Abstract With the support of our government’s commitment to the energy sector, the installed capacity of wind power will continue to grow. However, due to the instability of wind power, accurate prediction of wind power output is essential for effective grid dispatch. In addition, data privacy and protection have become paramount in today’s society. Traditional wind forecasting methods rely on centralized data, which raises concerns about data privacy and data silos. To address these challenges, we propose a hybrid approach that combines federated learning and deep learning for wind power forecasting. In our proposed method, we use a bidirectional long short-term memory (BILSTM) neural network as the basic prediction model to improve the prediction accuracy. Then, the model is integrated into the federated learning framework to form the Fed-BILSTM prediction method. In addition, we have introduced cloud computing technology into the Fed-BILSTM method, using cloud resources for model training and parameter update. Participants share model parameters instead of sharing raw data, which solves data privacy concerns. We compared Fed-BILSTM with traditional forecasting methods. Experimental results show that the proposed Fed-BILSTM is better than the traditional prediction method in terms of prediction accuracy. What’s more, Fed-BILSTM can effectively protect data privacy compared to traditional centralized forecasting methods while ensuring prediction performance.
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