Personalized federated learning for individual consumer load forecasting

Abstract

The installation of smart meters enables electricity retailers or consumers to implement individual load forecasting for demand response. An individual load forecasting model can be trained either on each consumer's own smart meter data or the smart meter data of multiple consumers. The former practice may suffer from overfitting if a complex model is trained because the dataset is limited; the latter practice cannot protect the privacy of individual consumers. This paper tackles the dilemma by proposing a personalized federated approach for individual consumer load forecasting. Specifically, a group of consumers first jointly train a federated forecasting model on the shared smart meter data pool, and then each consumer personalizes the federated forecasting model on their own data. Comprehensive case studies are conducted on an open dataset of 100 households. Results verify that the proposed method can enhance forecasting accuracy by making full use of the data from other consumers with privacy protection.