Short-term wind power prediction based on ICEEMDAN decomposition and BiTCN–BiGRU-multi-head self-attention model
Author:
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00202-024-02638-8.pdf
Reference46 articles.
1. Tianyao J, Jin W, Mengshi L et al (2022) Short-term wind power forecast based on chaotic analysis and multivariate phase space reconstruction. Energy Convers Manag 254:115196
2. Shuai H, Yue X, Hongcai Z et al (2021) Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction. Appl Energy 293:116951
3. Lin Y, Binhua D, Zhuo L et al (2022) An ensemble method for short-term wind power prediction considering error correction strategy. Appl Energy 322:119475
4. Li LL, Zhao X, Tseng ML et al (2020) Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm. J Clean Prod 242:118447.1-118447.12
5. Hoolohan V, Tomlin SA, Cockerill T (2018) Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data. Renew Energy 126:1043–1054
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