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

In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving a...

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Veröffentlicht in:	Applied energy Jg. 329; S. 120291
Hauptverfasser:	Li, Yang, Wang, Ruinong, Li, Yuanzheng, Zhang, Meng, Long, Chao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.01.2023
Schlagworte:	algorithms Data openness and sharing Deep reinforcement learning energy Federated learning issues and policy prediction Privacy protection Uncertainty modeling wind power Wind power forecasting Data openness and sharing Privacy protection Deep reinforcement learning Federated learning Wind power forecasting Uncertainty modeling
ISSN:	0306-2619, 1872-9118
Online-Zugang:	Volltext
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