From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment-Reference-Cited by-同舟云学术

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From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment

Published:2024-06 Issue: Volume:364 Page:123138
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Ding Jiaqi^ORCID,Zhao Pu,Liu Changjun,Wang Xiaofang,Xie Rong,Liu Haitao

Publisher

Elsevier BV

Reference58 articles.

1. A review and taxonomy of wind and solar energy forecasting methods based on deep learning;Alkhayat;Energy AI,2021

2. Machine learning and deep learning in energy systems: A review;Forootan;Sustainability,2022

3. Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep learning techniques;Abualigah;Energies,2022

4. Review on deep learning applications in frequency analysis and control of modern power system;Zhang;Int J Electr Power Energy Syst,2022

5. Deep neural networks for energy load forecasting;Amarasinghe,2017

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. Koopman-inspired data-driven quantification of fluid–structure energy transfers;Physics of Fluids;2024-09-01

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