An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization

The proliferation of advanced metering systems has enabled decentralized energy management, allowing prosumers to optimize usage and trading. This study introduces a machine-learning enhanced HEMS framework operating in three stages: asset scheduling, bid optimization, and real-time adjustment. Results from a simulated community of four prosumers demonstrate a 30% reduction in grid dependency, a 20% increase in revenue, and an 18% decrease in CO2 emissions. Interval-based uncertainty modeling further enhances robustness. This framework improves participation and economic returns in competitive peer-to-peer trading networks.