Journal Article10.1016/j.energy.2023.128226
A short-term wind power prediction method based on dynamic and static feature fusion mining
Mao Yang,Da Wei Wang,Wei Zhang +2 more
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TL;DR: In this paper , a short-term wind power prediction method based on dynamic and static feature fusion mining is proposed to take full advantage of the time-varying value provided by wind power fluctuations.
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About: This article is published in Energy. The article was published on 01 Oct 2023. The article focuses on the topics: Overfitting & Wind power.
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Citations
Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process
Mao Yang,Yunfeng Guo,Yutong Huang +2 more
TL;DR: This paper proposes an ultra-short-term wind power prediction method combining NWP wind speed correction and double clustering division of transitional weather processes, improving accuracy by 5.93% in RMSE and 4.82% in MAE compared to existing methods.
43
A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN
Anbo Meng,Haitao Zhang,Zikang Xian,Shu Chen,Zi Zhu,Jiayu Rong,Chenen Wang,Weisi Deng +7 more
TL;DR: A novel multi-gradient evolutionary deep learning approach, EATDLNN, is proposed for few-shot wind power prediction in newly constructed wind farms, incorporating TimeGAN and MVMD, achieving 79.40-91.05% reduction in root mean square errors compared to state-of-the-art methods.
16
Ultra-short-term wind farm cluster power prediction based on FC-GCN and trend-aware switching mechanism
TL;DR: This paper proposes a novel ultra-short-term wind farm cluster power prediction method based on FC-GCN and trend-aware switching mechanism, achieving improved accuracy by 1.34-2.07% RMSE and 4.49-6.62% CWC in three Chinese provinces.
15
A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information
TL;DR: A novel short-term wind power prediction method, DAEGC-TimesNet, is proposed for wind farm clusters, leveraging multi-source spatiotemporal features and achieving higher accuracy (RMSE: 0.0155, MAE: 0.0156) compared to existing methods.
12
Short-term Integrated Forecasting Method for Wind Power, Solar Power, and System Load Based on Variable Attention Mechanism and Multi-task Learning
Han Wang,Jie Yan,Jiawei Zhang,Shuang Liu,Yongqian LIU,Sang‐Ik Han,Tanxia Qu +6 more
10
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