A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting-Reference-Cited by-同舟云学术

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A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting

Published:2024-04 Issue: Volume:360 Page:122785
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Liu Zhi-Feng,Liu You-Yuan,Chen Xiao-Rui,Zhang Shu-Rui,Luo Xing-Fu,Li Ling-Ling,Yang Yi-Zhou,You Guo-Dong

Funder

National Natural Science Foundation of China

Ministry of Education of the People's Republic of China

Publisher

Elsevier BV

Reference67 articles.

1. Innovation in complementary energy technologies from renewable energy policies;Stevens;Renew Energy,2023

2. Does energy security improve renewable energy? A geopolitical perspective;Khan;Energy,2023

3. Renewable energy: moderated, moderating or mediating?;Akan;Appl Energy,2023

4. Scaling up community wind energy: the relevance of autonomy and community;Warlenius;Energy Sustain Soc,2023

5. Techno-economic analysis of a wind-photovoltaic-electrolysis-battery hybrid energy system for power and hydrogen generation;Li;Energ Conver Manage,2023

Cited by 1 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. High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine;Energies;2024-05-10

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