lack of performance data, Triple-Pane windows with storms are @@ -3413,7 +3401,7 @@ \subsubsection{Window Areas}\label{window_areas} \begin{itemize} \item - The window-to-wall ratios (WWR) are exponential weibull distributed. + The window-to-wall ratios (WWR) are exponential weibull distributed \item Multifamily with 2--4 Units distributions are independent of Geometry Stories @@ -3431,7 +3419,9 @@ \subsubsection{Overhangs}\label{overhangs} \paragraph{Description} Presence, depth, and location of window overhangs (not currently used in ResStock baseline). \paragraph{Distribution Data Source(s)} -Not applicable. +\begin{itemize} +\item Not applicable. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -3515,8 +3505,7 @@ \subsubsection{Eaves}\label{eaves} \begin{itemize} \item - Wilson et al. \textquotesingle Building America House Simulation - Protocols\textquotesingle{} 2014 + Building America House Simulation Protocols (\cite{Wilson2014}). \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3603,8 +3592,7 @@ \subsubsection{Infiltration}\label{infiltration} \item Distributions are based on the cumulative distribution functions from - the Residential Diagnostics Database (ResDB) - \url{http://resdb.lbl.gov/}. + the \href{http://resdb.lbl.gov/}{Residential Diagnostics Database (ResDB)}. \item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance @@ -3699,7 +3687,7 @@ \subsubsection{Infiltration}\label{infiltration} \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the - American Community Survey are used to distribute the between these two + American Community Survey are used to distribute proportionally between these two building types. \item For Alaska, Infiltration ACH50 values are calculated based on CFM50 diff --git a/docs/technical_reference_guide/3b_ResStockInputs_HVAC.tex b/docs/technical_reference_guide/3b_ResStockInputs_HVAC.tex index 5939dd3b8d..319aa6c0f3 100644 --- a/docs/technical_reference_guide/3b_ResStockInputs_HVAC.tex +++ b/docs/technical_reference_guide/3b_ResStockInputs_HVAC.tex @@ -28,7 +28,7 @@ \subsubsection{Heating Fuel} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item 2019 5-yr Public Use Microdata Samples (PUMS). + \item 2019 5-year PUMS \item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. \end{itemize} @@ -78,7 +78,7 @@ \subsubsection{Heating Fuel} \begin{itemize} \item In ACS, Heating Fuel is reported for occupied units only. By excluding Vacancy Status as a dependency, we assume vacant units share the same Heating Fuel distribution as occupied units. Where sample counts are less than 10, the State average distribution has been inserted. Prior to insertion, the following adjustments have been made to the state distribution so all rows have sample count > 10: 1. Where sample counts < 10 (which consists of Mobile Home and Single-Family Attached only), the Vintage ACS distribution is used instead of Vintage: [CT, DE, ID, MD, ME, MT, ND, NE, NH, NV, RI, SD, UT, VT, WY]. \item Remaining Mobile Homes < 10 are replaced by Single-Family Detached + Mobile Homes combined: [DE, RI, SD, VT, WY, and all DC]. - \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute the between these two building types. + \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute between these two building types. \item For Alaska, all wood is modeled as cord wood. \item For Alaska, when heating uses more than one fuel, the fuel with highest consumption is considered the primary (heating) fuel, and fuel with second highest usage (provided it is at least 10\% of total energy use across all fuels) is considered secondary (heating) fuel---except in case of electric heating, which is always assumed as primary. The rest of the fuels are ignored. \end{itemize} @@ -89,7 +89,7 @@ \subsubsection{HVAC Heating Type} The presence and type of the primary heating system in the housing unit. \paragraph{Distribution Data Source(s)} \begin{itemize} - \item U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item U.S.~EIA 2020 RECS microdata \item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. \end{itemize} @@ -107,7 +107,7 @@ \subsubsection{HVAC Heating Type} \paragraph{Distribution Assumption(s)} \begin{itemize} \item Due to low sample sizes, fallback rules lumped together the following: (1) Heating fuel lump: Fuel oil, Propane, Wood, and Other Fuel, (2) Geometry building SF: Mobile, Single-family attached, Single-family detached, (3) Geometry building MF: Multifamily with 2--4 Units, Multifamily with 5+ Units, and (4) Vintage Lump: 20-yr bins. - \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute the between these two building types. + \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute between these two building types. \end{itemize} %copy-paste from read the docs or tsv or appendix @@ -139,7 +139,7 @@ \subsubsection{HVAC Heating Efficiency} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item The sample counts and sample weights are constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item The sample counts and sample weights are constructed using U.S.~EIA 2020 RECS microdata. \item Shipment data based on ENERGY STAR ASHP shipments data and ENERGY STAR furnace shipments data. Efficiency data from Home Energy Saver are combined with age of equipment data from RECS. \item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. \end{itemize} @@ -337,7 +337,7 @@ \subsubsection{HVAC Heating Efficiency} \hline \texttt{heat\_pump\_is\_ducted} & false & & Boolean & auto, true, false & Whether the heat pump is ducted or not. Only used for mini-split. -It iss assumed that air-to-air and ground-to-air are +It is assumed that air-to-air and ground-to-air are ducted, and packaged terminal heat pump and room air conditioner with reverse cycle are not ducted. If not provided, assumes not ducted. \\ \hline @@ -412,16 +412,16 @@ \subsubsection{HVAC Heating Efficiency} \endlastfoot ASHP, SEER 10, 6.2 HSPF & air-to-air & 6.2 & 10 & auto & -auto & \\ +auto & \\ \hline ASHP, SEER 13, 7.7 HSPF & air-to-air & 7.7 & 13 & auto & -auto & \\ +auto & \\\hline ASHP, SEER 15, 8.5 HSPF & air-to-air & 8.5 & 15 & auto & -auto & \\ +auto & \\\hline MSHP, SEER 14.5, 8.2 HSPF & mini-split & 8.2 & 14.5 & 0.25 -& -5 & false \\ +& -5 & false \\\hline MSHP, SEER 29.3, 14 HSPF & mini-split & 14 & 29.3 & 0.5 & -15 & false \\ @@ -435,18 +435,18 @@ \subsubsection{HVAC Heating Efficiency} \texttt{heating\_system\_type} & \texttt{heating\_system\_heating\_efficiency} & \texttt{heating\_system\_pilot\_light}} -Electric Baseboard, 100\% Efficiency & ElectricResistance & 1 & \\ -Electric Boiler, 100\% AFUE & Boiler & 1 & \\ -Electric Furnace, 100\% AFUE & Furnace & 1 & \\ -Electric Wall Furnace, 100\% AFUE & WallFurnace & 1 & \\ -Fuel Boiler, 76\% AFUE & Boiler & 0.76 & auto \\ -Fuel Boiler, 80\% AFUE & Boiler & 0.8 & auto \\ -Fuel Boiler, 90\% AFUE & Boiler & 0.9 & auto \\ -Fuel Furnace, 60\% AFUE & Furnace & 0.6 & auto \\ -Fuel Furnace, 76\% AFUE & Furnace & 0.76 & auto \\ -Fuel Furnace, 80\% AFUE & Furnace & 0.8 & auto \\ -Fuel Wall/Floor Furnace, 60\% AFUE & WallFurnace & 0.6 & auto \\ -Fuel Wall/Floor Furnace, 68\% AFUE & WallFurnace & 0.68 & auto \\ +Electric Baseboard, 100\% Efficiency & ElectricResistance & 1 & \\ \hline +Electric Boiler, 100\% AFUE & Boiler & 1 & \\ \hline +Electric Furnace, 100\% AFUE & Furnace & 1 & \\ \hline +Electric Wall Furnace, 100\% AFUE & WallFurnace & 1 & \\ \hline +Fuel Boiler, 76\% AFUE & Boiler & 0.76 & auto \\ \hline +Fuel Boiler, 80\% AFUE & Boiler & 0.8 & auto \\ \hline +Fuel Boiler, 90\% AFUE & Boiler & 0.9 & auto \\ \hline +Fuel Furnace, 60\% AFUE & Furnace & 0.6 & auto \\ \hline +Fuel Furnace, 76\% AFUE & Furnace & 0.76 & auto \\ \hline +Fuel Furnace, 80\% AFUE & Furnace & 0.8 & auto \\ \hline +Fuel Wall/Floor Furnace, 60\% AFUE & WallFurnace & 0.6 & auto \\ \hline +Fuel Wall/Floor Furnace, 68\% AFUE & WallFurnace & 0.68 & auto \\ \hline None & none & 0 & \\ \end{customLongTable} @@ -458,7 +458,7 @@ \subsubsection{HVAC Heating Efficiency} \item Due to low sample size for some categories, the HVAC Has Shared System categories ‘Cooling Only’ and ‘None’ are combined for the purpose of querying Heating Efficiency distributions. \item For ‘other’ heating system types, we assign them to Electric Baseboard if fuel is Electric, and assign them to Wall/Floor Furnace if fuel is natural gas, fuel oil, or propane. \item For Other Fuel and Wood, the lowest efficiency systems are assumed. - \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute the between these two building types. + \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute between these two building types. \item For Alaska, electric space heaters are modeled as electric baseboards. \item For Alaska, Toyo/monitor direct-vent devices and other fuel space heaters are not modeled. \item For Alaska, fireplace and stoves are not modeled. @@ -545,7 +545,9 @@ \subsubsection{HVAC Secondary Heating Type} \paragraph{Description} The efficiency and type of the secondary heating system. \paragraph{Distribution Data Source(s)} -Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by the Alaska Housing Finance Corporation. Not implemented in baseline for other states. +\begin{itemize} +\item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by the Alaska Housing Finance Corporation. Not implemented in baseline for other states. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item Custom State @@ -558,7 +560,7 @@ \subsubsection{HVAC Secondary Heating Type} \paragraph{Distribution Assumption(s)} \begin{itemize} - \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute the between these two building types. + \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute between these two building types. \item For Alaska, all heat pumps are assumed to be non-ducted mini-splits. \end{itemize} @@ -567,7 +569,9 @@ \subsubsection{HVAC Secondary Heating Fuel} \paragraph{Description} Secondary Heating Fuel for the housing unit. \paragraph{Distribution Data Source(s)} -Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. Secondary heating is not currently implemented in baseline for other states. +\begin{itemize} +\item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. Secondary heating is not currently implemented in baseline for other states. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item County @@ -613,7 +617,7 @@ \subsubsection{HVAC Secondary Heating Fuel} \paragraph{Distribution Assumption(s)} \begin{itemize} - \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute the between these two building types. + \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute between these two building types. \item For Alaska, all wood is modeled as cord wood. \item For Alaska, when heating uses more than one fuel, the fuel with highest consumption is considered the primary (heating) fuel, and fuel with second highest usage (provided it is at least 10\% of total energy use across all fuels) is considered secondary (heating) fuel---except in case of electric heating, which is always assumed as primary. The rest of the fuels are ignored. \end{itemize} @@ -624,8 +628,9 @@ \subsubsection{HVAC Secondary Heating Efficiency} The efficiency of the secondary heating system. \paragraph{Distribution Data Source(s)} -Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. Secondary heating is not currently implemented in baseline for other states. - +\begin{itemize} +\item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. Secondary heating is not currently implemented in baseline for other states. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item Custom State @@ -680,7 +685,7 @@ \subsubsection{HVAC Secondary Heating Efficiency} \paragraph{Distribution Assumption(s)} \begin{itemize} - \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute the between these two building types. + \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute between these two building types. \item For Alaska, electric space heaters are modeled as electric baseboards. \item For Alaska, Toyo/monitor direct-vent devices and other fuel space heaters are not modeled. \item For Alaska, fireplace and stoves are not modeled. @@ -692,7 +697,9 @@ \subsubsection{HVAC Secondary Heating Partial Space Conditioning} The fraction of heating load served by secondary heating system. The remainder is served by the primary heating system. \paragraph{Distribution Data Source(s)} -Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. Secondary heating partial space conditioning is not currently implemented in baseline for other states. +\begin{itemize} +\item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. Secondary heating partial space conditioning is not currently implemented in baseline for other states. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -739,7 +746,7 @@ \subsubsection{HVAC Secondary Heating Partial Space Conditioning} \paragraph{Distribution Assumption(s)} \begin{itemize} - \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute the between these two building types. + \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute between these two building types. \item For Alaska, the fraction of the load served by the secondary heating system is calculated as the ratio of annual energy used by secondary fuel and annual energy used by secondary and primary fuel. \end{itemize} @@ -762,7 +769,10 @@ \subsubsection{HVAC Cooling Type} The presence and type of primary cooling system in the housing unit. \paragraph{Distribution Data Source(s)} -U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. +\begin{itemize} +\item U.S.~EIA 2020 RECS microdata. +\item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance Corporation. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -777,11 +787,10 @@ \subsubsection{HVAC Cooling Type} \paragraph{Distribution Assumption(s)} \begin{itemize} - \item Due to low sample sizes, fallback rules were applied, with coarsening of - \item (1) HVAC Heating type: Non-ducted heating and Non, (2) Geometry building SF: Mobile, Single-family attached, Single-family detached, (3) Geometry building MF: Multifamily with 2--4 Units, Multifamily with 5+ Units, (4) Vintage Lump: 20-yr bins. + \item Due to low sample sizes, fallback rules were applied, with coarsening of (1) HVAC Heating type: Non-ducted heating and None, (2) Geometry building SF: Mobile, Single-family attached, Single-family detached, (3) Geometry building MF: Multifamily with 2--4 Units, Multifamily with 5+ Units, (4) Vintage Lump: 20-yr bins. \item Homes having ducted heat pump for heating and electricity fuel are assumed to have ducted heat pump for cooling (separating from central AC category). \item Homes having non-ducted heat pump for heating are assumed to have non-ducted heat pump for cooling. - \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute the between these two building types. + \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the American Community Survey are used to distribute between these two building types. \item For Alaska, we are not modeling any central and room AC. \item For Alaska, cooling systems are never shared. \end{itemize} @@ -792,7 +801,7 @@ \subsubsection{HVAC Cooling Efficiency} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item The sample counts and sample weights are constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item The sample counts and sample weights are constructed using U.S.~EIA 2020 RECS microdata \item Efficiency data based on ENERGY STAR shipment and Home Energy Saver data combined with age of equipment data from RECS 2020. \end{itemize} @@ -911,7 +920,9 @@ \subsubsection{HVAC Cooling Partial Space Conditioning} The fraction of cooling load served by the cooling system. This is approximately equal to the fraction of finished floor area served by the cooling system. Cooling load must be met at every time step for the portion of floor area covered, and does not represent intermittent cooling overtime. \paragraph{Distribution Data Source(s)} -Constructed using U.S.~EIA 2009 Residential Energy Consumption Survey (RECS) microdata. +\begin{itemize} +\item Constructed using U.S.~EIA 2009 RECS microdata. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -971,8 +982,9 @@ \subsubsection{HVAC Cooling Autosizing Factor} The cooling capacity and airflow scaling factor applied to the auto-sizing methodology. Not currently used in baseline. \paragraph{Distribution Data Source(s)} -N/A. - +\begin{itemize} +\item N/A. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item HVAC Cooling Efficiency @@ -1019,7 +1031,9 @@ \subsubsection{HVAC Has Shared System} The presence of an HVAC system shared by multiple housing units. \paragraph{Distribution Data Source(s)} -The sample counts and sample weights are constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +\begin{itemize} +\item The sample counts and sample weights are constructed using U.S.~EIA 2020 RECS microdata. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1058,7 +1072,7 @@ \subsubsection{HVAC Shared Efficiencies} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item The sample counts and sample weights are constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item The sample counts and sample weights are constructed using U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1412,7 +1426,9 @@ \subsubsection{Heating Setpoint} Base heating setpoint (prior to any offset applied). \paragraph{Distribution Data Source(s)} -Constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +\begin{itemize} +\item Constructed using U.S.~EIA 2020 RECS microdata. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1484,7 +1500,7 @@ \subsubsection{Heating Setpoint Has Offset} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item Constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item Constructed using U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1508,7 +1524,7 @@ \subsubsection{Heating Setpoint Offset Magnitude} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item Constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item Constructed using U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1565,7 +1581,9 @@ \subsubsection{Heating Setpoint Offset Period} The time period(s) for the housing unit's heating setpoint offset. \paragraph{Distribution Data Source(s)} -Constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +\begin{itemize} +\item Constructed using U.S.~EIA 2020 RECS microdata. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1585,103 +1603,103 @@ \subsubsection{Heating Setpoint Offset Period} \texttt{hvac\_control\_heating\_weekend\_setpoint\_schedule}} Day & 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day -1h & 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day -2h & 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day -3h & 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day -4h & 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day -5h & 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day +1h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day +2h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day +3h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day +4h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day +5h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day and Night & -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, -1, -1 & --1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 \\ +-1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 \\ \hline Day and Night -1h & -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1 & --1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 \\ +-1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 \\ \hline Day and Night -2h & -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1, -1 & --1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 \\ +-1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 \\ \hline Day and Night -3h & -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 & --1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 \\ +-1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 \\ \hline Day and Night -4h & -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 & --1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 \\ +-1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 \\ \hline Day and Night -5h & -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 & --1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 \\ +-1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 \\ \hline Day and Night +1h & -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, -1 & --1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 \\ +-1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 \\ \hline Day and Night +2h & -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0 & --1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +-1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day and Night +3h & 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0 & -0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day and Night +4h & 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0 & -0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day and Night +5h & 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0 & -0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night & -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 & -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 \\ \hline Night -1h & -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 & -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 \\ \hline Night -2h & -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 & -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 \\ +0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 \\ \hline Night -3h & -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 & -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 \\ +0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 \\ \hline Night -4h & -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 & -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 \\ +0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 \\ \hline Night -5h & -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 & -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, -1, -1, -1, -1, -1, -1, -1 \\ +0, 0, 0, -1, -1, -1, -1, -1, -1, -1 \\ \hline Night +1h & -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 & -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 \\ \hline Night +2h & -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night +3h & 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night +4h & 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night +5h & 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, --1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline None & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ @@ -1715,7 +1733,7 @@ \subsubsection{Cooling Setpoint} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item Constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item Constructed using U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1783,7 +1801,7 @@ \subsubsection{Cooling Setpoint Has Offset} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item Constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item Constructed using U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1806,7 +1824,7 @@ \subsubsection{Cooling Setpoint Offset Magnitude} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item Constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item Constructed using U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1864,7 +1882,7 @@ \subsubsection{Cooling Setpoint Offset Period} \paragraph{Distribution Data Source(s)} \begin{itemize} - \item Constructed using U.S.~EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + \item Constructed using U.S.~EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -1884,169 +1902,169 @@ \subsubsection{Cooling Setpoint Offset Period} \texttt{hvac\_control\_cooling\_weekend\_setpoint\_schedule}} Day and Night Setup & 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,1, 1, 0, 0, 0, 0, 0, 1, 1 & -1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1 \\ +1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1 \\ \hline Day and Night Setup -1h & 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1 & -1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 \\ +1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 \\ \hline Day and Night Setup -2h & 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1 & -1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1 \\ +1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1 \\ \hline Day and Night Setup -3h & 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 & -1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 \\ +1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 \\ \hline Day and Night Setup -4h & 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1 & -1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1 \\ +1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1 \\ \hline Day and Night Setup -5h & 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1 & -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1 \\ +1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1 \\ \hline Day and Night Setup +1h & 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1 & -1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 \\ +1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 \\ \hline Day and Night Setup +2h & 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 & -1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day and Night Setup +3h & 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0 & -0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day and Night Setup +4h & 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0 & -0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day and Night Setup +5h & 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0 & -0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup & 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup -1h & 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup -2h & 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup -3h & 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup -4h & 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup -5h & 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup +1h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup +2h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup +3h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup +4h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup +5h & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup and Night Setback & -1, -1, -1, -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, -1, -1 & --1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 \\ +-1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 \\ \hline Day Setup and Night Setback -1h & -1, -1, -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, -1, -1, -1 & --1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 \\ +-1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 \\ \hline Day Setup and Night Setback -2h & -1, -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, -1, -1, -1, -1 & --1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 \\ +-1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 \\ \hline Day Setup and Night Setback -3h & -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 & --1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 \\ +-1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 \\ \hline Day Setup and Night Setback -4h & -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 & --1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 \\ +-1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 \\ \hline Day Setup and Night Setback -5h & -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 & --1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 \\ +-1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 \\ \hline Day Setup and Night Setback +1h & -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, -1 & --1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 \\ +-1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 \\ \hline Day Setup and Night Setback +2h & -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 & --1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +-1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup and Night Setback +3h & 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0 & -0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup and Night Setback +4h & 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0 & -0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Day Setup and Night Setback +5h & 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0 & -0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night Setback & -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 & -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1 \\ \hline Night Setback -1h & -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 & -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1 \\ \hline Night Setback -2h & -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 & -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1 \\ \hline Night Setback -3h & -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 & -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1 \\ \hline Night Setback -4h & -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 & -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 \\ +0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1 \\ \hline Night Setback -5h & -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 & -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 \\ +0, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1, -1, -1 \\ \hline Night Setback +1h & -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 & -1, -1, -1, -1, -1, -1, -1, -1, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1 \\ \hline Night Setback +2h & -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & -1, -1, -1, -1, -1, -1, -1, -1, -1, -0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night Setback +3h & 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, -1, -1, -1, -1, -1, -1, -1, -1, --1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night Setback +4h & 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, -1, -1, -1, -1, -1, -1, -1, --1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +-1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night Setback +5h & 0, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, -1, -1, -1, -1, -1, -1, --1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +-1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night Setup & 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1 & 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 1, 1 \\ +0, 0, 0, 0, 0, 1, 1 \\ \hline Night Setup -1h & 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 & 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 1, 1, 1 \\ +0, 0, 0, 0, 0, 0, 1, 1, 1 \\ \hline Night Setup -2h & 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1 & 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 1, 1, 1, 1 \\ +0, 0, 0, 0, 0, 1, 1, 1, 1 \\ \hline Night Setup -3h & 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 & 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 1, 1, 1, 1, 1 \\ +0, 0, 0, 0, 1, 1, 1, 1, 1 \\ \hline Night Setup -4h & 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1 & 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 1, 1, 1, 1, 1, 1 \\ +0, 0, 0, 1, 1, 1, 1, 1, 1 \\ \hline Night Setup -5h & 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1 & 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0, 0, 1, 1, 1, 1, 1, 1, 1 \\ +0, 0, 1, 1, 1, 1, 1, 1, 1 \\ \hline Night Setup +1h & 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 & 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 1 \\ +0, 0, 0, 0, 0, 0, 0, 0, 1 \\ \hline Night Setup +2h & 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night Setup +3h & 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night Setup +4h & 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline Night Setup +5h & 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, -0, 0, 0, 0, 0, 0, 0, 0, 0 \\ +0, 0, 0, 0, 0, 0, 0, 0, 0 \\ \hline None & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 & 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 \\ @@ -2088,9 +2106,15 @@ \subsubsection{Modeling Approach} \subsubsection{HVAC Has Ducts} \paragraph{Description} + The presence of ducts in the housing unit. + + \paragraph{Distribution Data Source(s)} -The sample counts and sample weights are constructed using RECS 2020 microdata. +\begin{itemize} +\item The sample counts and sample weights are constructed using RECS 2020 microdata. +\end{itemize} + \paragraph{Direct Conditional Dependencies} \begin{itemize} \item HVAC Cooling Type @@ -2131,8 +2155,11 @@ \subsubsection{Duct Leakage and Insulation} \paragraph{Description} Duct insulation and leakage to outside for the portion of ducts in unconditioned spaces. \paragraph{Distribution Data Source(s)} -Duct insulation as a function of location: IECC 2009; leakage -distribution: Lucas and Cole, "Impacts of the 2009 IECC for Residential Buildings at State Level", 2009. +\begin{itemize} +\item Duct insulation as a function of location: IECC 2009 +\item Leakage +distribution: Lucas and Cole (\href{https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-18545.pdf}{2009}). \textit{Impacts of the 2009 IECC for Residential Buildings at State Level}. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item Duct location @@ -2153,27 +2180,27 @@ \subsubsection{Duct Leakage and Insulation} \texttt{ducts\_supply\_leakage\_to\_outside\_value} & \texttt{ducts\_supply\_insulation\_r} & \texttt{ducts\_return\_leakage\_to\_outside\_value} & \texttt{ducts\_return\_insulation\_r}} -0\% Leakage to Outside, Uninsulated & 0 & 0 & 0 & 0 \\ +0\% Leakage to Outside, Uninsulated & 0 & 0 & 0 & 0 \\ \hline 10\% Leakage to Outside, R-4 & 0.067 & 4 -& 0.033 & 4\\ -10\% Leakage to Outside, R-6 & 0.067 & 6 & 0.033 & 6 \\ +& 0.033 & 4\\ \hline +10\% Leakage to Outside, R-6 & 0.067 & 6 & 0.033 & 6 \\ \hline 10\% Leakage to Outside, R-8 & 0.067 & 8 -& 0.033 & 8 \\ +& 0.033 & 8 \\ \hline 10\% Leakage to Outside, Uninsulated & 0.067 & 0 & -0.033 & 0 \\ +0.033 & 0 \\ \hline 20\% Leakage to Outside, R-4 & 0.133 & 4 -& 0.067 & 4 \\ +& 0.067 & 4 \\ \hline 20\% Leakage to Outside, R-6 & 0.133 & 6 -& 0.067 & 6 \\ +& 0.067 & 6 \\ \hline 20\% Leakage to Outside, R-8 & 0.133 & 8 & 0.067 & 8 \\\hline -20\% Leakage to Outside, Uninsulated & 0.133 & 0 & 0.067 & 0 \\ +20\% Leakage to Outside, Uninsulated & 0.133 & 0 & 0.067 & 0 \\ \hline 30\% Leakage to Outside, R-4 & 0.200 & 4 & -0.100 & 4 \\ -30\% Leakage to Outside, R-6 & 0.200 & 6& 0.100 & 6 \\ +0.100 & 4 \\ \hline +30\% Leakage to Outside, R-6 & 0.200 & 6& 0.100 & 6 \\ \hline 30\% Leakage to Outside, R-8 & 0.200 & 8 -& 0.100 & 8 \\ -30\% Leakage to Outside, Uninsulated & 0.200 & 0 & 0.100 & 0 \\ +& 0.100 & 8 \\ \hline +30\% Leakage to Outside, Uninsulated & 0.200 & 0 & 0.100 & 0 \\ \hline None & 0 & 0 & 0 & 0 \\ \end{customLongTable} @@ -2232,7 +2259,12 @@ \subsubsection{Duct Location} Primary location of duct system. As described earlier, a fraction of the ducts will also be assumed to be in conditioned space for homes with multiple stories. \paragraph{Distribution Data Source(s)} -OpenStudio-HPXML v1.6.0 and Wilson et al., 'Building America House Simulation Protocols', 2014 +\begin{itemize} +\item + +OpenStudio-HPXML v1.6.0 and Building America House Simulation Protocols (\cite{Wilson2014}). +\end{itemize} + \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -2329,7 +2361,13 @@ \subsubsection{HVAC System Single-Speed ASHP Airflow} \paragraph{Description} Single-speed ASHP actual airflow rates for faulted systems. This input file is currently not used since ResStock is still lacking data on faults. \paragraph{Distribution Data Source(s)} -Winkler et al. 'Impact of installation faults in air conditioners and heat pumps in single-family homes on US energy usage' 2020. +\begin{itemize} +\item + +\textit{Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S.~energy usage} (\cite{Winkler2020}). + +\end{itemize} + \paragraph{Direct Conditional Dependencies} \begin{itemize} \item HVAC Heating Efficiency @@ -2385,7 +2423,11 @@ \subsubsection{HVAC System Single-Speed ASHP Charge} ASHP deviation between design/installed charge. Not currently used because of lack of data on faulted HVAC. \paragraph{Distribution Data Source(s)} -Winkler et al. 'Impact of installation faults in air conditioners and heat pumps in single-family homes on US energy usage' 2020. +\begin{itemize} +\item + + \textit{Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S.~energy usage} (\cite{Winkler2020}). +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -2431,8 +2473,11 @@ \subsubsection{HVAC System Single-Speed AC Airflow} Single-speed central and room air conditioner actual air flow rates for faulted systems. Not currently used since ResStock lacks data on faulted systems. \paragraph{Distribution Data Source(s)} -Winkler et al. 'Impact of installation faults in air conditioners and heat pumps in single-family homes on US energy usage' 2020. +\begin{itemize} +\item +\textit{Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S.~energy usage} (\cite{Winkler2020}). +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item HVAC Cooling Efficiency @@ -2490,7 +2535,13 @@ \subsubsection{HVAC System Single-Speed AC Charge} Central and room air conditioner deviation between design/installed charge. \paragraph{Distribution Data Source(s)} -Winkler et al. 'Impact of installation faults in air conditioners and heat pumps in single-family homes on US energy usage' 2020. +\begin{itemize} +\item + +\textit{Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S.~energy usage} (\cite{Winkler2020}). + +\end{itemize} + \paragraph{Direct Conditional Dependencies} \begin{itemize} \item HVAC Cooling Efficiency @@ -2553,8 +2604,9 @@ \subsubsection{HVAC System is Scaled} None. \subsubsection{HVAC System is Faulted} -The presence of an HVAC system giving a fault or error. Note: this is a capability but is not used in baseline ResStock. \paragraph{Description} +The presence of an HVAC system giving a fault or error. Note: this is a capability but is not used in baseline ResStock. + \paragraph{Distribution Data Source(s)} N/A. \paragraph{Direct Conditional Dependencies} @@ -2566,7 +2618,7 @@ \subsubsection{HVAC System is Faulted} \subsection{Ventilation} \subsubsection{Modeling Approach} -Mechanical ventilation, natural ventilation, and local ventilation fans (bath fan, range fan) can be modeled in ResStock. There is currently no mechanical ventilation in the baseline. The bath fan and range fan operate for one hour a day according to the daily hourly schedule specified in the Bathroom Spot Vent Hour and Range Spot Vent Hour characteristics. In aggregate, the distributions of the Bathroom Spot Vent Hour and Range Spot Vent Hour characteristics provide an average schedule for a group of housing units. For default, constraints, and notes about the modeling approach see \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-local-ventilation-fans}{OpenStudio-HPXML Local Ventilation Fans} documentation. Natural ventilation (through opening the windows) is allowed during the Cooling Season under certain outside conditions set by the 2010 House Simulation Protocols (\cite{bahsp_2014}). When ventilating, 1/3 of the operable windows are open for natural ventilation (\cite{bahsp_2010}). +Mechanical ventilation, natural ventilation, and local ventilation fans (bath fan, range fan) can be modeled in ResStock. There is currently no mechanical ventilation in the baseline. The bath fan and range fan operate for one hour a day according to the daily hourly schedule specified in the Bathroom Spot Vent Hour and Range Spot Vent Hour characteristics. In aggregate, the distributions of the Bathroom Spot Vent Hour and Range Spot Vent Hour characteristics provide an average schedule for a group of housing units. For default, constraints, and notes about the modeling approach see \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-local-ventilation-fans}{OpenStudio-HPXML Local Ventilation Fans} documentation. Natural ventilation (through opening the windows) is allowed during the Cooling Season under certain outside conditions set by the 2010 House Simulation Protocols (\cite{bahsp_2010}). When ventilating, 1/3 of the operable windows are open for natural ventilation (\cite{bahsp_2010}). Four different input files influence ventilation in ResStock: \begin{itemize} @@ -2739,7 +2791,7 @@ \subsubsection{Natural Ventilation} Amount and schedule of natural ventilation through operable windows. \paragraph{Distribution Data Source(s)} -Wilson et al. 'Building America House Simulation Protocols' 2014. +Building America House Simulation Protocols (\cite{Wilson2014}). \paragraph{Direct Conditional Dependencies} None. @@ -2777,7 +2829,7 @@ \subsubsection{Bathroom Spot Vent Hour} Bathroom spot ventilation daily start hour. In ResStock, the bathroom fan(s) operates for 1 hour everyday. A schedule is generated on the fly based on these inputs. \paragraph{Distribution Data Source(s)} -Same as occupancy schedule from Wilson et al. "'Building America House Simulation Protocols' 2014. +Same as occupancy schedule from the Building America House Simulation Protocols (\cite{Wilson2014}). \paragraph{Direct Conditional Dependencies} None. @@ -2851,7 +2903,7 @@ \subsubsection{Range Spot Vent Hour} \label{range_spot_vent_hour} Range spot ventilation daily start hour. In ResStock, the range hood operates for 1 hour every day. A schedule is generated on the fly for range spot ventilation based on these inputs. \paragraph{Distribution Data Source(s)} -Derived from national average cooking range schedule in Wilson et al. 'Building America House Simulation Protocols' 2014. +Derived from national average cooking range schedule in Building America House Simulation Protocols (\cite{Wilson2014}). \paragraph{Direct Conditional Dependencies} None. \paragraph{Options} @@ -2906,13 +2958,13 @@ \subsubsection{Range Spot Vent Hour} \label{range_spot_vent_hour} \bottomrule\noalign{} \endlastfoot \texttt{kitchen\_fans\_quantity} & false & \# & Integer & auto & The -quantity of the kitchen fans. \\ +quantity of the kitchen fans. \\ \hline \texttt{kitchen\_fans\_flow\_rate} & false & CFM & Double & auto & The -flow rate of the kitchen fan. \\ +flow rate of the kitchen fan. \\ \hline \texttt{kitchen\_fans\_hours\_in\_operation} & false & hrs/day & Double -& auto & \\ +& auto & Hours per day of operation.\\ \hline \texttt{kitchen\_fans\_power} & false & W & Double & auto & The fan -power of the kitchen fan. \\ +power of the kitchen fan. \\ \hline \texttt{kitchen\_fans\_start\_hour} & false & hr & Integer & auto & The start hour of the kitchen fan. \\ \end{longtable} diff --git a/docs/technical_reference_guide/3c_ResStockInputs.tex b/docs/technical_reference_guide/3c_ResStockInputs.tex index 6fbb6c36ec..0142e2d065 100644 --- a/docs/technical_reference_guide/3c_ResStockInputs.tex +++ b/docs/technical_reference_guide/3c_ResStockInputs.tex @@ -7,7 +7,7 @@ \subsubsection{Modeling Approach} A water heater can be a standalone in-unit appliance or a centrally located system that serves multiple units in a multifamily or single-family attached building. ResStock models different water heating technologies, heating fuels, installation locations, and storage options. ResStock defines the heating efficiency and location using probability distributions. ResStock relies on OpenStudio-HPXML default assumptions for other technical details. To this end, all water heaters are modeled with a setpoint of 125\degree F. All fuel water heaters with an energy factor less than 0.63 are assumed to have an open flue, which increases the housing unit’s air infiltration for water heaters located in conditioned space. \paragraph{Tank Water Heaters} -Conventional storage water heaters are modeled as mixed tanks without additional tank insulation. ResStock calculates the amount of tank losses and the burner efficiency using an energy factor and recovery efficiency \citep{tank_model_parameters}. The recovery efficiency is 0.98 for electric tanks by fiat. The tank volume and heating capacity are calculated based on the number of bedrooms and bathrooms, per Table 8 of the 2014 House simulation protocol (which is based upon guidance from the U.S. Department of Housing and Urban Development [HUD]). +Conventional storage water heaters are modeled as mixed tanks without additional tank insulation. ResStock calculates the amount of tank losses and the burner efficiency using an energy factor and recovery efficiency \citep{tank_model_parameters}. The recovery efficiency is 0.98 for electric tanks by fiat. The tank volume and heating capacity are calculated based on the number of bedrooms and bathrooms, per Table 8 of the 2014 House Simulation Protocol (which is based upon guidance from the U.S. Department of Housing and Urban Development [HUD]). \paragraph{Tankless Water Heaters} Tankless water heaters, unlike storage water heaters, are designed to produce hot water on demand. To this end, they are typically equipped with a burner or electric elements several times larger in capacity. They are also much more compact. In ResStock, their heating performance is defined using an energy factor, which is further derated by 8\% to account for cycling \citep{ansi_resnet_301_2019}. @@ -29,7 +29,7 @@ \subsubsection{Water Heater Fuel}\label{water_heater_fuel} \paragraph{Distribution Data Sources} \begin{itemize} \item - U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + U.S. EIA 2020 RECS microdata \item Alaska-specific distribution is based on Alaska Retrofit Information System (2008 to 2022), maintained by Alaska Housing Finance @@ -67,7 +67,7 @@ \subsubsection{Water Heater Fuel}\label{water_heater_fuel} \item For Alaska, we are using a field in ARIS that lumps multifamily 2--4 units and multifamily 5+ units buildings together. Data from the - American Community Survey are used to distribute the between these two + American Community Survey are used to distribute between these two building types. \item For Alaska, wood and coal heating is modeled as other fuel. @@ -83,7 +83,7 @@ \subsubsection{Water Heater In Unit}\label{water_heater_in_unit} \paragraph{Distribution Data Sources} \begin{itemize} \item - U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + U.S. EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -109,7 +109,7 @@ \subsubsection{Water Heater In Unit}\label{water_heater_in_unit} \item State: Census Division RECS \item - Vintage ACS: Combining Vintage pre 1960s and post 2000 + Vintage ACS: Combining Vintage pre-1960s and post-2000 \item State: Census Region. \end{itemize} @@ -122,7 +122,7 @@ \subsubsection{Water Heater Location}\label{water_heater_location} \paragraph{Distribution Data Sources} \begin{itemize} \item - U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + U.S. EIA 2020 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -197,7 +197,7 @@ \subsubsection{Water Heater Location}\label{water_heater_location} \item 3 + Garage combined. \item - Single-/Multifamily + Foundation combined + Attic combined + + Single/Multifamily + Foundation combined + Attic combined + Garage combined. \item 5 + pre-1960 combined. @@ -218,16 +218,16 @@ \subsubsection{Water Heater Efficiency}\label{water_heater_efficiency} \paragraph{Distribution Sources} \begin{itemize} \item - U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. + U.S. EIA 2020 RECS microdata. \item - (Heat pump water heaters) 2016-17 RBSA II for WA and OR and Butzbaugh - et al. 2017 US HPWH Market Transformation - Where - We've Been and Where to Go Next for remainder of + Heat pump water heaters: 2016-17 RBSA II for WA and OR; Butzbaugh + et al. (\href{https://www.osti.gov/biblio/1433775}{2017}). \textit{US HPWH Market Transformation: Where + We've Been and Where to Go Next} for remainder of regions. \item Penetration of HPWH for Maine (6.71\%) calculated based on total - number of HPWH units (AWHI Stakeholder Meeting 12/08/2022) and total - housing units \url{https://www.census.gov/quickfacts/ME}. + number of HPWH units (from AWHI Stakeholder Meeting 12/08/2022) and total + housing units (from \url{https://www.census.gov/quickfacts/ME}). \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -262,26 +262,26 @@ \subsubsection{Water Heater Efficiency}\label{water_heater_efficiency} \texttt{water\_heater\_efficiency\_type} & \texttt{water\_heater\_recovery\_efficiency}} Electric Heat Pump, 50 gal, 3.45 UEF & heat pump water heater & -electricity & 50 & 3.45 & 0 \\ +electricity & 50 & 3.45 & 0 \\ \hline Electric Heat Pump, 66 gal, 3.35 UEF & heat pump water heater & -electricity & 66 & 3.35 & 0 \\ +electricity & 66 & 3.35 & 0 \\ \hline Electric Heat Pump, 80 gal, 3.45 UEF & heat pump water heater & -electricity & 80 & 3.45 & 0 \\ -Electric Premium & storage water heater & electricity & auto & 0.95 & 0\\ -Electric Standard & storage water heater & electricity & auto & 0.92 & 0 \\ -Electric Tankless & instantaneous water heater & electricity & 0 & 0.99 & 0 \\ +electricity & 80 & 3.45 & 0 \\ \hline +Electric Premium & storage water heater & electricity & auto & 0.95 & 0\\ \hline +Electric Standard & storage water heater & electricity & auto & 0.92 & 0 \\ \hline +Electric Tankless & instantaneous water heater & electricity & 0 & 0.99 & 0 \\ \hline FIXME Fuel Oil Indirect & storage water heater & fuel oil & -auto & 0.62 & 0.78 \\ -Fuel Oil Premium & storage water heater & fuel oil & auto & 0.68 & 0.9 \\ -Fuel Oil Standard & storage water heater & fuel oil & auto & 0.62 & 0.78 \\ -Natural Gas Premium & storage water heater & natural gas & auto & 0.67 & 0.78 \\ -Natural Gas Standard & storage water heater & natural gas & auto & 0.59 & 0.76 \\ +auto & 0.62 & 0.78 \\ \hline +Fuel Oil Premium & storage water heater & fuel oil & auto & 0.68 & 0.9 \\ \hline +Fuel Oil Standard & storage water heater & fuel oil & auto & 0.62 & 0.78 \\ \hline +Natural Gas Premium & storage water heater & natural gas & auto & 0.67 & 0.78 \\ \hline +Natural Gas Standard & storage water heater & natural gas & auto & 0.59 & 0.76 \\ \hline Natural Gas Tankless & instantaneous water heater & natural gas -& 0 & 0.82 & 0 \\ +& 0 & 0.82 & 0 \\ \hline Other Fuel & storage water heater & wood & auto -& 0.59 & 0.76 \\ -Propane Premium & storage water heater & propane & auto & 0.67 & 0.78 \\ -Propane Standard & storage water heater & propane & auto & 0.59 & 0.76 \\ +& 0.59 & 0.76 \\ \hline +Propane Premium & storage water heater & propane & auto & 0.67 & 0.78 \\ \hline +Propane Standard & storage water heater & propane & auto & 0.59 & 0.76 \\ \hline Propane Tankless & instantaneous water heater & propane & 0 & 0.82 & 0 \\ \end{customLongTable} @@ -397,7 +397,7 @@ \subsubsection{Solar Hot Water}\label{solar_hot_water} \paragraph{Distribution Data Sources} \begin{itemize} \item - Not applicable. + Not applicable \item All homes are assumed to not have solar water heating. \end{itemize} @@ -496,7 +496,7 @@ \subsubsection{Hot Water Distribution}\label{hot_water_distribution} \texttt{hot\_water\_distribution\_recirc\_control\_type} & false & & Choice & auto, no control, timer, temperature, presence sensor demand control, manual demand control & If the distribution system is -Recirculation, the type of hot water recirculation control, if any. \\ +Recirculation, the type of hot water recirculation control, if any. \\ \hline \texttt{hot\_water\_distribution\_recirc\_piping\_length} & false & ft & Double & auto & If the distribution system is Recirculation, the length of the recirculation piping. \\ @@ -632,7 +632,7 @@ \subsubsection{Refrigerator} \paragraph{Description} The presence and rated efficiency of the primary refrigerator. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. Age of refrigerator converted to efficiency levels using ENERGY STAR shipment-weighted efficiencies by year data from Home Energy Saver\footnote{For more information, see http://hes-documentation.lbl.gov/.}. +Constructed using U.S. EIA 2020 RECS microdata. Age of refrigerator converted to efficiency levels using ENERGY STAR shipment-weighted efficiencies by year data from Home Energy Saver.\footnote{For more information, see http://hes-documentation.lbl.gov/.} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item Federal Poverty Level @@ -705,7 +705,12 @@ \subsubsection{Refrigerator Usage Level} \paragraph{Description} Refrigerator energy usage level multiplier. \paragraph{Distribution Data Source(s)} +\begin{itemize} +\item + Not applicable---direct translation of the \ref{usage_level} Usage input file. +\end{itemize} + \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -753,7 +758,13 @@ \subsubsection{Misc Extra Refrigerator} The presence and rated efficiency of the secondary refrigerator. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. Age of refrigerator converted to efficiency levels using ENERGY STAR shipment-weighted efficiencies by year data from Home Energy Saver\footnote{For more information, see http://hes-documentation.lbl.gov/.}. %Check the comments in: HES-Refrigerator\_Age\_vs\_Efficiency.tsv +\begin{itemize} +\item + +Constructed using U.S. EIA 2020 RECS microdata. +\item Age of refrigerator converted to efficiency levels using ENERGY STAR shipment-weighted efficiencies by year data from Home Energy Saver.\footnote{For more information, see http://hes-documentation.lbl.gov/.} %Check the comments in: HES-Refrigerator\_Age\_vs\_Efficiency.tsv +\end{itemize} + \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -839,7 +850,13 @@ \subsubsection{Misc Freezer} \paragraph{Description} The presence and rated efficiency of a standalone freezer. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +\begin{itemize} +\item + +Constructed using U.S. EIA 2020 RECS microdata. +\end{itemize} + + \paragraph{Direct Conditional Dependencies} \begin{itemize} \item Federal Poverty Level @@ -912,9 +929,12 @@ \subsubsection{Cooking Range} Presence and fuel type of the cooking range. \paragraph{Distribution Data Source(s)} +\begin{itemize} +\item -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +Constructed using U.S. EIA 2020 RECS microdata. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item Federal Poverty Level @@ -930,22 +950,17 @@ \subsubsection{Cooking Range} \texttt{cooking\_range\_oven\_is\_convection} ResStock arguments (Table \ref{table:hc_opt_cooking}). The \texttt{cooking\_range\_oven\_is\_convection} and \texttt{cooking\_range\_oven\_location} is always set to auto. %options table -\begin{longtable}[]{ |p{3.5cm}|p{3.5cm}|p{3.5cm}|p{3.5cm}| } -\caption{Cooking Range options and arguments that vary for each option} \label{table:hc_opt_cooking} \\ -\toprule\noalign{} -Option name & \texttt{cooking\_range\_oven\_present} & +\begin{customLongTable}{ |p{3.5cm}|p{3.5cm}|p{3.5cm}|p{3.5cm}| } +{Cooking Range options and arguments that vary for each option} {table:hc_opt_cooking} +{Option name & \texttt{cooking\_range\_oven\_present} & \texttt{cooking\_range\_oven\_fuel\_type} & -\texttt{cooking\_range\_oven\_is\_induction} \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot -Electric Induction & true & electricity & true \\ -Electric Resistance & true & electricity & false \\ -Gas & true & natural gas & false \\ -None & false & natural gas & false \\ +\texttt{cooking\_range\_oven\_is\_induction}} +Electric Induction & true & electricity & true \\ \hline +Electric Resistance & true & electricity & false \\\hline +Gas & true & natural gas & false \\\hline +None & false & natural gas & false \\\hline Propane & true & propane & false \\ -\end{longtable} +\end{customLongTable} For the argument definitions, see Table \ref{table:hc_arg_def_cooking_range}. See the OpenStudio-HPXML \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-cooking-range-oven}{Cooking Range/Oven} documentation for the available HPXML schema elements, default values, and constraints. %arguments table @@ -972,7 +987,7 @@ \subsubsection{Cooking Range} For Dual Fuel Range, the distribution is split equally between Electric and Natural Gas. Due to low sample count, the input file is constructed by downscaling a housing unit sub-input file with a household sub-input file. The sub-input files have the following dependencies: -housing unit sub-input file: deps = 'Geometry Building Type RECS', 'State', 'Heating Fuel', and 'Vintage,' with the following fallback coarsening order: +housing unit sub-input file: deps = `Geometry Building Type RECS', `State', `Heating Fuel', and `Vintage,' with the following fallback coarsening order: \begin{enumerate} \item State coarsened to Census Division RECS with AK/HI separate \item Heating Fuel coarsened to Other Fuel, Wood and Propane combined @@ -986,7 +1001,7 @@ \subsubsection{Cooking Range} \item Census Division RECS to Census Region \item Census Region to National. \end{enumerate} -Household sub-input file : deps = 'Geometry Building Type RECS', 'State' 'Tenure', 'Federal Poverty Level,' with the following fallback coarsening order +Household sub-input file : deps = `Geometry Building Type RECS', `State' `Tenure', `Federal Poverty Level,' with the following fallback coarsening order \begin{enumerate} \item State coarsened to Census Division RECS with AK/HI separate \item Geometry Building Type RECS coarsened to SF/MF/MH @@ -997,14 +1012,17 @@ \subsubsection{Cooking Range} \item Census Division RECS to Census Region \item Census Region to National. \end{enumerate} -In combining the housing unit sub-input file and household sub-input file, the conditional relationships are ignored across 'Heating Fuel' and 'Vintage', as well as for 'Tenure' and 'Federal Poverty Level'. +In combining the housing unit sub-input file and household sub-input file, the conditional relationships are ignored across `Heating Fuel' and `Vintage', as well as for `Tenure' and `Federal Poverty Level'. \subsubsection{Cooking Range Usage Level}\label{cooking_range_usage_level} \paragraph{Description} Cooking range energy usage level multiplier. \paragraph{Distribution Data Source(s)} +\begin{itemize} +\item Not applicable---direct translation of the Usage Level input file; see Section \ref{usage_level}. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item Usage Level. @@ -1059,7 +1077,9 @@ \subsubsection{Dishwasher} The presence and rated efficiency of the dishwasher. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +\begin{itemize} +\item Constructed using U.S. EIA 2020 RECS microdata. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1134,7 +1154,10 @@ \subsubsection{Dishwasher Usage Level}\label{dishwasher_usage_level} Dishwasher energy usage level multiplier. \paragraph{Distribution Data Source(s)} +\begin{itemize} +\item Not applicable---direct translation of Usage Level; see Section \ref{usage_level}. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1142,21 +1165,17 @@ \subsubsection{Dishwasher Usage Level}\label{dishwasher_usage_level} \end{itemize} \paragraph{Options} -The dishwasher usage level is set based on the usage level characteristic; See section \ref{usage_level}. It is 80\% Usage when the usage level is Low, 100\% Usage when the usage level is Medium, and 120\% Usage when the usage level is High. The characteristic sets the \texttt{dishwasher\_usage\_multiplier} ResStock argument (Table \ref{table:hc_opt_dish_use}). +The dishwasher usage level is set based on the usage level characteristic; see Section \ref{usage_level}. It is 80\% Usage when the usage level is Low, 100\% Usage when the usage level is Medium, and 120\% Usage when the usage level is High. The characteristic sets the \texttt{dishwasher\_usage\_multiplier} ResStock argument (Table \ref{table:hc_opt_dish_use}). %options table -\begin{longtable}[]{ |p{3cm}|p{8cm}| } -\caption{Dishwasher Usage Level options and arguments that vary for each option} \label{table:hc_opt_dish_use} \\\toprule\noalign{} -Option name & -\texttt{dishwasher\_usage\_multiplier} \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot -80\% Usage & 0.8 \\ -100\% Usage & 1.0 \\ +\begin{customLongTable}{ |p{3cm}|p{8cm}| } +{Dishwasher Usage Level options and arguments that vary for each option} {table:hc_opt_dish_use} +{Option name & +\texttt{dishwasher\_usage\_multiplier}} +80\% Usage & 0.8 \\ \hline +100\% Usage & 1.0 \\ \hline 120\% Usage & 1.2 \\ -\end{longtable} +\end{customLongTable} For the argument definitions, see Table \ref{table:hc_arg_def_dishwasher_usage_level}. @@ -1193,7 +1212,10 @@ \subsubsection{Clothes Washer} Presence and rated efficiency of the clothes washer. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +\begin{itemize} +\item +Constructed using U.S. EIA 2020 RECS microdata. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} \item Clothes Washer Presence @@ -1288,7 +1310,10 @@ \subsubsection{Clothes Washer Presence}\label{clothes_washer_presence} The presence of a clothes washer in the housing unit. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +\begin{itemize} +\item +Constructed using U.S. EIA 2020 RECS microdata. +\end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1331,7 +1356,7 @@ \subsubsection{Clothes Washer Presence}\label{clothes_washer_presence} \end{longtable} \paragraph{Distribution Assumption(s)} -Due to the low sample count, the input file is constructed by downscaling a housing unit sub-input file with a household sub-input file. The sub-input files have the following dependencies. housing unit sub-input file: dependencies = Geometry Building Type RECS, State, Heating Fuel, and Vintage, with the following coarsening order: +Due to the low sample count, the input file is constructed by downscaling a housing unit sub-input file with a household sub-input file. The sub-input files have the following dependencies. Housing unit sub-input file: dependencies = Geometry Building Type RECS, State, Heating Fuel, and Vintage, with the following coarsening order: \begin{enumerate} \item State coarsened to Census Division RECS with AK/HI separate \item Geometry Building Type RECS coarsened to SF/MF/MH @@ -1405,8 +1430,10 @@ \subsubsection{Clothes Washer Usage Level}\label{clothes_washer_usage_level} reflect, e.g., high/low usage occupants. \\ \end{longtable} \paragraph{Distribution Assumption(s)} +\begin{itemize} +\item Engineering judgment. - +\end{itemize} \subsection{Clothes Dryer} \subsubsection{Modeling Approach} @@ -1423,7 +1450,7 @@ \subsubsection{Clothes Dryer}\label{clothes_dryer} The presence, rated efficiency, and fuel type of the clothes dryer in a housing unit. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +Constructed using U.S. EIA 2020 RECS microdata. \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1569,7 +1596,7 @@ \subsubsection{Ceiling Fan} Presence and efficiency of ceiling fans. \paragraph{Distribution Data Source(s)} -Wilson et al. 'Building America House Simulation Protocols' 2014, national average used as saturation. +Building America House Simulation Protocols (\cite{Wilson2014}); national average used as saturation. \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1598,14 +1625,9 @@ \subsubsection{Ceiling Fan} For the argument definitions, see Table \ref{table:hc_arg_def_ceiling_fans}. See the OpenStudio-HPXML \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-ceiling-fans}{Ceiling Fans} documentation for the available HPXML schema elements, default values, and constraints. %arguments table -\begin{longtable}[]{ |p{3.cm}|p{1.5cm}|p{1cm}|p{1.1cm}|p{3.4cm}|p{4cm}| } -\caption{The ResStock argument definitions set in the Ceiling Fan characteristic} \label{table:hc_arg_def_ceiling_fans} \\ -\toprule\noalign{} -Name & Required & Units & Type & Choices & Description \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot +\begin{customLongTable}{ |p{3.cm}|p{1.5cm}|p{1cm}|p{1.1cm}|p{3.4cm}|p{4cm}| } +{The ResStock argument definitions set in the Ceiling Fan characteristic} {table:hc_arg_def_ceiling_fans} +{Name & Required & Units & Type & Choices & Description} \texttt{ceiling\_fan\_present} & true & & Boolean & true, false & Whether there are any ceiling fans. \\ \hline \texttt{ceiling\_fan\_label\_energy\_use} & false & W & Double & auto & @@ -1625,7 +1647,7 @@ \subsubsection{Ceiling Fan} Double & auto & The cooling setpoint temperature offset during months when the ceiling fans are operating. Only applies if ceiling fan quantity is greater than zero. \\ -\end{longtable} +\end{customLongTable} \paragraph{Distribution Assumption(s)} If the unit is vacant, there is no ceiling fan energy. @@ -1643,7 +1665,7 @@ \subsubsection{Misc Pool} The presence of a pool. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +Constructed using U.S. EIA 2020 RECS microdata. \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1706,7 +1728,7 @@ \subsubsection{Misc Pool Heater} The heating fuel of the pool heater if there is a pool. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +Constructed using U.S. EIA 2020 RECS microdata. \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1764,7 +1786,7 @@ \subsubsection{Misc Pool Pump} \paragraph{Description} Presence and size of pool pump. \paragraph{Distribution Data Source(s)} -Wilson et al. 'Building America House Simulation Protocols' 2014, national average fraction used for saturation. +Building America House Simulation Protocols (\cite{Wilson2014}); national average fraction used for saturation. \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1813,7 +1835,7 @@ \subsubsection{Misc Hot Tub Spa} The presence and heating fuel of a hot tub/spa at the housing unit. \paragraph{Distribution Data Source(s)} -Constructed using U.S. EIA 2020 Residential Energy Consumption Survey (RECS) microdata. +Constructed using U.S. EIA 2020 RECS microdata. \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -1854,13 +1876,9 @@ \subsubsection{Misc Hot Tub Spa} For the argument definitions, see Table \ref{table:hc_arg_def_hot_tub}. See the OpenStudio-HPXML \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-permanent-spas}{Permanent Spas} documentation for the available HPXML schema elements, default values, and constraints. -\begin{longtable}[]{|p{3.cm}|p{1.5cm}|p{1.5cm}|p{1.1cm}|p{2.4cm}|p{4.5cm}|} -\caption{The ResStock argument definitions set in the Misc Hot Tub Spa characteristic} \label{table:hc_arg_def_hot_tub} \\\toprule\noalign{} -Name & Required & Units & Type & Choices & Description \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot +\begin{customLongTable}{|p{3.cm}|p{1.5cm}|p{1.5cm}|p{1.1cm}|p{2.4cm}|p{4.5cm}|} +{The ResStock argument definitions set in the Misc Hot Tub Spa characteristic} {table:hc_arg_def_hot_tub} +{Name & Required & Units & Type & Choices & Description} \texttt{permanent\_spa\_present} & true & & Boolean & true, false & Whether there is a permanent spa. \\ \hline @@ -1890,7 +1908,7 @@ \subsubsection{Misc Hot Tub Spa} \texttt{permanent\_spa\_heater\_usage\_multiplier} & false & & Double & auto & Multiplier on the permanent spa heater energy usage that can reflect, e.g., high/low usage occupants. \\ -\end{longtable} +\end{customLongTable} \paragraph{Distribution Assumption(s)} Due to the low sample count, the input file is constructed by downscaling a housing unit sub-input file with a household sub-input file. The sub-input files have the following dependencies: @@ -1911,8 +1929,8 @@ \subsubsection{Misc Hot Tub Spa} \item State coarsened to Census Division RECS with AK/HI separate \item Geometry Building Type RECS coarsened to SF/MF/MH \item Geometry Building Type RECS coarsened to SF and MH/MF - \item Federal Poverty Level coarsened every 100 percent - \item Federal Poverty Level coarsened every 200 percent + \item Federal Poverty Level coarsened every 100\% + \item Federal Poverty Level coarsened every 200\% \item Census Division RECS with AK/HI separate coarsened to Census Division RECS \item Census Division RECS to Census Region \item Census Region to National. @@ -1928,7 +1946,7 @@ \subsubsection{Misc Well Pump} Presence and efficiency of well pump. \paragraph{Distribution Data Source(s)} -Wilson et al. 'Building America House Simulation Protocols' 2014, national average fraction used for saturation. +Building America House Simulation Protocols (\cite{Wilson2014}); national average fraction used for saturation. \paragraph{Direct Conditional Dependencies} None. @@ -1982,14 +2000,14 @@ \subsubsection{Misc Well Pump} \subsection{Miscellaneous Gas Uses} \subsubsection{Modeling Approach} -ResStock models miscellaneous gas loads including fireplaces, grills, and lighting. ResStock randomly assigns these gas appliances to housing units based on saturation estimated by \citet{bahsp_2014}. Gas grill and gas lighting are assumed to be outdoor and therefore do not generate internal gains for the housing unit. For each gas appliance, the annual energy is estimated based on conditioned floor area and number of bedrooms converted from occupants using an equation from \citet{bahsp_2010} and can be adjusted by a usage multiplier. The annual energy is then multiplied by a default simple schedule to produce the end-use load profile. The gas lighting characteristic distribution is in Section \ref{misc_gas_lighting}. +ResStock models miscellaneous gas loads including fireplaces, grills, and lighting. ResStock randomly assigns these gas appliances to housing units based on saturation estimated by \citet{Wilson2014}. Gas grill and gas lighting are assumed to be outdoor and therefore do not generate internal gains for the housing unit. For each gas appliance, the annual energy is estimated based on conditioned floor area and number of bedrooms converted from occupants using an equation from \citet{bahsp_2010} and can be adjusted by a usage multiplier. The annual energy is then multiplied by a default simple schedule to produce the end-use load profile. The gas lighting characteristic distribution is in Section \ref{misc_gas_lighting}. \subsubsection{Misc Gas Fireplace} \paragraph{Description} Presence of a gas fireplace. \paragraph{Distribution Data Source(s)} -Wilson et al. 'Building America House Simulation Protocols' 2014, national average fraction used for saturation. +Building America House Simulation Protocols (\cite{Wilson2014}); national average fraction used for saturation. \paragraph{Direct Conditional Dependencies} None. @@ -2056,14 +2074,14 @@ \subsubsection{Misc Gas Grill} Presence of a gas grill. \paragraph{Distribution Data Source(s)} -Wilson et al. 'Building America House Simulation Protocols' 2014, national average fraction used for saturation. +Building America House Simulation Protocols (\cite{Wilson2014}); national average fraction used for saturation. \paragraph{Direct Conditional Dependencies} None. \paragraph{Options} -The options for Misc Gas Grill are ``Gas Grill'' if the housing unit has a gas grill or ``None'' if the housing unit does not have a gas grill. The characteristic sets the \texttt{misc\_fuel\_loads\_grill\_present}, \texttt{misc\_fuel\_loads\_grill\_fuel\_type}, \texttt{misc\_fuel\_loads\_grill\_annual\_therm}, and \texttt{misc\_fuel\_loads\_grill\_usage\_multiplier} ResStock arguments (Table \ref{table:hc_opt_def_gas_grill}). The \texttt{misc\_fuel\_loads\_grill\_fuel\_type} is always set to natural gas. +The options for Misc Gas Grill are ``Gas Grill'' if the housing unit has a gas grill or ``None'' if the housing unit does not have a gas grill. The characteristic sets the \texttt{misc\_fuel\_loads\_grill\_present}, \texttt{misc\_fuel\_loads\_grill\_fuel\_type}, \texttt{misc\_fuel\_loads\_grill\_annual\_therm}, and \texttt{misc\_fuel\_loads\_grill\_usage\_multiplier} ResStock arguments (Table \ref{table:hc_opt_def_gas_grill}). The \texttt{misc\_fuel\_loads\_grill\_fuel\_type} is always set to natural gas. \begin{longtable}[]{ |p{2.5cm}|p{3cm}|p{3cm}|p{3cm}|p{3cm}| } \caption{Misc Gas Grill options and arguments that vary for each option} \label{table:hc_opt_def_gas_grill} \\ @@ -2165,21 +2183,16 @@ \subsubsection{PV Orientation} \label{sec:pv_orientation} For the argument definitions, see Table \ref{table:hc_arg_def_pv_orient}. See the OpenStudio-HPXML \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-photovoltaics}{Photovoltaics} documentation for the available HPXML schema elements, default values, and constraints. -\begin{longtable}[]{|p{3.cm}|p{1.5cm}|p{1.5cm}|p{1.1cm}|p{2.4cm}|p{4.5cm}|} -\caption{The ResStock argument definitions set in the PV Orientation characteristic} \label{table:hc_arg_def_pv_orient} \\ -\toprule\noalign{} -Name & Required & Units & Type & Choices & Description \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot -\texttt{pv\_system\_array\_azimuth} & true & degrees & Double & & Array +\begin{customLongTable}{|p{3.cm}|p{1.5cm}|p{1.5cm}|p{2.4cm}|p{5.5cm}|} +{The ResStock argument definitions set in the PV Orientation characteristic} {table:hc_arg_def_pv_orient} +{Name & Required & Units & Type & Description} +\texttt{pv\_system\_array\_azimuth} & true & degrees & Double & Array azimuth of the PV system. Azimuth is measured clockwise from north (e.g., North=0, East=90, South=180, West=270). \\ -\texttt{pv\_system\_2\_array\_azimuth} & true & degrees & Double & & +\texttt{pv\_system\_2\_array\_azimuth} & true & degrees & Double & Array azimuth of the second PV system. Azimuth is measured clockwise from north (e.g., North=0, East=90, South=180, West=270). \\ -\end{longtable} +\end{customLongTable} \paragraph{Distribution Assumption(s)} \begin{itemize} @@ -2205,15 +2218,10 @@ \subsubsection{PV System Size} \label{sec:pv_system_size} The following arguments are set to auto: \texttt{pv\_system\_module\_type}, \texttt{pv\_system\_tracking}, \texttt{pv\_system\_max\_power\_output}, \texttt{pv\_system\_inverter\_efficiency}, \texttt{pv\_system\_system\_losses\_fraction}, \texttt{pv\_system\_2\_module\_type}, \texttt{pv\_system\_2\_tracking}, and \texttt{pv\_system\_2\_max\_power\_output}. The \texttt{pv\_system\_location} and \texttt{pv\_system\_2\_location} are set to roof. \texttt{pv\_system\_array\_tilt} and \texttt{pv\_system\_2\_array\_tilt} are always set to roofpitch. \texttt{pv\_system\_2\_present} is always false. \texttt{pv\_system\_2\_max\_power\_output} is always 0. -\begin{longtable}[]{ |p{2.5cm}|p{4cm}|p{4cm}| } -\caption{PV System Size options and arguments that vary for each option} \label{table:hc_opt_def_pv_size} \\ -\toprule\noalign{} -Option name & \texttt{pv\_system\_present} & -\texttt{pv\_system\_max\_power\_output} \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot +\begin{customLongTable}{ |p{2.5cm}|p{4cm}|p{4cm}| } +{PV System Size options and arguments that vary for each option} {table:hc_opt_def_pv_size} +{Option name & \texttt{pv\_system\_present} & +\texttt{pv\_system\_max\_power\_output}} 1.0 kWDC & true & 100\\ 3.0 kWDC & true & 3,000 \\ 5.0 kWDC & true & 5,000 \\ @@ -2222,17 +2230,13 @@ \subsubsection{PV System Size} \label{sec:pv_system_size} 11.0 kWDC &true & 11,000 \\ 13.0 kWDC & true & 13,000 \\ None & false & 0 \\ -\end{longtable} +\end{customLongTable} For the argument definitions, see Table \ref{table:hc_arg_def_pv_size}. See the OpenStudio-HPXML \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-photovoltaics}{Photovoltaics} documentation for the available HPXML schema elements, default values, and constraints. -\begin{longtable}[]{|p{3.cm}|p{1.5cm}|p{1.5cm}|p{1.1cm}|p{2.4cm}|p{4.5cm}|} -\caption{The ResStock argument definitions set in the PV System Size characteristic} \label{table:hc_arg_def_pv_size} \\\toprule\noalign{} -Name & Required & Units & Type & Choices & Description \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot +\begin{customLongTable}{|p{3.cm}|p{1.5cm}|p{1.5cm}|p{1.1cm}|p{2.4cm}|p{4.5cm}|} +{The ResStock argument definitions set in the PV System Size characteristic} {table:hc_arg_def_pv_size} +{Name & Required & Units & Type & Choices & Description} \texttt{pv\_system\_present} & true & & Boolean & true, false & Whether there is a PV system present. \\ \hline @@ -2281,7 +2285,7 @@ \subsubsection{PV System Size} \label{sec:pv_system_size} \texttt{pv\_system\_2\_max\_power\_output} & true & W & Double & & Maximum power output of the second PV system. For a shared system, this is the total building maximum power output. \\ -\end{longtable} +\end{customLongTable} \paragraph{Distribution Assumption(s)} Installations of unknown mount type are assumed to be rooftop. States without data are backfilled with aggregates at the Census Region. ``East South Central'' assumed the same distribution as ``West South Central.'' @@ -2420,7 +2424,7 @@ \subsection{Modeling Approach} \item{Lighting}---Electric lighting: interior, exterior, and garage \item{Misc Gas Lighting}---Exterior natural gas lighting \item{Holiday Lighting}---Not used - \item{Lighting Interior Use}--Not used + \item{Lighting Interior Use}---Not used \item{Lighting Other Use}---Not used. \end{itemize} @@ -2438,7 +2442,7 @@ \subsubsection{Lighting}\label{lighting} \begin{itemize} \item - U.S. EIA 2015 Residential Energy Consumption Survey (RECS) microdata. + U.S. EIA 2015 RECS microdata. \item 2019 Energy Savings Forecast of Solid-State Lighting in General Illumination Applications.\footnote{https://www.energy.gov/sites/prod/files/2019/12/f69/2019\_ssl-energy-savings-forecast.pdf} @@ -2484,50 +2488,49 @@ \subsubsection{Lighting}\label{lighting} For the argument definitions, see Table \ref{table:hc_arg_def_lighting}. See the OpenStudio-HPXML \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-lighting}{Lighting} documentation for the available HPXML schema elements, default values, and constraints. -\begin{customLongTable}{ |p{3.cm}|p{1.5cm}|p{1cm}|p{1.1cm}|p{1.4cm}|p{6cm}| } +\begin{customLongTable}{ |p{4.cm}|p{1.5cm}|p{1.5cm}|p{1.1cm}|p{1.5cm}|p{4.4cm}|} {The ResStock arguments set in the Lighting characteristic} {table:hc_arg_def_lighting} {Name & Required & Units & Type & Choices & Description} \texttt{lighting\_present} & true & & Boolean & true, false & Whether there is lighting energy use. \\ \hline -\texttt{lighting\_interior\_fraction\_cfl} & true & & Double & & +\texttt{lighting\_interior\_fraction\_cfl} & true & Double & & & Fraction of all lamps (interior) that are compact fluorescent. Lighting not specified as CFL, LFL, or LED is assumed to be incandescent. \\ \hline -\texttt{lighting\_interior\_fraction\_lfl} & true & & Double & & +\texttt{lighting\_interior\_fraction\_lfl} & true & Double & & & Fraction of all lamps (interior) that are linear fluorescent. Lighting not specified as CFL, LFL, or LED is assumed to be incandescent. \\ \hline -\texttt{lighting\_interior\_fraction\_led} & true & & Double & & +\texttt{lighting\_interior\_fraction\_led} & true & Double & & & Fraction of all lamps (interior) that are light emitting diodes. Lighting not specified as CFL, LFL, or LED is assumed to be incandescent. \\ \hline -\texttt{lighting\_exterior\_fraction\_cfl} & true & & Double & & +\texttt{lighting\_exterior\_fraction\_cfl} & true & Double & & & Fraction of all lamps (exterior) that are compact fluorescent. Lighting not specified as CFL, LFL, or LED is assumed to be incandescent. \\ \hline -\texttt{lighting\_exterior\_fraction\_lfl} & true & & Double & & +\texttt{lighting\_exterior\_fraction\_lfl} & true & Double & & & Fraction of all lamps (exterior) that are linear fluorescent. Lighting not specified as CFL, LFL, or LED is assumed to be incandescent. \\ \hline -\texttt{lighting\_exterior\_fraction\_led} & true & & Double & & +\texttt{lighting\_exterior\_fraction\_led} & true & Double & & & Fraction of all lamps (exterior) that are light emitting diodes. Lighting not specified as CFL, LFL, or LED is assumed to be incandescent. \\ \hline -\texttt{lighting\_garage\_fraction\_cfl} & true & & Double & & Fraction +\texttt{lighting\_garage\_fraction\_cfl} & true & Double & & & Fraction of all lamps (garage) that are compact fluorescent. Lighting not specified as CFL, LFL, or LED is assumed to be incandescent. \\ \hline -\texttt{lighting\_garage\_fraction\_lfl} & true & & Double & & Fraction +\texttt{lighting\_garage\_fraction\_lfl} & true & Double & & & Fraction of all lamps (garage) that are linear fluorescent. Lighting not specified as CFL, LFL, or LED is assumed to be incandescent. \\ \hline -\texttt{lighting\_garage\_fraction\_led} & true & & Double & & Fraction +\texttt{lighting\_garage\_fraction\_led} & true & Double & & & Fraction of all lamps (garage) that are light emitting diodes. Lighting not specified as CFL, LFL, or LED is assumed to be incandescent. \\ - \end{customLongTable} \paragraph{Distribution Assumption(s)}\label{assumption-67} @@ -2559,9 +2562,7 @@ \subsubsection{Miscellaneous Gas Lighting}\label{misc_gas_lighting} \begin{itemize} \item - Wilson et al. \textquotesingle Building America House Simulation - Protocols\textquotesingle{} 2014, national average fraction used for - saturation. + Building America House Simulation Protocols (\cite{Wilson2014}); national average fraction used for saturation. \end{itemize} \paragraph{Direct Conditional Dependencies} None. @@ -2709,7 +2710,6 @@ \subsubsection{Lighting Other Use}\label{lighting_other_use} \texttt{lighting\_garage\_usage\_multiplier} & false & & Double & auto & Multiplier on the lighting energy usage (garage) that can reflect, e.g., high/low usage occupants. \\ - \\ \end{longtable} \paragraph{Distribution Assumption(s)} @@ -2738,7 +2738,7 @@ \subsubsection{Plug Loads}\label{plug_loads} \begin{itemize} \item - U.S. EIA 2015 Residential Energy Consumption Survey (RECS) microdata. + U.S. EIA 2015 RECS microdata. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -2758,15 +2758,10 @@ \subsubsection{Plug Loads}\label{plug_loads} \item \texttt{misc\_plug\_loads\_other\_frac\_latent} = 0.021. \end{itemize} -\begin{longtable}[] {|p{3.5cm}|p{2.5cm}|p{2.5cm}|}\caption{The ResStock arguments set in the Plug Loads characteristic} \label{table:hc_opt_plug_load} \\ -\toprule\noalign{} -Option name & +\begin{customLongTable} {|p{3.5cm}|p{2.5cm}|p{2.5cm}|}{The ResStock arguments set in the Plug Loads characteristic} {table:hc_opt_plug_load} +{Option name & \texttt{misc\_plug\_loads\_television\_usage\_multiplier} & -\texttt{misc\_plug\_loads\_other\_usage\_multiplier} \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot +\texttt{misc\_plug\_loads\_other\_usage\_multiplier}} 78\% & 0.78 & 0.78 \\ 79\% & 0.79 & 0.79 \\ 82\% & 0.82 & 0.82 \\ @@ -2798,7 +2793,7 @@ \subsubsection{Plug Loads}\label{plug_loads} 140\% & 1.4 & 1.4 \\ 144\% & 1.44 & 1.44 \\ 166\% & 1.66 & 1.66 \\ -\end{longtable} +\end{customLongTable} For the argument definitions, see Table \ref{table:hc_arg_def_plug_load}. See the OpenStudio-HPXML \href{https://openstudio-hpxml.readthedocs.io/en/v1.8.1/workflow_inputs.html#hpxml-plug-loads}{Plug Loads} documentation for the available HPXML schema elements, default values, and constraints. @@ -2836,8 +2831,7 @@ \subsubsection{Plug Loads}\label{plug_loads} \begin{itemize} \item - Multipliers are based on ratio of the ResStock MELS regression - equations and the MELS modeled in RECS. + Multipliers are based on ratio of the ResStock miscellaneous electric loads (MELS) regression equations and the MELS modeled in RECS. \end{itemize} \subsubsection{Plug Load Diversity}\label{plug_load_diversity} @@ -2847,7 +2841,7 @@ \subsubsection{Plug Load Diversity}\label{plug_load_diversity} \begin{itemize} \item - Engineering Judgment, Calibration. + Engineering judgment, calibration. \end{itemize} \paragraph{Direct Conditional Dependencies} \begin{itemize} @@ -2924,7 +2918,7 @@ \subsection{Income} Per Table \ref{tab:fpl}, the poverty line is \$25,750 for a household of four in the contiguous U.S. A household of the same size making \$40,000 per year in Colorado is therefore at 150\%--200\% of FPL (40,000/25,750*100\% = 155\%). However, that exact household would be considered 100\%--150\% of FPL if living in Hawaii instead (40,000/29,620*100\% = 135\%). FPL is used to determine eligibility for several federal assistance programs, including Low-Income Home Energy Assistance Program (LIHEAP) and Weatherization Assistance for Low-Income Persons (\cite{aspe_fpl_use}). -Similar to FPL, Area Median Income in ResStock is household income standardized as a percentage of the \href{https://www.huduser.gov/portal/datasets/il.html#data_2019}{2019 Income Limits}, which are annually updated by HUD. The Income Limits are means-testing metrics intended to determine financial assistance eligibility, such as Section 8 housing, based on family income (\cite{hud2019_inc_lim_method}). Since ResStock does not model multiple families sharing a single housing unit, household income is treated the same as family income, and household income is used to calculate percent Area Median Income instead. Like FPL, the income limits vary by family size. But unlike FPL, they adjust for local housing costs and vary by county subdivisions. Generally, 0\%--80\% Area Median Income is regarded as Low-to-Moderate Income and the threshold for receiving most types of financial assistance. Sometimes 80\%--150\% Area Median Income are eligible for partial financial assistance, such as in Section 50122 of the Inflation Reduction Act (\cite{2022IRA}) for home electrification rebates. +Similar to FPL, Area Median Income in ResStock is household income standardized as a percentage of the \href{https://www.huduser.gov/portal/datasets/il.html#data_2019}{2019 Income Limits}, which are annually updated by HUD. The Income Limits are means-testing metrics intended to determine financial assistance eligibility, such as Section 8 housing, based on family income (\cite{hud2019_inc_lim_method}). Since ResStock does not model multiple families sharing a single housing unit, household income is treated the same as family income, and household income is used to calculate percent Area Median Income instead. Like FPL, the income limits vary by family size. But unlike FPL, they adjust for local housing costs and vary by county subdivisions. Generally, 0\%--80\% Area Median Income is regarded as Low-to-Moderate Income and the threshold for receiving most types of financial assistance. Sometimes 80\%--150\% Area Median Income households are eligible for partial financial assistance, such as in Section 50122 of the Inflation Reduction Act (\cite{2022IRA}) for home electrification rebates. State Metro Median Income (SMMI) is a variant of Area Median Income. As the name suggests, SMMI standardizes the household income based on 2019 Income Limits set at the state level while differentiating between metropolitan and non-metropolitan areas. This metric is created primarily for the integration of socio-demographically differentiated time-use schedules from the American Time Use Survey, which tags respondents by state and metro status. @@ -2970,8 +2964,7 @@ \subsubsection{Income}\label{income} \paragraph{Distribution Data Sources} \begin{itemize} \item - 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University - of Minnesota, www.ipums.org. + 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3014,8 +3007,7 @@ \subsubsection{Income RECS2015}\label{income_recs2015} \paragraph{Distribution Data Sources} \begin{itemize} \item - 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University - of Minnesota, www.ipums.org. + 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3041,8 +3033,7 @@ \subsubsection{Income RECS2020}\label{income_recs2020} \paragraph{Distribution Data Sources} \begin{itemize} \item - 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University - of Minnesota, www.ipums.org. + 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3066,7 +3057,7 @@ \subsubsection{Federal Poverty Level}\label{federal_poverty_level} \paragraph{Distribution Data Sources} \begin{itemize} \item - Income from 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University of Minnesota, www.ipums.org. + Income from 2019 5-year PUMS from the University of Minnesota. \item 2019 federal poverty guidelines from \href{https://aspe.hhs.gov/topics/poverty-economic-mobility/poverty-guidelines/prior-hhs-poverty-guidelines-federal-register-references/2019-poverty-guidelines}{Office of the Assistant Secretary for Planning and Evaluation within the U.S. Department of Health and Human Services}. \end{itemize} @@ -3084,7 +3075,7 @@ \subsubsection{Federal Poverty Level}\label{federal_poverty_level} \begin{itemize} \item Percent Federal Poverty Level is calculated using annual household income - in 2019 USD (continuous, not binned) from 2019-5yrs PUMS data and 2019 + in 2019 USD (continuous, not binned) from 2019 5-year PUMS data and 2019 Federal Poverty Lines for contiguous U.S., where the FPL threshold for 1-occupant household is \$12,490 and \$4,420 for every additional person in the household. @@ -3097,7 +3088,7 @@ \subsubsection{Area Median Income}\label{area_median_income} \paragraph{Distribution Data Sources} \begin{itemize} \item - Income from 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University of Minnesota, www.ipums.org. + Income from 2019 5-year PUMS from the University of Minnesota. \item Area Median Income definitions based on \href{https://www.huduser.gov/portal/datasets/il.html#data_2019}{2019 Income Limits from HUD}. @@ -3117,7 +3108,7 @@ \subsubsection{Area Median Income}\label{area_median_income} \begin{enumerate} \item Percent Area Median Income is calculated using annual household income in - 2019 USD (continuous, not binned) from 2019-5yrs PUMS data and 2019 + 2019 USD (continuous, not binned) from 2019 5-year PUMS data and 2019 income limits from HUD. These limits adjust for household size AND local housing costs (i.e., fair market rents). Income limits reported at county subdivisions are consolidated to County using a \href{https://mcdc.missouri.edu/applications/geocorr2014.html}{crosswalk} @@ -3139,9 +3130,9 @@ \subsubsection{State Metro Median Income}\label{state_metro_median_income} \paragraph{Distribution Data Sources} \begin{itemize} \item - Income from 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University of Minnesota, www.ipums.org. + Income from 2019 5-year PUMS from the University of Minnesota. \item - Income Limits derived from \href{Income from 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University of Minnesota, www.ipums.org.}{2019 median income by state and metro/nonmetro area from HUD}. + Income Limits derived from 2019 5-year PUMS from the University of Minnesota and 2019 median income by state and metro/nonmetro area from HUD. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3158,7 +3149,7 @@ \subsubsection{State Metro Median Income}\label{state_metro_median_income} \begin{itemize} \item Percent State Metro Median Income is calculated using annual household - income in 2019 USD (continuous, not binned) from 2019-5yrs PUMS data + income in 2019 USD (continuous, not binned) from 2019 5-year PUMS data and 2019 median income by state and metro/nonmetro area from HUD. A County Metro Status-differentiated Income Limits table is derived from the median income table by adjusting for household size, which is consistent with the method of generating state income limits by HUD. @@ -3171,8 +3162,7 @@ \subsubsection{Occupants}\label{occupants} \paragraph{Distribution Data Sources} \begin{itemize} \item - 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University - of Minnesota, www.ipums.org. + 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3188,13 +3178,8 @@ \subsubsection{Occupants}\label{occupants} \paragraph{Options} The Occupants options range from 0 to 10+ for the the ResStock baseline baseline (Table \ref{table:hc_arg_def_occupants}). This characteristic assigns value to \texttt{geometry\_unit\_num\_occupants} accordingly, with \texttt{geometry\_unit\_num\_occupants}=11 for Occupants=10+. Occupants=0 corresponds to vacant units. \texttt{general\_water\_use\_usage\_multiplier} is auto-calculated from occupants. -\begin{longtable}[]{|p{3.5cm}|p{1.5cm}|p{1cm}|p{1.1cm}|p{1.9cm}|p{5cm}|} \caption{The ResStock arguments set in the Occupants characteristic} \label{table:hc_arg_def_occupants} \\ -\toprule\noalign{} -Name & Required & Units & Type & Choices & Description \\ -\midrule\noalign{} -\endhead -\bottomrule\noalign{} -\endlastfoot +\begin{customLongTable}{|p{3.5cm}|p{1.5cm}|p{1cm}|p{1.1cm}|p{1.9cm}|p{5cm}|} {The ResStock arguments set in the Occupants characteristic} {table:hc_arg_def_occupants} +{Name & Required & Units & Type & Choices & Description} \texttt{geometry\_unit\_num\_occupants} & false & \# & Double & & The number of occupants in the unit. If not provided, an \emph{asset} calculation is performed assuming standard occupancy, in which various @@ -3210,7 +3195,7 @@ \subsubsection{Occupants}\label{occupants} mopping, shower evaporation, water films on showers, tubs \& sinks surfaces, plant watering, etc.) that can reflect, e.g., high/low usage occupants. \\ -\end{longtable} +\end{customLongTable} \paragraph{Distribution Assumptions} \begin{itemize} @@ -3243,8 +3228,7 @@ \subsubsection{Vacancy Status}\label{vacancy_status} \paragraph{Distribution Data Sources} \begin{itemize} \item - 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University - of Minnesota, www.ipums.org. + 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3300,8 +3284,7 @@ \subsubsection{Tenure}\label{tenure} \paragraph{Distribution Data Sources} \begin{itemize} \item - 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University - of Minnesota, www.ipums.org. + 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} @@ -3335,8 +3318,7 @@ \subsubsection{Household Has Tribal \paragraph{Distribution Data Sources} \begin{itemize} \item - 2019-5yrs Public Use Microdata Samples (PUMS). IPUMS USA, University - of Minnesota, www.ipums.org. + 2019 5-year PUMS from the University of Minnesota. \end{itemize} \paragraph{Direct Conditional Dependencies} diff --git a/docs/technical_reference_guide/5_ResStockOutputs.tex b/docs/technical_reference_guide/5_ResStockOutputs.tex index bbbf4270a4..e6e830406b 100644 --- a/docs/technical_reference_guide/5_ResStockOutputs.tex +++ b/docs/technical_reference_guide/5_ResStockOutputs.tex @@ -1,5 +1,5 @@ \chapter{ResStock Outputs}\label{sec:resstock_outputs} -ResStock produces a range of results around energy, housing characteristics, schedules, emissions, and costs. This section overviews the outputs available in the latest ResStock data release (2024.2). The data dictionary that summarizes these outputs is available with the \href{https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/2024/resstock_amy2018_release_2/data_dictionary.tsv}{data release}. +ResStock produces a range of results around energy, housing characteristics, schedules, emissions, and costs. This section overviews the outputs available in the latest ResStock data release (2024 release 2). The data dictionary that summarizes these outputs is available with the \href{https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/2024/resstock_amy2018_release_2/data_dictionary.tsv}{data release}. \section{Building Characteristics} %Tables - basically our data dictionaries (ideally automated). Data dictionaries are already automated but need heavy QC @@ -107,8 +107,8 @@ \section{Building Characteristics} in.hvac\_system\_single\_speed\_ashp\_airflow & Not used \\ \hline in.hvac\_system\_single\_speed\_ashp\_charge & Not used \\ \hline in.income & Income bin of the household occupying the housing unit \\ \hline - in.income\_recs\_2015 & Income bin of the household occupying the housing unit aligned with the 2015 U.S. Energy Information Administration Residential Energy Consumption Survey \\ \hline - in.income\_recs\_2020 & Income bin of the household occupying the housing unit aligned with the 2020 U.S. Energy Information Administration Residential Energy Consumption Survey \\ \hline + in.income\_recs\_2015 & Income bin of the household occupying the housing unit aligned with the 2015 U.S. EIA RECS \\ \hline + in.income\_recs\_2020 & Income bin of the household occupying the housing unit aligned with the 2020 U.S. EIA RECS \\ \hline in.infiltration & Air leakage rates for the living and garage spaces \\ \hline in.insulation\_ceiling & Ceiling insulation level (between the living space and unconditioned attic) \\ \hline in.insulation\_floor & Floor insulation level \\ \hline diff --git a/docs/technical_reference_guide/6_PublicDataAccess.tex b/docs/technical_reference_guide/6_PublicDataAccess.tex index 32f3fa6982..8a23e2281c 100644 --- a/docs/technical_reference_guide/6_PublicDataAccess.tex +++ b/docs/technical_reference_guide/6_PublicDataAccess.tex @@ -11,7 +11,7 @@ \section{Open Energy Data Initiative} \item The full-year (annual) results for each housing unit sample model, in both .csv and .parquet formats \item The building energy models used in the run, in either .idf, .osm, or .xml format \item The schedule files for each housing unit sample used in running the models - \item Select fields of the weather data (e.g., \href{https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2Fend-use-load-profiles-for-us-building-stock%2F2024%2Fresstock_tmy3_release_2%2Fweather%2F}{2024.2}, that is associated with the model run + \item Select fields of the weather data (e.g., \href{https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2Fend-use-load-profiles-for-us-building-stock%2F2024%2Fresstock_tmy3_release_2%2Fweather%2F}{2024.2}) that is associated with the model run \item Data dictionaries \item Documentation containing details of the ResStock run and upgrade measures. \end{itemize} diff --git a/docs/technical_reference_guide/README.md b/docs/technical_reference_guide/README.md index 1a44123dc6..f546f8c7e4 100644 --- a/docs/technical_reference_guide/README.md +++ b/docs/technical_reference_guide/README.md @@ -9,7 +9,7 @@ This file is then committed in the "Latest results" commit in the `Commit latest These actions will help keep the document up to date with any changes and fail in the tests if the document does not compile. ## Building Technical Reference Guide Locally -To compile the ResStock Technical Reference Guide locally it is recommended to use the same environment as the GitHub Action (currently ubuntu 22.04). To get this enviornment install [Docker](https://www.docker.com/) and follow the steps below. After the first time running through this process, where docker container is built and texlive is installed steps #2 and #4 can be skipped. +Docker can be used to compile the ResStock Technical Reference Guide locally. First install [Docker](https://www.docker.com/) and follow the steps below. 1. With the terminal/command prompt navigate to the resstock repository technical reference guide directory. @@ -17,32 +17,33 @@ To compile the ResStock Technical Reference Guide locally it is recommended to u cd /docs/technical_reference_guide ``` -2. Build an ubuntu container similar to GitHub Actions ubuntu 22.04 (only needs to be done the first time) +2. Pull a docker container with the full version of textlive (this might take several minutes, but only needs to be done once. If you have issues, get off the VPN during the pull) ``` -docker build -t github-actions-ubuntu22 . +$ docker pull mfisherman/texlive-full ``` -3. Run the newly built container and mount the current directory contents in the workspace directory of the container. +3. Run the container and mount the current directory contents in the workspace directory of the container. Use /bin/bash as the default shell. ``` -docker run -it -v $(pwd):/workspace github-actions-ubuntu22 +docker run --rm -it -v $(pwd):/workspace mfisherman/texlive-full /bin/bash ``` -4. Install the full texlive package (only needs to be done the first time and might take a few minutes) +4. Go to the workspace folder ``` -apt-get install texlive-full +cd ../workspace ``` -5. Go to the workspace - +6. Create a _build directory for the output of `pdflatex` to be stored. ``` -cd workspace +mkdir _build ``` -6. Compile the documentation (this command may need to be run two times for some parts of the documentation to show up in the output pdf) +5. Compile the documentation (this command may need to be run two times for some parts of the documentation to show up in the output pdf) ``` -pdflatex ResStockTechnicalReferenceGuide.tex -file-line-error -interaction=nonstopmode +latexmk -pdf -latexoption=-file-line-error -latexoption=-interaction=nonstopmode -output-directory=_build -halt-on-error ResStockTechnicalReferenceGuide.tex + +6. All of the pdf and log files from the compile will be located in docs/technical_reference_guide/_build (Note: this won't show up as modified files in git because _build is in .gitignore) ``` diff --git a/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.tex b/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.tex index 1e81174218..bff3cb8445 100644 --- a/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.tex +++ b/docs/technical_reference_guide/ResStockTechnicalReferenceGuide.tex @@ -124,10 +124,13 @@ \chapter{List of Acronyms} \acro {ACH} {air changes per hour} \acro {ACS} {American Community Survey} \acro {AFUE} {Annual Fuel Utilization Efficiency} +\acro {AIANNH}{American Indian/Alaska Native/Native Hawaiian} \acro {AMY}{Actual Meteorological Year} \acro {ASHP}{air-source heat pump} \acro {ATUS}{American Time Use Survey} \acro {AWS}{Amazon Web Services} +\acro {CBSA}{Core-Based Statistical Area} +\acro {CEC}{California Energy Commission} \acro {CEER}{combined energy efficiency ratio} \acro {CFL}{compact florescent bulb} \acro {CFM}{cubic feet per minute} @@ -151,11 +154,11 @@ \chapter{List of Acronyms} \acro {LIHEAP}{Low-Income Home Energy Assistance Program} \acro {MELS} {Miscellaneous Electric Loads} \acro {MF}{multifamily} +\acro {MSA}{Metropolitan Statistical Area} \acro {MSHP}{mini-split heat pump} \acro {NEEA}{Northwest Energy Efficiency Alliance} \acro {NFRC}{National Fenestration Rating Council} \acro {NREL}{National Renewable Energy Laboratory} -\acro {NUMAPTS} {} \acro {OEDI}{Open Energy Dataset Initiative} \acro {PUMA}{Public Use Microdata Area} \acro {PUMS}{Public Use Microdata Samples} @@ -166,7 +169,7 @@ \chapter{List of Acronyms} \acro {SEER}{Seasonal Energy Efficiency Ratio} \acro {SFA}{single-family attached} \acro {SFD}{single-family detached} -\acro {TMY}{Typical Metorological Year} +\acro {TMY}{Typical Meteorological Year} \acro {WWR}{window-to-wall ratio} \end{acronym} diff --git a/docs/technical_reference_guide/dockerfile b/docs/technical_reference_guide/dockerfile deleted file mode 100644 index 627e8869a2..0000000000 --- a/docs/technical_reference_guide/dockerfile +++ /dev/null @@ -1,32 +0,0 @@ -# Start with Ubuntu 22.04 as the base image -FROM ubuntu:22.04 - -# Update and install required dependencies -RUN apt-get update && apt-get install -y \ - software-properties-common \ - build-essential \ - curl \ - git \ - wget \ - unzip \ - zip \ - tar \ - python3 \ - python3-pip \ - python3-venv \ - jq \ - && apt-get clean - -# Install Node.js (as done in GitHub runners) -RUN curl -fsSL https://deb.nodesource.com/setup_16.x | bash - && \ - apt-get install -y nodejs - -# Install GitHub Actions CLI -RUN curl -fsSL https://cli.github.com/packages/githubcli-archive-keyring.gpg | dd of=/usr/share/keyrings/githubcli-archive-keyring.gpg && \ - chmod go+r /usr/share/keyrings/githubcli-archive-keyring.gpg && \ - echo "deb [signed-by=/usr/share/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" | tee /etc/apt/sources.list.d/github-cli.list > /dev/null && \ - apt-get update && apt-get install -y gh - -# Set default command -CMD [ "bash" ] - diff --git a/docs/technical_reference_guide/files/refs.bib b/docs/technical_reference_guide/files/refs.bib index 9d925f7218..bfc335faa7 100644 --- a/docs/technical_reference_guide/files/refs.bib +++ b/docs/technical_reference_guide/files/refs.bib @@ -17,10 +17,56 @@ @article{example publisher = {Publisher} } ` +@article{Winkler2020, + title = {Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S.~energy usage}, + author = {Jon Winkler and Saptarshi Das and Lieko Earle and Lena Burkett and Joseph Robertson and David Roberts and Charles Booten}, + journal = {Applied Energy}, + volume = {278}, + pages = {115533}, + year = {2020}, + publisher = {Elsevier}, + doi = {https://doi.org/10.1016/j.apenergy.2020.115533}, +} + + +@techreport{Nettleton2012, +author = {Nettleton, G and Edwards, J}, +institution = {{NorthernSTAR Building America Partnership, Building Technologies Program, U.S. Department of Energy}}, +title = {{Data Collection-Data Characterization Summary}}, +year = {2012} +} + +@techreport{Ritschard1992, +author = {Ritschard, R.L. and Hanford, J.W. and Sezgen, A.O.}, +institution = {Lawrence Berkeley Laboratory}, +pages = {LBL-30377}, +title = {{Single family heating and cooling requirements: Assumptions, methods, and summary results}}, +url = {https://www.osti.gov/biblio/7243112}, +year = {1992} +} + + +@techreport{Gagnon2021, +author = {Gagnon, P. and Frazier, W. and Cole, W. and Hale, E.}, +institution = {National Renewable Energy Laboratory}, +pages = {NREL/TP--6A40--81611}, +title = {{Cambium Documentation: Version 2021}}, +url = {https://www.nrel.gov/docs/fy22osti/81611.pdf}, +year = {2021} +} + +@techreport{Brown2019, +author = {Brown, M. and Cole, W. and Eurek, K. and Becker, J. and Bielen, D. and Chernyakhovskiy, I. and Cohen, S.}, +institution = {National Renewable Energy Laboratory}, +pages = {NREL/TP--6A20--74111}, +title = {{Regional Energy Deployment System (ReEDS) Model Documentation: Version 2019}}, +url = {https://www.nrel.gov/docs/fy20osti/74111.pdf}, +year = {2020} +} + @techreport{Wilson2014, author = {Wilson, E. and Engebrecht Metzger, C. and Horowitz, S. and Hendron, R.}, institution = {National Renewable Energy Laboratory}, -number = {March}, pages = {NREL/TP--5500--60988}, title = {{2014 Building America House Simulation Protocols}}, url = {https://www.nrel.gov/docs/fy14osti/60988.pdf}, @@ -46,7 +92,6 @@ @techreport{Wilson2017 institution = {National Renewable Energy Laboratory}, keywords = {January 2017,NREL/TP-5500-65667,building stock,buildings,economic potential,end use,energy efficiency,housing,modeling,residential,simulation,technical potential}, mendeley-groups = {NSF Spatial}, -number = {January}, pages = {NREL/TP--5500--65667}, title = {{Electric End-Use Energy Efficiency Potential in the U.S. Single-Family Housing Stock}}, url = {https://www.nrel.gov/docs/fy17osti/65667.pdf}, @@ -316,7 +361,7 @@ @techreport{LBNLTTS2019 } @techreport{WoodsMackenzie2020, -institution = {Woods Mackenzie}, +author = {{Woods Mackenzie}}, title = {{Solar PV module technology market report 2020}}, url = {https://www.woodmac.com/reports/power-markets-solar-pv-module-technology-market-report-2020-443274/}, year = {2020} diff --git a/docs/technical_reference_guide/images/Figure 1.pdf b/docs/technical_reference_guide/images/Figure 1.pdf new file mode 100644 index 0000000000..d42f91adf7 Binary files /dev/null and b/docs/technical_reference_guide/images/Figure 1.pdf differ diff --git a/docs/technical_reference_guide/images/Figure 4.pdf b/docs/technical_reference_guide/images/Figure 4.pdf new file mode 100644 index 0000000000..7dd80d2923 Binary files /dev/null and b/docs/technical_reference_guide/images/Figure 4.pdf differ