An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization
Ameni Boumaiza, Kenza Maher
Abstract
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 CO 2 emissions. Interval-based uncertainty modeling further enhances robustness. This framework improves participation and economic returns in competitive peer-to-peer trading networks.
Topics & Concepts
Peer-to-peerBusinessComputer scienceEnvironmental economicsDistributed computingEconomicsSmart Grid Security and ResilienceSmart Grid Energy ManagementElectricity Theft Detection Techniques