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25. A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN. (2023). Luo, Jianqiang ; Yin, Hao ; Zhang, Haitao ; Xian, Zikang ; Li, Chen ; Zhu, Zibin ; Wang, Chenen ; Meng, Anbo ; Wu, Zhenbo ; Deng, Weisi ; Chen, Shu ; Rong, Jiayu.
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    RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025331.

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26. Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm. (2023). Fu, Yongyan ; Peng, Tian ; Song, Shihao ; Nazir, Muhammad Shahzad ; Suo, Leiming ; Zhang, Chu ; Wang, Yuhan.
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    RePEc:eee:energy:v:276:y:2023:i:c:s0360544223009209.

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27. A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction. (2023). Tao, Zihan ; Peng, Tian ; Song, Shihao ; Nazir, Muhammad Shahzad ; Xiong, Jinlin ; Zhang, Chu.
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    RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033059.

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28. Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window. (2023). Ouassaid, Mohammed ; Adjallah, Kondo Hloindo ; Yetilmezsoy, Kaan ; Bilal, Boudy ; Sava, Alexandre.
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29. Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy. (2023). Du, Zongjuan ; Wang, Shouyang ; Sun, Shaolong ; Jin, Kun ; Li, Hongtao.
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30. Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm. (2022). Ji, Zhengsen ; Gao, Tian ; Sun, Lijie ; Niu, Dongxiao.
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    RePEc:eee:energy:v:261:y:2022:i:pb:s0360544222022125.

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31. Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids. (2022). Yin, Linfei ; Han, Kunlun ; Yang, Kai.
    In: Applied Energy.
    RePEc:eee:appene:v:317:y:2022:i:c:s0306261922005359.

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