Renewable Generation Prediction Method Based on Differential Privacy and Ensemble Learning
Renewable energy plays a crucial role in ensuring energy supply,improving the climate environment,and optimizing the national energy structure.Accurate renewable energy output forecasting is of great significance for maintaining power system balance and reducing operating costs.However,due to its high dependence on complex climate conditions and spatiotemporal correlations,traditional methods are difficult to achieve accurate predictions.Although machine learning models are widely used in this field,the problem of insufficient data still limits the accuracy of predictions.To address this challenge,this paper proposes a novel renewable energy generation prediction method that combines differential privacy and ensemble learning.Firstly,a data protection mechanism based on differential privacy was designed to achieve data aggregation and solve the problem of insufficient data;then,a renewable energy output prediction framework based on ensemble learning was constructed,utilizing multiple basic models to jointly complete the prediction,thereby improving the accuracy and robustness of the prediction.The case study comprehensively demonstrates the effectiveness and superiority of the proposed method.
万方数据
维普
