A novel meta-learning approach for few-shot short-term wind power forecasting-Reference-Cited by-同舟云学术

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A novel meta-learning approach for few-shot short-term wind power forecasting

Published:2024-05 Issue: Volume:362 Page:122838
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Chen Fuhao,Yan Jie,Liu Yongqian,Yan Yamin,Tjernberg Lina Bertling

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference45 articles.

1. Review of meta-heuristic algorithms for wind power prediction: methodologies, applications and challenges;Lu;Appl Energy,2021

2. Medium-term multi-stage distributionally robust scheduling of hydro–wind–solar complementary systems in electricity markets considering multiple time-scale uncertainties;Li;Appl Energy,2023

3. The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant;Zou;Appl Energy,2023

4. Wind power forecasting considering data privacy protection: a federated deep reinforcement learning approach;Li;Appl Energy,2023

5. Ultra-short-term probabilistic wind power forecasting with spatial-temporal multi-scale features and K-FSDW based weight;Che;Appl Energy,2023

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events;Applied Energy;2024-12

2. A reconstruction-based secondary decomposition-ensemble framework for wind power forecasting;Energy;2024-11

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