Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization
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DOI: 10.1016/j.energy.2021.121145
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Cited by:
- Aoqi Xu & Man-Wen Tian & Behnam Firouzi & Khalid A. Alattas & Ardashir Mohammadzadeh & Ebrahim Ghaderpour, 2022. "A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting," Sustainability, MDPI, vol. 14(16), pages 1-12, August.
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- Zhang, Yituo & Li, Chaolin & Jiang, Yiqi & Zhao, Ruobin & Yan, Kefen & Wang, Wenhui, 2023. "A hybrid model combining mode decomposition and deep learning algorithms for detecting TP in urban sewer networks," Applied Energy, Elsevier, vol. 333(C).
- Zhoufan Chen & Congmin Wang & Longjin Lv & Liangzhong Fan & Shiting Wen & Zhengtao Xiang, 2023. "Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model," Sustainability, MDPI, vol. 15(15), pages 1-16, July.
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- Banteng Liu & Yangqing Xie & Ke Wang & Lizhe Yu & Ying Zhou & Xiaowen Lv, 2023. "Short-Term Multi-Step Wind Direction Prediction Based on OVMD Quadratic Decomposition and LSTM," Sustainability, MDPI, vol. 15(15), pages 1-18, July.
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Keywords
Secondary decomposition; Grey wolf optimized support vector regression; Power demand forecasting; Interval optimization;All these keywords.
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