Wind Power Forecasting: LSTM-Combined Deep Reinforcement Learning Approach
Author:
Affiliation:
1. Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang,Guizhou,China,550002
2. CSG Electric Power Research Institute Co., Ltd.,Guangzhou,Guangdong,China,510663
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10511272/10512350/10512354.pdf?arnumber=10512354
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1. Deep learning for cybersecurity in smart grids: Review and perspectives
2. Decentralized Energy Management of Microgrid Based on Blockchain-Empowered Consensus Algorithm With Collusion Prevention
3. Wind turbine fault diagnosis based on ReliefF-PCA and DNN
4. On Vulnerability of Renewable Energy Forecasting: Adversarial Learning Attacks
5. Privacy-Preserving Regulation Capacity Evaluation for HVAC Systems in Heterogeneous Buildings Based on Federated Learning and Transfer Learning
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