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Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task.
Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
Earlier work this paper cites.
The time series approach to short term load forecasting
Martin T Hagan and Suzanne M Behr · 1987
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser · 1989
Earlier work this paper cites.
Finding structure in time
Jeffrey L. Elman · 1990
Earlier work this paper cites.
Backpropagation through time: What it does and how to do it
Paul J. Werbos · 1990
Earlier work this paper cites.
Electric load forecasting using an artificial neural network
D. C. Park, M. A. El-Sharkawi, R. J. Marks, L. E. Atlas, and M. J. Damborg · 1991
Earlier work this paper cites.
Short-term load forecasting using an artificial neural network
K. Y. Lee, Y. T. Cha, and J. H. Park · 1992
Earlier work this paper cites.
Weather sensitive short-term load forecasting using nonfully connected artificial neural network
S.T. Chen, D.C. Yu, and A.R. Moghaddamjo · 1992
Earlier work this paper cites.
Learning complex, extended sequences using the principle of history compression
Jürgen Schmidhuber · 1992
Earlier work this paper cites.
A neural network short-term load forecaster
Dipti Srinivasan, A.C. Liew, and C.S. Chang · 1994
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Y. Bengio, P. Simard, and P. Frasconi · 1994
Earlier work this paper cites.
Analysis of an adaptive time-series autoregressive moving-average (arma) model for short-term load forecasting
Jiann-Fuh Chen, Wei-Ming Wang, and Chao-Ming Huang · 1995
Earlier work this paper cites.
Identification of armax model for short term load forecasting: An evolutionary programming approach
Hong-Tzer Yang, Chao-Ming Huang, and Ching-Lien Huang · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
An efficient gradient-based algorithm for on-line training of recurrent network trajectories
Ronald J. Williams and Jing Peng · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann Lecun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Short-term load forecasting with local ann predictors
I. Drezga and S. Rahman · 1999
Earlier work this paper cites.
Neural networks for short-term load forecasting: a review and evaluation
H. S. Hippert, C. E. Pedreira, and R. C. Souza · 2001
Earlier work this paper cites.
Short-term load forecasting via arma model identification including non-gaussian process considerations
Shyh-Jier Huang and Kuang-Rong Shih · 2003
Earlier work this paper cites.
A particle swarm optimization to identifying the armax model for short-term load forecasting
Chao-Ming Huang, Chi-Jen Huang, and Ming-Li Wang · 2005
Earlier work this paper cites.
Time series prediction using dirrec strategy
Antti Sorjamaa and Amaury Lendasse · 2006
Earlier work this paper cites.
Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
Earlier work this paper cites.
Long term time series prediction with multi-input multi-output local learning
Gianluca Bontempi · 2008
Earlier work this paper cites.
Long-term prediction of time series by combining direct and mimo strategies
Souhaib Ben Taieb, Gianluca Bontempi, Antti Sorjamaa, and Amaury Lendasse · 2009
Earlier work this paper cites.
Building-level occupancy data to improve arima-based electricity use forecasts
Guy R Newsham and Benjamin J Birt · 2010
Earlier work this paper cites.
Electricity short term load forecasting using elman recurrent neural network
Siddarameshwara Nayaka, Anup Yelamali, and Kshitiz Byahatti · 2010
Earlier work this paper cites.
A methodology for electric power load forecasting
Eisa Almeshaiei and Hassan Soltan · 2011
Earlier work this paper cites.
Using recurrent artificial neural networks to forecast household electricity consumption
Antonino Marvuglia and Antonio Messineo · 2011
Earlier work this paper cites.
Smart grid — the new and improved power grid: A survey
X. Fang, S. Misra, G. Xue, and D. Yang · 2012
Earlier work this paper cites.
Adadelta: An adaptive learning rate method
Matthew D. Zeiler · 2012
Earlier work this paper cites.
A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition
Souhaib Ben Taieb, Gianluca Bontempi, Amir F. Atiya, and Antti Sorjamaa · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
Cited alongside, same era.
How to construct deep recurrent neural networks
Razvan Pascanu, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio · 2013
Cited alongside, same era.
Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey E. Hinton · 2013
Cited alongside, same era.
Training and analysing deep recurrent neural networks
Michiel Hermans and Benjamin Schrauwen · 2013
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, ukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, and Jeffrey Dean · 2016
Later among the works it cites.
End-to-end attention-based large vocabulary speech recognition
D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio · 2016
Later among the works it cites.
Professor forcing: A new algorithm for training recurrent networks
Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron C. Courville, and Yoshua Bengio · 2016
Later among the works it cites.
Wavenet: A generative model for raw audio
Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alexander Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
Later among the works it cites.
A guide to convolution arithmetic for deep learning
Vincent Dumoulin and Francesco Visin · 2016
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A. Graves, A. Mohamed, and G. Hinton · 2013
Cited alongside, same era.
Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods
A. Marinescu, C. Harris, I. Dusparic, S. Clarke, and V. Cahill · 2013
Cited alongside, same era.
Global energy forecasting competition 2012
Tao Hong, Pierre Pinson, and Shu Fan · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Cited alongside, same era.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
Cited alongside, same era.
Later among the works it cites.
Sihan Li, Jiantao Jiao, Yanjun Han, and Tsachy Weissman · 2016
Later among the works it cites.
Deep neural networks for energy load forecasting
K. Amarasinghe, D. L. Marino, and M. Manic · 2017
Later among the works it cites.
An overview and comparative analysis of recurrent neural networks for short term load forecasting
Filippo Maria Bianchi, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, and Robert Jenssen · 2017
Later among the works it cites.
Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network
Jian Zheng, Cencen Xu, Ziang Zhang, and Xiaohua Li · 2017
Later among the works it cites.
Short-term residential load forecasting based on lstm recurrent neural network
Weicong Kong, Zhao Yang Dong, Youwei Jia, David J Hill, Yan Xu, and Yuan Zhang · 2017
Later among the works it cites.
Load forecasting via deep neural networks
Wan He · 2017
Later among the works it cites.
UCI machine learning repository, 2017
Dua Dheeru and Efi Karra Taniskidou · 2017
Later among the works it cites.
Powerlstm: Power demand forecasting using long short-term memory neural network
Yao Cheng, Chang Xu, Daisuke Mashima, Vrizlynn L. L. Thing, and Yongdong Wu · 2017
Later among the works it cites.
http://traces.cs.umass.edu/index.php/Smart/Smart , 2017
Umass smart dataset · 2017
Later among the works it cites.
A review of deep learning methods applied on load forecasting
A. Almalaq and G. Edwards · 2017
Later among the works it cites.
Lstm: A search space odyssey
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber · 2017
Later among the works it cites.
Comparative study of cnn and rnn for natural language processing
Wenpeng Yin, Katharina Kann, Mo Yu, and Hinrich Schütze · 2017
Later among the works it cites.
Pixel recursive super resolution
Ryan Dahl, Mohammad Norouzi, and Jonathon Shlens · 2017
Later among the works it cites.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
Later among the works it cites.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin · 2017
Later among the works it cites.
Conditional time series forecasting with convolutional neural networks
Anastasia Borovykh, Sander Bohte, and Kees Oosterlee · 2017
Later among the works it cites.
Short-term load forecasting with deep residual networks
K. Chen, K. Chen, Q. Wang, Z. He, J. Hu, and J. He · 2018
Later among the works it cites.
A high precision artificial neural networks model for short-term energy load forecasting
Ping-Huan Kuo and Chiou-Jye Huang · 2018
Later among the works it cites.
Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches
Salah Bouktif, Ali Fiaz, Ali Ouni, and Mohamed Serhani · 2018
Later among the works it cites.
Short-term load forecasting with multi-source data using gated recurrent unit neural networks
Yixing Wang, Meiqin Liu, Zhejing Bao, and Senlin Zhang · 2018
Later among the works it cites.
A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network
Chujie Tian, Jian Ma, Chunhong Zhang, and Panpan Zhan · 2018
Later among the works it cites.
Combining auto-regression with exogenous variables in sequence-to-sequence recurrent neural networks for short-term load forecasting
Henning Wilms, Marco Cupelli, and Antonello Monti · 2018
Later among the works it cites.
Deep reinforcement learning for sequence to sequence models
Yaser Keneshloo, Tian Shi, Naren Ramakrishnan, and Chandan K. Reddy · 2018
Later among the works it cites.
Combining auto-regression with exogenous variables in sequence-to-sequence recurrent neural networks for short-term load forecasting
Henning Wilms, Marco Cupelli, and A Monti · 2018
Later among the works it cites.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
Later among the works it cites.