A novel short-term electrical load forecasting framework with intelligent feature engineering
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DOI: 10.1016/j.apenergy.2022.120089
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- Jiang, Zongxi & Zhang, Luliang & Ji, Tianyao, 2023. "NSDAR: A neural network-based model for similar day screening and electric load forecasting," Applied Energy, Elsevier, vol. 349(C).
- Tan Ngoc Dinh & Gokul Sidarth Thirunavukkarasu & Mehdi Seyedmahmoudian & Saad Mekhilef & Alex Stojcevski, 2024. "Robust-mv-M-LSTM-CI : Robust Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic," Sustainability, MDPI, vol. 16(15), pages 1-21, August.
- Taorong Jia & Lixiao Yao & Guoqing Yang & Qi He, 2022. "A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
- Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
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Keywords
Short-term electrical load forecasting; Intelligent feature engineering; Influencing factors with time-scale differences; Multiple time-scale features;All these keywords.
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