aupec.bib

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@Article{Zheng2017,
  author    = {Huiting Zheng and Jiabin Yuan and Long Chen},
  title     = {Short-Term Load Forecasting Using {EMD}-{LSTM} Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation},
  journal   = {Energies},
  year      = {2017},
  volume    = {10},
  number    = {8},
  month     = {aug},
  pages     = {1168},
  doi       = {10.3390/en10081168},
  abstract  = {Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.},
  comment   = {This method uses a S2S LSTM RNN for load forecasting.
K-means clustering with xgboost is used to select a similar day for input to the network.
Intrinsic mode decomposition is used to split the forecast into seperate nerual networks which are then recombined at the end.
Authors claim it works far better than plain LSTM ( such as implemented by Marino)},
  file      = {:papers/Short-Term Load Forecasting Using EMD-LSTM.pdf:PDF},
  publisher = {{MDPI} {AG}},
}

@Article{Marino2016,
  author      = {Daniel L. Marino and Kasun Amarasinghe and Milos Manic},
  title       = {Building Energy Load Forecasting using Deep Neural Networks},
  journal     = {CoRR},
  year        = {2016},
  date        = {2016-10-29},
  eprint      = {1610.09460v1},
  eprintclass = {cs.NE},
  eprinttype  = {arXiv},
  abstract    = {Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past, however has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically Long Short Term Memory (LSTM) algorithms. The presented work investigates two variants of the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures where trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.},
  file        = {:papers/Building Energy Load Forecasting using Deep Neural.pdf:PDF},
  keywords    = {cs.NE},
}

@Article{Bianchi2017,
  author        = {Filippo Maria Bianchi and Enrico Maiorino and Michael C. Kampffmeyer and Antonello Rizzi and Robert Jenssen},
  title         = {An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting},
  journal       = {CoRR},
  year          = {2017},
  volume        = {abs/1705.04378},
  eprint        = {1705.04378},
  url           = {http://arxiv.org/abs/1705.04378},
  abstract      = {The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementation of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. In this paper we perform a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. We test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. We provide a general overview of the most important architectures and we define guidelines for configuring the recurrent networks to predict real-valued time series.},
  archiveprefix = {arXiv},
  bibsource     = {dblp computer science bibliography, https://dblp.org},
  biburl        = {https://dblp.org/rec/bib/journals/corr/BianchiMKRJ17},
  comment       = {An overview of the use of Elman RNN, LSTM, GRU, NARX, and Echo state RNNs for load forecasting.
Found some reasonable variance between all architectures.
Does not consider S2S. 

Also gives a very nice overview of RNNs in general. },
  file          = {:papers/An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting.pdf:PDF},
  timestamp     = {Wed, 07 Jun 2017 14:43:10 +0200},
}

@Article{Flunkert2017,
  author      = {Valentin Flunkert and David Salinas and Jan Gasthaus},
  title       = {DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks},
  journal     = {CoRR},
  date        = {2017-04-13},
  eprint      = {1704.04110v2},
  eprintclass = {cs.AI},
  eprinttype  = {arXiv},
  abstract    = {A key enabler for optimizing business processes is accurately estimating the probability distribution of a time series future given its past. Such probabilistic forecasts are crucial for example for reducing excess inventory in supply chains. In this paper we propose DeepAR, a novel methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We show through extensive empirical evaluation on several real-world forecasting data sets that our methodology is more accurate than state-of-the-art models, while requiring minimal feature engineering.},
  file        = {:papers/DeepAR Forecasting Recurrent Networks.pdf:PDF},
  keywords    = {cs.AI, cs.LG, stat.ML},
}

@InProceedings{Scott2014,
  author    = {Scott, Paul and Thi\'ebaux, Sylvie},
  title     = {Distributed {Multi-Period} Optimal Power Flow for Demand Response in Microgrids},
  booktitle = {{ACM} {e-Energy}},
  year      = {2015},
  month     = {jul},
  url       = {http://users.cecs.anu.edu.au/~pscott/extras/papers/scott2015.pdf},
  address   = {Bangalore India},
  file      = {:papers/Dynamic Optimal Power Flow in Microgrids using the Alternating Direction Method of Multipliers.pdf:PDF},
}

@Article{Evan2016,
  author  = {Evan Franklin},
  title   = {Agents of change Making batteries go the extra mile},
  journal = {ReNew, no. 136, pp. 56-58,},
  year    = {2016},
  file    = {:papers/agents-of-change-bruny.pdf:PDF},
}

@Report{Jacobs2017,
  author      = {Walter Gerardi and Damien O’Connor},
  title       = {Projections of uptake of small-scale systems},
  type        = {resreport},
  institution = {AEMO},
  year        = {2017},
  date        = {2017-06-09},
  url         = {https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/Electricity-Forecasting-Insights/2017-Electricity-Forecasting-Insights/Key-component-consumption-forecasts/PV-and-storage},
  file        = {:papers/Projections of Uptake of Small-scale Systems.pdf:PDF},
}

@Report{AEMO2016,
  title       = {2016 NATIONAL ELECTRICITY FORECASTING REPORT},
  type        = {resreport},
  institution = {Australian Energy Market Operator},
  year        = {2016},
  url         = {https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/National-Electricity-Forecasting-Report},
  file        = {:papers/2016 National Electricity Forecasting Report NEFR.pdf:PDF},
}

@Article{Kong2018,
  author    = {Weicong Kong and Zhao Yang Dong and David J. Hill and Fengji Luo and Yan Xu},
  title     = {Short-Term Residential Load Forecasting Based on Resident Behaviour Learning},
  journal   = {{IEEE} Transactions on Power Systems},
  year      = {2018},
  volume    = {33},
  number    = {1},
  month     = {jan},
  pages     = {1087--1088},
  doi       = {10.1109/tpwrs.2017.2688178},
  file      = {:papers/kong2017.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Thesis{Lier2015,
  author      = {Ciarán Lier},
  title       = {Applying Machine Learning Techniques to Short Term Load Forecasting},
  type        = {mathesis},
  institution = {University of Groningen},
  year        = {2015},
  file        = {:papers/Thesis_Ciaran_Lier.pdf:PDF},
}

@Article{Basil1975,
  author    = {Victor R. Basil and Albert J. Turner},
  title     = {Iterative enhancement: A practical technique for software development},
  journal   = {{IEEE} Transactions on Software Engineering},
  year      = {1975},
  volume    = {{SE}-1},
  number    = {4},
  month     = {dec},
  pages     = {390--396},
  doi       = {10.1109/tse.1975.6312870},
  file      = {:papers/Iterative enhancement A practical technique for software development.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Book{Box1970,
  author    = {George E. P. Box and Gwilym M. Jenkins and Gregory C. Reinsel},
  title     = {Time Series Analysis},
  year      = {1970},
  publisher = {John Wiley {\&} Sons, Inc.},
  file      = {:papers/Time Series Analysis_ Forecasting and Control-Wiley (2015).pdf:PDF},
  month     = {jun},
}

@Book{Weron2006,
  author    = {Rafa{\l} Weron},
  title     = {Modeling and Forecasting Electricity Loads and Prices},
  year      = {2006},
  publisher = {John Wiley {\&} Sons Ltd},
  doi       = {10.1002/9781118673362},
  file      = {:papers/Rafal Weron-Modeling and Forecasting Electricity Loads and Prices_ A Statistical Approach (The Wiley Finance Series) (2006).pdf:PDF},
  month     = {dec},
}

@Article{Ceperic2013,
  author    = {Ervin Ceperic and Vladimir Ceperic and Adrijan Baric},
  title     = {A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines},
  journal   = {{IEEE} Transactions on Power Systems},
  year      = {2013},
  volume    = {28},
  number    = {4},
  month     = {nov},
  pages     = {4356--4364},
  doi       = {10.1109/tpwrs.2013.2269803},
  file      = {:papers/A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@InProceedings{Drucker1996,
  author    = {Drucker, Harris and Burges, Chris J. C. and Kaufman, Linda and Smola, Alex and Vapnik, Vladimir},
  title     = {Support Vector Regression Machines},
  booktitle = {Proceedings of the 9th International Conference on Neural Information Processing Systems},
  year      = {1996},
  series    = {NIPS'96},
  publisher = {MIT Press},
  location  = {Denver, Colorado},
  pages     = {155--161},
  url       = {http://dl.acm.org/citation.cfm?id=2998981.2999003},
  acmid     = {2999003},
  address   = {Cambridge, MA, USA},
  file      = {:papers/Support Vector Regression Machines.pdf:PDF},
  numpages  = {7},
}

@Article{Chen2004,
  author    = {B.-J. Chen and M.-W. Chang and C.-J. Lin},
  title     = {Load Forecasting Using Support Vector Machines: A Study on {EUNITE} Competition 2001},
  journal   = {{IEEE} Transactions on Power Systems},
  year      = {2004},
  volume    = {19},
  number    = {4},
  month     = {nov},
  pages     = {1821--1830},
  doi       = {10.1109/tpwrs.2004.835679},
  file      = {:papers/Load Forecasting Using Support Vector Machines A Study on EUNITE Competition 2001.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Article{Smola2004,
  author    = {Alex J. Smola and Bernhard Schölkopf},
  title     = {A tutorial on support vector regression},
  journal   = {Statistics and Computing},
  year      = {2004},
  volume    = {14},
  number    = {3},
  month     = {aug},
  pages     = {199--222},
  doi       = {10.1023/b:stco.0000035301.49549.88},
  file      = {:papers/A tutorial on support vector regression.pdf:PDF},
  publisher = {Springer Nature},
}

@Article{Desouky2000,
  author    = {A.A. El Desouky and M.M. El Kateb},
  title     = {Hybrid adaptive techniques for electric-load forecast using {ANN} and {ARIMA}},
  journal   = {{IEE} Proceedings - Generation, Transmission and Distribution},
  year      = {2000},
  volume    = {147},
  number    = {4},
  pages     = {213},
  doi       = {10.1049/ip-gtd:20000521},
  file      = {:papers/Hybrid adaptive techniques for electric-load forecast using ANN and ARIMA.pdf:PDF},
  publisher = {Institution of Engineering and Technology ({IET})},
}

@Article{Bennett2014,
  author    = {Christopher J. Bennett and Rodney A. Stewart and Jun Wei Lu},
  title     = {Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system},
  journal   = {Energy},
  year      = {2014},
  volume    = {67},
  month     = {apr},
  pages     = {200--212},
  doi       = {10.1016/j.energy.2014.01.032},
  file      = {:papers/Bennett2014.pdf:PDF},
  publisher = {Elsevier {BV}},
}

@InProceedings{Karthika2017,
  author    = {S Karthika and Vijaya Margaret and K. Balaraman},
  title     = {Hybrid short term load forecasting using {ARIMA}-{SVM}},
  booktitle = {2017 Innovations in Power and Advanced Computing Technologies (i-{PACT})},
  year      = {2017},
  publisher = {{IEEE}},
  month     = {apr},
  doi       = {10.1109/ipact.2017.8245060},
  file      = {:papers/Hybrid Short Term Load Forecasting using ARIMA-SVM.pdf:PDF},
}

@Article{RochaReis2005,
  author    = {A.J. RochaReis and A.P. AlvesdaSilva},
  title     = {Feature Extraction via Multiresolution Analysis for Short-Term Load Forecasting},
  journal   = {{IEEE} Transactions on Power Systems},
  year      = {2005},
  volume    = {20},
  number    = {1},
  month     = {feb},
  pages     = {189--198},
  doi       = {10.1109/tpwrs.2004.840380},
  file      = {:papers/Feature extraction via multiresolution analysis.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Article{AMJADY2009,
  author    = {N AMJADY and F KEYNIA},
  title     = {Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm},
  journal   = {Energy},
  year      = {2009},
  volume    = {34},
  number    = {1},
  month     = {jan},
  pages     = {46--57},
  doi       = {10.1016/j.energy.2008.09.020},
  file      = {:papers/Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm.pdf:PDF},
  publisher = {Elsevier {BV}},
}

@Article{Deihimi2012,
  author    = {Ali Deihimi and Hemen Showkati},
  title     = {Application of echo state networks in short-term electric load forecasting},
  journal   = {Energy},
  year      = {2012},
  volume    = {39},
  number    = {1},
  month     = {mar},
  pages     = {327--340},
  doi       = {10.1016/j.energy.2012.01.007},
  file      = {:papers/Application of echo state networks in short-term electric load forecasting.pdf:PDF},
  publisher = {Elsevier {BV}},
}

@Article{Elattar2010,
  author    = {Ehab E Elattar and John Goulermas and Q H Wu},
  title     = {Electric Load Forecasting Based on Locally Weighted Support Vector Regression},
  journal   = {{IEEE} Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)},
  year      = {2010},
  volume    = {40},
  number    = {4},
  month     = {jul},
  pages     = {438--447},
  doi       = {10.1109/tsmcc.2010.2040176},
  file      = {:papers/Electric Load Forecasting Based on Locally Weighted Support Vector Regression.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Book{Goodfellow-et-al-2016,
  author    = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
  title     = {Deep Learning},
  year      = {2016},
  note      = {\url{http://www.deeplearningbook.org}},
  publisher = {MIT Press},
  file      = {:papers/deeplearningbook.pdf:PDF},
}

@Book{negnevitsky2005artificial,
  author    = {Negnevitsky, Michael},
  title     = {Artificial intelligence: a guide to intelligent systems},
  year      = {2005},
  publisher = {Pearson Education},
  file      = {:papers/Artificial_Intelligence-A_Guide_to_Intelligent_Systems.pdf:PDF},
}

@InCollection{Bottou2011,
  author    = {L{\'{e}}on Bottou},
  title     = {Large-Scale Machine Learning with Stochastic Gradient Descent},
  booktitle = {Chapman {\&} Hall/{CRC} Computer Science {\&} Data Analysis},
  year      = {2011},
  publisher = {Chapman and Hall/{CRC}},
  pages     = {17--25},
  doi       = {10.1201/b11429-4},
  file      = {:papers/Large-Scale Machine Learning with Stochastic Gradient Descent.pdf:PDF},
  month     = {dec},
}

@Article{Zainal-Mokhtar2013,
  author    = {Khursiah Zainal-Mokhtar and Junita Mohamad-Saleh},
  title     = {An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data},
  journal   = {Sensors},
  year      = {2013},
  volume    = {13},
  number    = {9},
  month     = {aug},
  pages     = {11385--11406},
  doi       = {10.3390/s130911385},
  file      = {:papers/An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data.pdf:PDF;:images/A-schematic-diagram-of-a-Multi-Layer-Perceptron-MLP-neural-network.png:PNG image},
  publisher = {{MDPI} {AG}},
}

@WWW{Deshpande2017,
  author = {Mohit Deshpande},
  editor = {Perceptrons: The First Neural Networks},
  title  = {Perceptrons: The First Neural Networks},
  year   = {2017},
  date   = {2017-09-12},
  url    = {https://pythonmachinelearning.pro/perceptrons-the-first-neural-networks/},
  file   = {:images/Single-Perceptron-825x459.png:PNG image},
}

@Article{2017arXiv171009829S,
  author        = {{Sabour}, S. and {Frosst}, N. and {E Hinton}, G.},
  title         = {{Dynamic Routing Between Capsules}},
  journal       = {ArXiv e-prints},
  year          = {2017},
  month         = oct,
  eprint        = {1710.09829},
  adsnote       = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl        = {http://adsabs.harvard.edu/abs/2017arXiv171009829S},
  archiveprefix = {arXiv},
  file          = {:papers/Dynamic Routing Between Capsules.pdf:PDF},
  keywords      = {Computer Science - Computer Vision and Pattern Recognition},
  primaryclass  = {cs.CV},
}

@incollection{Vaswani2017,
	title = {Attention is All you Need},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, \L ukasz and Polosukhin, Illia},
	booktitle = {Advances in Neural Information Processing Systems 30},
	editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
	pages = {5998--6008},
	year = {2017},
	publisher = {Curran Associates, Inc.},
	url = {http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf},
  file        = {:papers/Attention Is All You Need.pdf:PDF},
}


@Article{Chiu2017,
  author      = {Chung-Cheng Chiu and Tara N. Sainath and Yonghui Wu and Rohit Prabhavalkar and Patrick Nguyen and Zhifeng Chen and Anjuli Kannan and Ron J. Weiss and Kanishka Rao and Ekaterina Gonina and Navdeep Jaitly and Bo Li and Jan Chorowski and Michiel Bacchiani},
  title       = {State-of-the-art Speech Recognition With Sequence-to-Sequence Models},
  year        = {2017},
  date        = {2017-12-05},
  eprint      = {1712.01769v6},
  eprintclass = {cs.CL},
  eprinttype  = {arXiv},
  abstract    = {Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to state-of-theart ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We also introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12, 500 hour voice search task, we find that the proposed changes improve the WER from 9.2% to 5.6%, while the best conventional system achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to 5% for the conventional system.},
  file        = {:papers/STATE-OF-THE-ART SPEECH RECOGNITION WITH SEQUENCE-TO-SEQUENCE MODELS.pdf:PDF},
  keywords    = {cs.CL, cs.SD, eess.AS, stat.ML},
}

@Article{DBLP:journals/corr/OordDZSVGKSK16,
  author        = {A{\"{a}}ron van den Oord and Sander Dieleman and Heiga Zen and Karen Simonyan and Oriol Vinyals and Alex Graves and Nal Kalchbrenner and Andrew W. Senior and Koray Kavukcuoglu},
  title         = {WaveNet: {A} Generative Model for Raw Audio},
  journal       = {CoRR},
  year          = {2016},
  volume        = {abs/1609.03499},
  eprint        = {1609.03499},
  url           = {http://arxiv.org/abs/1609.03499},
  archiveprefix = {arXiv},
  bibsource     = {dblp computer science bibliography, https://dblp.org},
  biburl        = {https://dblp.org/rec/bib/journals/corr/OordDZSVGKSK16},
  file          = {:papers/WAVENET A GENERATIVE MODEL FOR RAW AUDIO.pdf:PDF},
  timestamp     = {Wed, 07 Jun 2017 14:42:54 +0200},
}

@Article{Mandal2010,
  author    = {Paras Mandal and Anurag K. Srivastava and Tomonobu Senjyu and Michael Negnevitsky},
  title     = {A new recursive neural network algorithm to forecast electricity price for {PJM} day-ahead market},
  journal   = {International Journal of Energy Research},
  year      = {2010},
  volume    = {34},
  number    = {6},
  month     = {may},
  pages     = {507--522},
  doi       = {10.1002/er.1569},
  file      = {:papers/A new recursive neural network algorithm to forecast electricity price for PJM day-ahead market.pdf:PDF},
  publisher = {Wiley},
}

@Article{Zhao2017,
  author    = {Zheng Zhao and Weihai Chen and Xingming Wu and Peter C. Y. Chen and Jingmeng Liu},
  title     = {{LSTM} network: a deep learning approach for short-term traffic forecast},
  journal   = {{IET} Intelligent Transport Systems},
  year      = {2017},
  volume    = {11},
  number    = {2},
  month     = {mar},
  pages     = {68--75},
  doi       = {10.1049/iet-its.2016.0208},
  file      = {:papers/LSTM network a deep learning approach for short-term traffic forecast.pdf:PDF},
  publisher = {Institution of Engineering and Technology ({IET})},
}

@Article{Chen2010,
  author    = {Ying Chen and P.B. Luh and Che Guan and Yige Zhao and L.D. Michel and M.A. Coolbeth and P.B. Friedland and S.J. Rourke},
  title     = {Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks},
  journal   = {{IEEE} Transactions on Power Systems},
  year      = {2010},
  volume    = {25},
  number    = {1},
  month     = {feb},
  pages     = {322--330},
  doi       = {10.1109/tpwrs.2009.2030426},
  file      = {:papers/Short-Term Load Forecasting Similar Day-Based Wavelet Neural Networks.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Article{Kong2017,
  author    = {Weicong Kong and Zhao Yang Dong and Youwei Jia and David J. Hill and Yan Xu and Yuan Zhang},
  title     = {Short-Term Residential Load Forecasting based on {LSTM} Recurrent Neural Network},
  journal   = {{IEEE} Transactions on Smart Grid},
  year      = {2017},
  pages     = {1--1},
  doi       = {10.1109/tsg.2017.2753802},
  file      = {:papers/Short-Term Residential Load Forecasting based on {LSTM} Recurrent Neural Network.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@InProceedings{Cho2014,
  author    = {Kyunghyun Cho and Bart van Merrienboer and Dzmitry Bahdanau and Yoshua Bengio},
  title     = {On the Properties of Neural Machine Translation: Encoder{\textendash}Decoder Approaches},
  booktitle = {Proceedings of {SSST}-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation},
  year      = {2014},
  publisher = {Association for Computational Linguistics},
  doi       = {10.3115/v1/w14-4012},
  file      = {:papers/On the Properties of Neural Machine Translation Encoder–Decoder Approaches.pdf:PDF},
}

@Article{Cho2014a,
  author      = {Kyunghyun Cho and Bart van Merrienboer and Caglar Gulcehre and Dzmitry Bahdanau and Fethi Bougares and Holger Schwenk and Yoshua Bengio},
  title       = {Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation},
  year        = {2014},
  date        = {2014-06-03},
  eprint      = {1406.1078v3},
  eprintclass = {cs.CL},
  eprinttype  = {arXiv},
  abstract    = {In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.},
  file        = {:papers/Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation.pdf:PDF},
  keywords    = {cs.CL, cs.LG, cs.NE, stat.ML},
}

@Article{hochreiter1997long,
  author    = {Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  title     = {Long short-term memory},
  journal   = {Neural computation},
  year      = {1997},
  volume    = {9},
  number    = {8},
  pages     = {1735--1780},
  file      = {:papers/LONG SHORT-TERM MEMORY.pdf:PDF},
  publisher = {MIT Press},
}

@Article{luong2015effective,
  author  = {Luong, Minh-Thang and Pham, Hieu and Manning, Christopher D},
  title   = {Effective approaches to attention-based neural machine translation},
  journal = {arXiv preprint arXiv:1508.04025},
  year    = {2015},
  file    = {:papers/Effective Approaches to Attention-based Neural Machine Translation.pdf:PDF},
}

@paper{Song2017,
	author = {Huan Song and Deepta Rajan and Jayaraman Thiagarajan and Andreas Spanias},
	title = {Attend and Diagnose: Clinical Time Series Analysis Using Attention Models},
	conference = {AAAI Conference on Artificial Intelligence},
	year = {2018},
	keywords = {Attention model; Recurrent Neural Network; Time Series Analysis; Deep Learning; Clinical; Healthcare},
	abstract = {With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data. Powered by Recurrent Neural Network (RNN) architectures with Long Short-Term Memory (LSTM) units, deep neural networks have achieved state-of-the-art results in several clinical prediction tasks. Despite the success of RNN, its sequential nature prohibits parallelized computing, thus making it inefficient particularly when processing long sequences. Recently, architectures which are based solely on attention mechanisms have shown remarkable success in transduction tasks in NLP, while being computationally superior. In this paper, for the first time, we utilize attention models for clinical time-series modeling, thereby dispensing recurrence entirely. We develop the SAnD (Simply Attend and Diagnose) architecture, which employs a masked, self-attention mechanism, and uses positional encoding and dense interpolation strategies for incorporating temporal order. Furthermore, we develop a multi-task variant of SAnD to jointly infer models with multiple diagnosis tasks. Using the recent MIMIC-III benchmark datasets, we demonstrate that the proposed approach achieves state-of-the-art performance in all tasks, outperforming LSTM models and classical baselines with hand-engineered features.},
	
	url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16325},
	file        = {:papers/Attend and Diagnose Clinical Time Series Analysis using Attention Models.pdf:PDF},
}

@Article{Hippert2001,
  author    = {H.S. Hippert and C.E. Pedreira and R.C. Souza},
  title     = {Neural networks for short-term load forecasting: a review and evaluation},
  journal   = {{IEEE} Transactions on Power Systems},
  year      = {2001},
  volume    = {16},
  number    = {1},
  pages     = {44--55},
  doi       = {10.1109/59.910780},
  file      = {:papers/Neural Networks for Short-Term Load Forecasting A Review and Evaluation.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Article{AEMO2017,
  author = {AEMO},
  title  = {FORECAST ACCURACY REPORT 2017},
  year   = {2017},
  date   = {2017-11-01},
}

@Article{Rahman1993,
  author    = {S. Rahman and O. Hazim},
  title     = {A generalized knowledge-based short-term load-forecasting technique},
  journal   = {{IEEE} Transactions on Power Systems},
  year      = {1993},
  volume    = {8},
  number    = {2},
  month     = {may},
  pages     = {508--514},
  doi       = {10.1109/59.260833},
  file      = {:papers/A Generalized Knowledge-Based Short-Term Load-Forecasting Technique.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Article{Senjyu1998,
  author    = {T. Senjyu and S. Higa and K. Uezato},
  title     = {Future load curve shaping based on similarity using fuzzy logic approach},
  journal   = {{IEE} Proceedings - Generation, Transmission and Distribution},
  year      = {1998},
  volume    = {145},
  number    = {4},
  pages     = {375},
  doi       = {10.1049/ip-gtd:19981998},
  file      = {:papers/Future load curve shaping based on simi arity using fuzzy logic approach.pdf:PDF},
  publisher = {Institution of Engineering and Technology ({IET})},
}

@Article{Mansouri2014,
  author        = {Mansouri, Vahid and akbari, Mohammad Esmaeil},
  title         = {Neural Networks in Electric Load Forecasting:A Comprehensive Survey},
  journal       = {Journal of Artificial Intelligence in Electrical Engineering},
  year          = {2014},
  volume        = {3},
  number        = {10},
  pages         = {37-50},
  issn          = {2345-4652},
  url           = {http://jaiee.iau-ahar.ac.ir/article_514436.html},
  __markedentry = {[michael:]},
  abstract      = {Review and classification of electric load forecasting (LF) techniques based on artificial neuralnetworks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANNoriented applications for forecasting are given in the literature. These are classified into five groups:(1) ANNs in short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term LF, (4) Hybrid ANNs inLF, (5) ANNs in Special applications of LF. The major research articles for each category are brieflydescribed and the related literature reviewed. Conclusions are made on future research directions.},
  file          = {:papers/Neural Networks in Electric Load Forecasting A Comprehensive Survey.pdf:PDF},
}

@WWW{CIGRE2016,
  author = {CIGRE},
  title  = {CIGRE Working Group concludes Demand forecasting study},
  year   = {2016},
  date   = {2016-12-19},
  url    = {https://www.cigreaustralia.org.au/news/features/cigre-working-group-concludes-demand-forecasting-study/},
}

@Article{Schuster1997,
  author    = {M. Schuster and K.K. Paliwal},
  title     = {Bidirectional recurrent neural networks},
  journal   = {{IEEE} Transactions on Signal Processing},
  year      = {1997},
  volume    = {45},
  number    = {11},
  pages     = {2673--2681},
  doi       = {10.1109/78.650093},
  file      = {:papers/Bidirectional Recurrent Neural Networks.pdf:PDF},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
}

@Article{BahdanauCB14,
  author        = {Dzmitry Bahdanau and Kyunghyun Cho and Yoshua Bengio},
  title         = {Neural Machine Translation by Jointly Learning to Align and Translate},
  journal       = {CoRR},
  year          = {2014},
  volume        = {abs/1409.0473},
  eprint        = {1409.0473},
  url           = {http://arxiv.org/abs/1409.0473},
  archiveprefix = {arXiv},
  bibsource     = {dblp computer science bibliography, https://dblp.org},
  biburl        = {https://dblp.org/rec/bib/journals/corr/BahdanauCB14},
  file          = {:papers/Neural Machine Translation by Jointly Learning to Align and Translate.pdf:PDF},
  timestamp     = {Wed, 07 Jun 2017 14:40:19 +0200},
}

@InProceedings{Haoyu2012,
  author    = {Wang Haoyu and Zhou Haili},
  title     = {Basic Design Principles in Software Engineering},
  booktitle = {2012 Fourth International Conference on Computational and Information Sciences},
  year      = {2012},
  publisher = {{IEEE}},
  month     = {aug},
  doi       = {10.1109/iccis.2012.91},
  file      = {:papers/Basic Design Principles in Software Engineering.pdf:PDF},
}

@Article{Parmar2018,
  author        = {Niki Parmar and Ashish Vaswani and Jakob Uszkoreit and Lukasz Kaiser and Noam Shazeer and Alexander Ku},
  title         = {Image Transformer},
  journal       = {CoRR},
  year          = {2018},
  volume        = {abs/1802.05751},
  eprint        = {1802.05751},
  url           = {http://arxiv.org/abs/1802.05751},
  archiveprefix = {arXiv},
  bibsource     = {dblp computer science bibliography, https://dblp.org},
  biburl        = {https://dblp.org/rec/bib/journals/corr/abs-1802-05751},
  file          = {:papers/image transformer.pdf:PDF},
  timestamp     = {Thu, 01 Mar 2018 19:20:48 +0100},
}

@Article{Ba2016,
  author  = {Ba, Jimmy Lei and Kiros, Jamie Ryan and Hinton, Geoffrey E},
  title   = {Layer normalization},
  journal = {arXiv preprint arXiv:1607.06450},
  year    = {2016},
  file    = {:papers/Layer Normalization.pdf:PDF},
}

@InProceedings{He2015,
  author    = {K. He and X. Zhang and S. Ren and J. Sun},
  title     = {Deep Residual Learning for Image Recognition},
  booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2016},
  month     = {June},
  pages     = {770-778},
  doi       = {10.1109/CVPR.2016.90},
  abstract  = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
  file      = {:papers/Deep Residual Learning for Image Recognition.pdf:PDF},
  issn      = {1063-6919},
  keywords  = {COCO segmentation;ImageNet localization;ILSVRC & COCO 2015 competitions;deep residual nets;COCO object detection dataset;visual recognition tasks;CIFAR-10;ILSVRC 2015 classification task;ImageNet test set;VGG nets;residual nets;ImageNet dataset;residual function learning;deeper neural network training;image recognition;deep residual learning;Training;Degradation;Complexity theory;Image recognition;Neural networks;Visualization;Image segmentation},
}

@Article{Kingma2014,
  author  = {Kingma, Diederik P and Ba, Jimmy},
  title   = {Adam: A method for stochastic optimization},
  journal = {arXiv preprint arXiv:1412.6980},
  year    = {2014},
  file    = {:papers/ADAM A METHOD FOR STOCHASTIC OPTIMIZATION.pdf:PDF},
}

@Online{census2016,
  author  = {{Australian Bureau of Statistics}},
  title   = {Statistical Areas Level 1 6106906 and 6106909},
  year    = {2016},
  url     = {http://www.abs.gov.au/websitedbs/D3310114.nsf/Home/2016%20search%20by%20geography},
  journal = {Census},
}

@Article{srivastava14a,
  author     = {Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
  title      = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
  journal    = {J. Mach. Learn. Res.},
  year       = {2014},
  volume     = {15},
  number     = {1},
  month      = jan,
  pages      = {1929--1958},
  issn       = {1532-4435},
  url        = {http://dl.acm.org/citation.cfm?id=2627435.2670313},
  acmid      = {2670313},
  file       = {:papers/Dropout A Simple Way to Prevent Neural Networks from Overfitting.pdf:PDF},
  issue_date = {January 2014},
  keywords   = {deep learning, model combination, neural networks, regularization},
  numpages   = {30},
  publisher  = {JMLR.org},
}

@Report{apvi2017,
  author      = {Warwick Johnston},
  title       = {National Survey Report of Photovoltaic Applications in AUSTRALIA 2017},
  institution = {Australian PV Institute},
  year        = {2017},
  date        = {2017},
  url         = {http://apvi.org.au/pv-in-australia-2017/},
  file        = {:papers/National Survey Report of Photovoltaic Applications in AUSTRALIA 2017.pdf:PDF},
}

@Report{bloomberg2018,
  author = {{Bloomberg New Energy Finance}},
  title  = {New Energy Outlook 2018},
  year   = {2018},
  date   = {2018},
  url    = {https://about.bnef.com/new-energy-outlook/},
}

@Comment{jabref-meta: databaseType:biblatex;}