FedForecast: A federated learning framework for short-term probabilistic individual load forecasting in smart grid

Abstract

Load forecasting plays a crucial role in the power system operation and planning. However, with people’s increased awareness of privacy, consumers may not be willing to share their data with the retailer. Thus, the conventional centralized training approach could not be adopted. To tackle these issues, we propose a distributed short-term individual load forecasting method based on the federated learning framework called FedForecast, which could protect the privacy of consumers and make full use of edge computing resources. In this framework, the forecasting models rather than load data are transmitted during the model training and in this way, the privacy of consumers is protected and computation capacity of edge device is fully utilized. The detailed theoretical mathematical proof is presented to verify the convergence of the proposed algorithm. Case studies on the PecanStreet dataset show that the proposed federated approach has comparable performance with the centralized method and robustness of the proposed framework is also verified. The results demonstrate that the proposed framework could achieve good accuracy and robustness in individual probabilistic load forecasting.