Personalized federated learning with mixture of experts and conformal prediction for household energy forecasting

Accurate forecasting of household energy load and generation is critical for energy management systems, especially in the context of the rapid development of smart grids and renewable energy. However, privacy concerns often arise when handling sensitive household energy data. Federated learning (FL), as a privacy-preserving distributed learning method, enables collaborative model training across households without exposing sensitive energy data. Nevertheless, traditional federated learning still faces challenges in meeting the personalized needs of households due to the differences in consumption patterns among households, which affects the forecasting accuracy. In this paper, we propose a novel personalized FL method that combines mixture of expert and conformal predictions to improve forecasting accuracy while quantifying prediction uncertainty. Our method utilizes personalized federated learning (PFL) to develop a personalized model for each household that captures its unique consumption behavior. The mixture of experts dynamically integrates global and local personalized models to enhance prediction and adaptability. In addition, uncertainty quantification is achieved through conformal prediction, providing reliable prediction interval. Experiments conducted on two real-world household energy datasets demonstrate that our method outperforms existing approaches in terms of prediction accuracy and uncertainty assessment.