Privacy-Preserving Socio-Aware Short-Term Residential Load Forecasting

This paper introduces a novel approach named the Privacy-Preserving Socio-Aware Model (PSocLF) for Short-Term Residential Load Forecasting, which addresses the need for forecasting models tailored to district-specific socio-demographic characteristics. By leveraging sociodemographic characteristics and personalized socio-aware knowledge sharing, PSocLF develops district-level forecasting models that enhance load forecasting precision while safeguarding individual privacy. Within PSocLF, we propose a new model structure named the Self-Gating TSMixer (SGTSMixer), which integrates self-gating mixing procedures with stacked multi-layer perceptrons (MLPs). It can efficiently extract temporal patterns and incorporate socioaware information to improve prediction accuracy. Simulation results based on real-world data demonstrate the effectiveness of the proposed PSocLF framework, outperforming alternative training paradigms and benchmarks in model structure design, particularly in scenarios with varying sociodemographic characteristics among districts. This paper contributes to advancing federated residential load forecasting and highlights the practical benefits of integrating sociodemographic information for improved forecasting accuracy and effectiveness.