Short-Term Residential Load Forecasting Based on Federated Learning and Load Clustering

Power load forecasting plays a fundamental role in modern energy systems' operations. While traditional load forecasting applies to bus-level aggregated load data, widespread deployment of advanced metering infrastructure creates an opportunity to fine-grained monitor the power consumption of single households and to predict their load requirements. This paper proposes a distributed residential load forecasting framework that combines federated learning and load clustering techniques. The system firstly applies a K-means clustering algorithm to divide a group of residential users into multiple clusters based on their historical power consumption patterns. For each cluster, the system then applies a federated learning process to enable the users in that cluster to collaboratively train their local load prediction models without physically sharing their load data. Experiments and comparison studies are conducted based on a real Australian residential load dataset to validate the proposed approach and to highlight its ease of use.