A reconstruction-based secondary decomposition-ensemble framework for wind power forecasting
Author:
Funder
China Postdoctoral Science Foundation
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Publisher
Elsevier BV
Reference62 articles.
1. A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests;Jonkers;Appl Energy,2024
2. A short-term wind power forecasting method based on multivariate signal decomposition and variable selection;Yang;Appl Energy,2024
3. Ultra-short-term wind power probabilistic forecasting based on an evolutionary non-crossing multi-output quantile regression deep neural network;Zhu;Energy Convers Manag,2024
4. A novel meta-learning approach for few-shot short-term wind power forecasting;Chen;Appl Energy,2024
5. Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy;Sun;Appl Energy,2023
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