基于联邦学习和DAL策略的电力负荷预测OA

Electric load forecasting is a core foundation for power grid planning and operation.However,traditional methods rely on training models with data from a single region,resulting in a significant decline in generalization ca-pability when applied to cross-regional forecasting.To address this issue,this paper proposes a hybrid model that integrates a Temporal Convolutional Network(TCN),Long Short-Term Memory(LSTM),and an attention mecha-nism(TCN-LSTMs-Attention),combined with a Decentralized Aggregation Learning(DAL)strategy.In this framework,multiple sub-models are jointly trained to obtain a global model capable of cross-regional forecasting by sequentially training with data from different regions on the same server.Moreover,the proposed method incorpo-rates a dynamic learning rate halving and parameter resetting mechanism to further accelerate model convergence.Experiments based on the dataset from the 9th China Electrical Engineering Cup Competition demonstrate that,com-pared to models trained independently on regional data,the proposed method improves Mean Squared Error(MSE),Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and Mean Absolute Percentage Error(MAPE)by 43.0％,29.5％,24.4％,and 35.4％,respectively,in cross-regional forecasting tasks.These results validate the model's robustness and engineering practicality in high-heterogeneity load scenarios.