Open Access
Issue
E3S Web Conf.
Volume 182, 2020
2020 10th International Conference on Power, Energy and Electrical Engineering (CPEEE 2020)
Article Number 02007
Number of page(s) 6
Section Modern Power System Control and Operation
DOI https://doi.org/10.1051/e3sconf/202018202007
Published online 31 July 2020
  1. KONG Xiangyu, ZHENG Feng, E Zhijun, et al, “Short-term Load Forecasting Based on Deep Belief Network[J]”, Automation of Electric Power Systems, 2018, 42(5), pp. 133-139. [Google Scholar]
  2. LIUJ, GAO H,ZHAO M.A, et al, “Review and prospect of active distribution system planning[J]”, Journal of Modern Power Systems and Clean Energy, 2015, 3(4), pp. 457-467. [Google Scholar]
  3. YAO Jiangang, FU Qiang, YE Lun, et al, “Substation Capacity Planning Method Considering Influence of Peak-valley Time-of-use Power Price[J]”, Automation of Electric Power Systems, 2017, 41(13), pp. 53-61. [Google Scholar]
  4. LIU H, ZENG P,GUO J, et al, “An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic[J]”, Journal of Modern Power Systems and Clean Energy, 2015, 3(2), pp.232-239. [Google Scholar]
  5. LI Bo, MEN Deyue, YAN Yaqin, et al, “Architecture and Key Technologies for Auxiliary Decision-making System of Integrated Dispatch Scheduling[J]”, Automation of Electric Power Systems, 2015, 39(1), pp.137-140. [Google Scholar]
  6. WU Qianhong, GAO Jun, HOU Guangsong, et al. “Short-term Load Forecasting Support Vector Machine Algorithm Based on Multi-source Heterogeneous Fusion of Load Factors[J]”, Automation of Electric Power Systems, 2016, 40(15), pp. 67-72. [Google Scholar]
  7. WU Xiaoyu, HE Jinghan, ZHANG Pei, et al, “Power System Short-term Load Forecasting Based on Improved Random Forest with Grey Relation Projection[J]”, Automation of Electric Power Systems, 2015, 39(12), pp. 50-55. [Google Scholar]
  8. XIAO Bai, NIE Peng, MU Gang, et al, “A Spatial Load Forecasting Method Based on Multilevel Clustering Analysis and Support Vector Machine[J]”, Automation of Electric Power Systems, 2015, 39(12), pp. 56-61. [Google Scholar]
  9. Lipton, Zachary C., Berkowitz, John, and Elkan, Charles, “A critical review of recurrent neural networks for sequence learning”, arXiv preprint arXiv:1506.00019, 2015. [Google Scholar]
  10. Justin Bayer, Daan Wierstra, Julian Togelius, and J¨urgen Schmidhuber, “Evolv-ing memory cell structures for sequence learning”, Artificial Neural Networks–ICANN 2009, pp. 755-764, Springer, 2009. [Google Scholar]
  11. Yoshua Bengio, Patrice Simard, and Paolo Frasconi’ “ Learning long-term depen-dencies with gradient descent is difficult”, Neural Networks,IEEE Transactions on, 5(2), pp. 157-166, 1994. [Google Scholar]
  12. XU Longbo,WANG Wei,ZHANG Tao, et al, “Ultra-short-term Wind Power Prediction Based on Neural Network and Mean Impact Value[J]”, Automation of Electric Power Systems, 2017, 41(21), pp. 40-45. [Google Scholar]
  13. LIU Ruiye, HUANG Lei, “Wind Power Forecasting Based on Dynamic Neural Networks[J]”, Automation of Electric Power Systems, 2012, 36(11), pp. 19-22. [Google Scholar]
  14. HERNANDEZ L, BALADRON C, AGUIAR J.M, et al, “Artificial neural network for short-term load forecasting in distribution systems[J]”, Energies, 2014, 7(3), pp. 1576-1598. [Google Scholar]
  15. Bengio Y, Boulanger-Lewandowski N, Pascanu R, “Advances in Optimizing Recurrent Networks”, IEEE International Conference on Acoustics, 2012, pp. 8624-8. [Google Scholar]
  16. Adam Gibson, Josh Patterson, “Deep Learning: A Practitioner’s Approach [M]”, Boston:O’Reilly Media, 2017. [Google Scholar]
  17. Wim De Mulder, Steven Bethard, and Marie-Francine Moens, “A survey on the application of recurrent neural networks to statistical language modeling”, Computer Speech & Language, 30(1), pp. 61-98, 2015. [Google Scholar]
  18. “Hourly Load Data Archives [EB/OL]”, [2018-06-12]. http://www.ercot.com/gridinfo/load/load_hist/. [Google Scholar]

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