Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training-Reference-Cited by-同舟云学术

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Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training

Published:2024-04 Issue: Volume:224 Page:120200
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Tang Yugui,Yang Kuo,Zheng Yichu,Ma Li,Zhang Shujing,Zhang Zhen^ORCID

Publisher

Elsevier BV

Reference42 articles.

1. Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain;Yan;Renew. Sustain. Energy Rev.,2022

2. Correlation based neuro-fuzzy Wiener type wind power forecasting model by using special separate signals;Xu;Energy Convers. Manag.,2022

3. Wind power prediction based on numerical and statistical models;Stathopoulos;J. Wind Eng. Ind. Aerod.,2013

4. Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output;Cassola;Appl. Energy,2012

5. Forecast of hourly average wind speed with ARMA models in Navarre (Spain);Torres;Sol. Energy,2005

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. Fractional-order transfer Q-learning based on modal decomposition and convolutional neural networks for voltage control of smart grids;Advanced Engineering Informatics;2024-10

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