Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach-Reference-Cited by-同舟云学术

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Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach

Published:2023-01 Issue: Volume:329 Page:120291
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Li Yang,Wang Ruinong,Li Yuanzheng,Zhang Meng,Long Chao

Funder

Natural Science Foundation of Jilin Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

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

Reference38 articles.

1. A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making;Li;Energy,2018

2. Probabilistic wind power forecasting using optimised deep auto-regressive recurrent neural networks;Arora;IEEE Trans Ind Inf,2022

3. Multistage wind-electric power forecast by using a combination of advanced statistical methods;Buhan;IEEE Trans Ind Inf,2015

4. Towards privacy protection in smart grid;Zeadally;Wirel Pers Commun,2013

5. The future of forecasting for renewable energy;Sweeney;Wiley Interdiscip Rev Energy Environ,2020

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