Federated Learning with LSTM Neural Network for Regional Load Forecasting

With the increasing penetration of distributed energy resources at the grid edge, including renewable energy generation, flexible loads, and energy storage devices, accurately predicting consumer-level distributed generation and consumption has become crucial. However, conventional centralized processing and machine learning approaches are impractical because of data security and privacy issues. This paper proposes a load forecasting method based on federated learning with LSTM neural network which updates model parameters by having clients responsible for model training and a server responsible for model aggregation, allowing multiple clients to collaboratively train a machine learning model without sharing raw data. Simulation results show that this method can achieve accurate predictions while preserving privacy.