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Residential load forecasting based on LSTM fusing self-attention mechanism with pooling

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  1. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
  2. Yixiang Ma & Lean Yu & Guoxing Zhang, 2022. "A Hybrid Short-Term Load Forecasting Model Based on a Multi-Trait-Driven Methodology and Secondary Decomposition," Energies, MDPI, vol. 15(16), pages 1-20, August.
  3. Tao, Peng & Xu, Fei & Dong, Zengbo & Zhang, Chao & Peng, Xuefeng & Zhao, Junpeng & Li, Kangping & Wang, Fei, 2022. "Graph convolutional network-based aggregated demand response baseline load estimation," Energy, Elsevier, vol. 251(C).
  4. Bo Hu & Jian Xu & Zuoxia Xing & Pengfei Zhang & Jia Cui & Jinglu Liu, 2022. "Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO," Energies, MDPI, vol. 15(8), pages 1-14, April.
  5. Xiao, Xun & Mo, Huadong & Zhang, Yinan & Shan, Guangcun, 2022. "Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting," Energy, Elsevier, vol. 246(C).
  6. Fan, Jingmin & Zhong, Mingwei & Guan, Yuanpeng & Yi, Siqi & Xu, Cancheng & Zhai, Yanpeng & Zhou, Yongwang, 2024. "An online long-term load forecasting method: Hierarchical highway network based on crisscross feature collaboration," Energy, Elsevier, vol. 299(C).
  7. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
  8. Banafshe Parizad & Hassan Ranjbarzadeh & Ali Jamali & Hamid Khayyam, 2024. "An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price," Sustainability, MDPI, vol. 16(6), pages 1-17, March.
  9. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
  10. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
  11. Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).
  12. Zang, Haixiang & Jiang, Xin & Cheng, LiLin & Zhang, Fengchun & Wei, Zhinong & Sun, Guoqiang, 2022. "Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations," Renewable Energy, Elsevier, vol. 195(C), pages 795-808.
  13. Mustafa Saglam & Xiaojing Lv & Catalina Spataru & Omer Ali Karaman, 2024. "Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning," Energies, MDPI, vol. 17(4), pages 1-22, February.
  14. Wang, Jun & Cao, Junxing & Fu, Jingcheng & Xu, Hanqing, 2022. "Missing well logs prediction using deep learning integrated neural network with the self-attention mechanism," Energy, Elsevier, vol. 261(PB).
  15. Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
  16. Wei, Nan & Yin, Chuang & Yin, Lihua & Tan, Jingyi & Liu, Jinyuan & Wang, Shouxi & Qiao, Weibiao & Zeng, Fanhua, 2024. "Short-term load forecasting based on WM algorithm and transfer learning model," Applied Energy, Elsevier, vol. 353(PA).
  17. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
  18. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
  19. Park, Jungyeon & Alvarenga, Estêvão & Jeon, Jooyoung & Li, Ran & Petropoulos, Fotios & Kim, Hokyun & Ahn, Kwangwon, 2024. "Probabilistic forecast-based portfolio optimization of electricity demand at low aggregation levels," Applied Energy, Elsevier, vol. 353(PB).
  20. Mulleriyawage, U.G.K. & Wang, P. & Rui, T. & Zhang, K. & Hu, C. & Shen, W.X., 2023. "Prosumer-centric demand side management for minimizing electricity bills in a DC residential PV-battery system: An Australian household case study," Renewable Energy, Elsevier, vol. 205(C), pages 800-812.
  21. Pan, Shaowei & Yang, Bo & Wang, Shukai & Guo, Zhi & Wang, Lin & Liu, Jinhua & Wu, Siyu, 2023. "Oil well production prediction based on CNN-LSTM model with self-attention mechanism," Energy, Elsevier, vol. 284(C).
  22. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
  23. Xu, Huifeng & Hu, Feihu & Liang, Xinhao & Zhao, Guoqing & Abugunmi, Mohammad, 2024. "A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network," Energy, Elsevier, vol. 299(C).
  24. Mingping Liu & Xihao Sun & Qingnian Wang & Suhui Deng, 2022. "Short-Term Load Forecasting Using EMD with Feature Selection and TCN-Based Deep Learning Model," Energies, MDPI, vol. 15(19), pages 1-22, September.
  25. Jiajie Tang & Jie Zhao & Hongliang Zou & Gaoyuan Ma & Jun Wu & Xu Jiang & Huaixun Zhang, 2021. "Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
  26. Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
  27. Fazlipour, Zahra & Mashhour, Elaheh & Joorabian, Mahmood, 2022. "A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism," Applied Energy, Elsevier, vol. 327(C).
  28. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
  29. Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
  30. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
  31. Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Fractional-order long-term price guidance mechanism based on bidirectional prediction with attention mechanism for electric vehicle charging," Energy, Elsevier, vol. 293(C).
  32. Yongqi Zou & Wenjiang Feng & Juntao Zhang & Jingfu Li, 2022. "Forecasting of Short-Term Load Using the MFF-SAM-GCN Model," Energies, MDPI, vol. 15(9), pages 1-16, April.
  33. Tingting Hou & Rengcun Fang & Jinrui Tang & Ganheng Ge & Dongjun Yang & Jianchao Liu & Wei Zhang, 2021. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms," Energies, MDPI, vol. 14(22), pages 1-21, November.
  34. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).
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