Privacy-Preserving and Hierarchically Federated Framework for Short-Term Residential Load Forecasting

Short-term Load Forecasting (STLF) plays a fundamental role in modern energy systems. This paper proposes a privacy-preserving STLF framework for residential energy users. The framework enables users to collaboratively train an STLF model without exchanging their load data. The system works in two stages: (i) a federated user clustering method is developed to divide the users into multiple clusters based on load pattern similarity. Instead of aggregating the users’ data to do clustering, the developed method determines the user clusters in a distributed manner. (ii) after the user clustering stage, a hierarchically federated STLF model training is developed, which applies federated learning principles to facilitate intra- and inter-user cluster model training. In the process, the individual users also do not expose their load data. Further, an asynchronous communication mechanism is designed and integrated into the framework, making it high fault-tolerant and adaptable to the residential environment with communication uncertainties. Comprehensive experiments based on a real Australian load dataset are conducted to validate the system. The simulation results show that the proposed framework can effectively train the load forecasting model with a fast coverage rate and achieve up to 37.25% prediction accuracy improvement (in terms of the Mean Absolute Percentage Error metric) compared with the other benchmark methods.