A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction

Citations

Cited by:

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8. Zhang, Chu & Li, Zhengbo & Ge, Yida & Liu, Qianlong & Suo, Leiming & Song, Shihao & Peng, Tian, 2024. "Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD," Energy, Elsevier, vol. 296(C).
9. Wang, Zhongliang & Zhu, Hongyu & Zhang, Dongdong & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Modelling of wind and photovoltaic power output considering dynamic spatio-temporal correlation," Applied Energy, Elsevier, vol. 352(C).
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13. Ba-Alawi, Abdulrahman H. & Nguyen, Hai-Tra & Yoo, ChangKyoo, 2024. "Coordinated operation for a resilient and green energy-water supply system: A co-optimization approach with flexible strategies," Energy, Elsevier, vol. 304(C).
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15. 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).
16. Zhang, Chu & Zhang, Yue & Li, Zhengbo & Zhang, Zhao & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction," Applied Energy, Elsevier, vol. 359(C).
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35. Qu, Zhijian & Hou, Xinxing & Li, Jian & Hu, Wenbo, 2024. "Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation," Energy, Elsevier, vol. 290(C).
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