Multi-step wind speed prediction based on an improved multi-objective seagull optimization algorithm and a multi-kernel extreme learning machine
Author:
Funder
Postdoctoral Research Foundation of China
Hongliu Outstanding Talents Program of Lanzhou University of Technology
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-04312-7.pdf
Reference58 articles.
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2. Han C, Vinel A (2022) Reducing forecasting error by optimally pooling wind energy generation sources through portfolio optimization. Energy 239:122099. https://doi.org/10.1016/j.energy.2021.122099
3. Afrasiabi M, Mohammadi M, Rastegar M et al (2021) Advanced deep learning approach for probabilistic wind speed forecasting. IEEE T Ind Inform 17(1):720–727. https://doi.org/10.1109/TII.2020.3004436
4. Liu Z, Jiang P, Zhang L, Niu X (2020) A combined forecasting model for time series: Application to short-term wind speed forecasting. Appl Energ 259:114137. https://doi.org/10.1016/j.apenergy.2019.114137
5. Zhao J, Wang J, Guo Z, Guo Y, Lin W, Lin Y (2019) Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method. Appl Energ 255:113833. https://doi.org/10.1016/j.apenergy.2019.113833
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