Resident Load Forecasting Using Federal Learning Based on LSTM Model

Residential electricity consumption constitutes over 15% of the total social electricity consumption, a significant number of users spread across various locations. Accurate prediction of the residential load is conducive to integrating demand-side resources and meeting the response requirements on the demand side. Most of the existing residential load forecasting methods are centralized, requiring entensive data exchange between the server and the client. This results in increased communication costs and information security problems. Based on the federal learning framework, the long-term and short-term memory network is utilized to study the residential load forecasting method. The simulation results indicate that the residential load forecasting based on federal learning outperforms centralized learning in real calculation efficiency. In addition, we compared our algorithm with the FedAvg, FedAdagrad, and FedYogi federal learning strategies, and found that the FedAdagrad federal learning strategy with adaptive optimization function achieves higher accuracy and better convergence in residential load forecasting.