no code implementations • 2 Nov 2022 • Yang Li, Ruinong Wang, Yuanzheng Li, Meng Zhang, Chao Long
To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL).
no code implementations • 15 Aug 2021 • Yang Li, Ruinong Wang, Zhen Yang
In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads.