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Cited by 2,645

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Kong, Weicong, Dong, Zhao Yang, Jia, Youwei, Hill, David J., Xu, Yan, Zhang, Yuan·2019·IEEE Transactions on Smart Grid
DOI 10.1109/tsg.2017.2753802
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Abstract

As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

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Research works printed but not linked: 36

  1. 10.1109/TASL.2011.2109382 · DOI · Paper page
  2. 10.1109/TPWRS.2010.2062542 · doi:10.1109/tpwrs.2010.2062542
  3. al-rfou, 2016, Theano A Python framework for fast computation of mathematical expressions
  4. chollet, 2015, Keras
  5. lipton, 2015, A critical review of recurrent neural networks for sequence learning
  6. 10.1162/089976600300015015 · DOI · Paper page
  7. hinton, 2012, RMSProp: Divide the gradient by a running average of its recent magnitude, COURSERA Neural Networks for Machine Learning, 4
  8. duchi, 2011, Adaptive subgradient methods for online learning and stochastic optimization, J Mach Learn Res, 12, 2121
  9. li, 2015, Short-term load forecasting based on the grid method and the time series fuzzy load forecasting method, Proc Int Conf Renewable Power Gener, 1
  10. 10.1109/ICICTA.2010.38 · doi:10.1109/icicta.2010.38
  11. 10.1049/iet-gtd.2012.0541 · DOI
  12. 10.1109/PESGM.2016.7741097 · DOI
  13. 10.1109/IJCNN.2013.6706742 · DOI
  14. 10.1016/j.energy.2015.01.028 · DOI
  15. 10.17775/CSEEJPES.2015.00036 · doi:10.17775/cseejpes.2015.00036
  16. 10.1109/TSG.2014.2364233 · DOI · Paper page
  17. stephen, 2017, Incorporating practice theory in sub-profile models for short term aggregated residential load forecasting, IEEE Trans Smart Grid, 8, 1591, 10.1109/TSG.2015.2493205 · DOI · Paper page
  18. 10.1109/TSG.2013.2277171 · doi:10.1109/tsg.2013.2277171
  19. 10.1109/72.279181 · DOI · Paper page
  20. 10.1109/5.58337 · DOI · Paper page
  21. sutskever, 2014, Sequence to sequence learning with neural networks, Proc Adv Neural Inf Process Syst, 3104
  22. mao, 2014, Deep captioning with multimodal recurrent neural networks (m-rnn)
  23. john, 2001, Gradient flow in recurrent nets: The difficulty of learning long-term dependencies, A Field Guide to Dynamical Recurrent Networks, 464
  24. 10.1109/CIT/IUCC/DASC/PICOM.2015.41 · doi:10.1109/cit/iucc/dasc/picom.2015.41
  25. graves, 2014, Towards end-to-end speech recognition with recurrent neural networks, Proc ICML, 1764
  26. 10.1109/TPWRS.2008.922249 · DOI
  27. 10.1109/TPAMI.2013.50 · DOI · Paper page
  28. 10.1109/NAPS.2011.6025124 · DOI
  29. 10.1016/j.segan.2016.02.005 · DOI · Paper page
  30. ryu, 2016, Deep neural network based demand side short term load forecasting, Proc IEEE Int Conf Smart Grid Commun (SmartGridComm), 308
  31. rowe, 2014, The real-time optimisation of DNO owned storage devices on the LV network for peak reduction, Energies, 7, 3537, 10.3390/en7063537 · DOI
  32. 2014, Smart Grid Smart City
  33. 10.1109/SE4SG.2013.6596108 · DOI
  34. 10.1109/IECON.2016.7793413 · DOI · Paper page
  35. ester, 1996, A density-based algorithm for discovering clusters in large spatial databases with noise, Proc KDD, 226
  36. shove, 2012, The Dynamics of Social Practice Every-day Life and How It Changes, 10.4135/9781446250655 · DOI · Paper page

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Metadata

Authors
Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y.
Venue
IEEE Transactions on Smart Grid
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-01
Volume
10
Issue
1
Pages
841-851
ISSN
  • 1949-3053 (ISSN-L)
  • 1949-3061
DOI
10.1109/tsg.2017.2753802
OpenAlex
W2754252319
Cited by
2,645
References
6
FWCI
53.81
Type
journal article
Language
en
Open access
closed
Licence

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CoChat. (2026). Citation check: Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. https://app.cochat.ai/papers/10.1109/tsg.2017.2753802
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Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2019). Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2017.2753802

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