Abstract:Renewable energy plays a vital role in ensuring energy supply, improving climate and regulating national energy structure. Forecasting renewable generation accurately is conducive to maintaining system balance and reducing operating costs. However, since the output of renewable generation is highly dependent on the climate and has complex spatiotemporal correlations, it is difficult to make accurate predictions using traditional methods. Although machine learning models are gradually being widely used in renewable energy output prediction, they usually face the problem of insufficient model training data. This type of problem hinders the realization of accurate predictions. In order to solve the above problems, this paper proposes a renewable generation prediction method based on differential privacy and ensemble learning to achieve accurate predictions. Specifically, we first propose a data protection scheme based on differential privacy to achieve data aggregation, thereby solving the problem of insufficient data. In addition, we also propose a renewable energy output forecasting framework based on ensemble learning, which uses multiple basic models to jointly complete the prediction, thereby improving the accuracy and robustness of the prediction. Case studies comprehensively demonstrate the effectiveness and superiority of the proposed method.