Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms-Reference-Cited by-同舟云学术

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Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms

Published:2024-02 Issue: Volume:355 Page:122185
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Hu Jiaxiang,Hu Weihao,Cao Di,Huang Yuehui,Chen Jianjun,Li Yahe,Chen Zhe,Blaabjerg Frede

Funder

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference56 articles.

1. Privacy-preserving spatiotemporal scenario generation of renewable energies: a federated deep generative learning approach;Li;IEEE Trans Ind Inform,2022

2. Load forecasting techniques for power system: research challenges and survey;Ahmad;IEEE Access,2022

3. Self-attention-based machine theory of mind for electric vehicle charging demand forecast;Hu;IEEE Trans Ind Inform,2022

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

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1. Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization;Cleaner Energy Systems;2024-12

2. Combined central-local voltage control of inverter-based DG in active distribution networks;Applied Energy;2024-10

3. Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method;Renewable Energy;2024-05

4. Ultra-short-term wind power probabilistic forecasting based on an evolutionary non-crossing multi-output quantile regression deep neural network;Energy Conversion and Management;2024-02

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