TY - JOUR
T1 - An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization
AU - Boumaiza, Ameni
AU - Maher, Kenza
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/7/5
Y1 - 2025/7/5
N2 - 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.
AB - 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.
KW - Home Energy Management
KW - Machine learning
KW - Optimization
KW - Peer-to-peer trading
KW - Smart grids
KW - Uncertainty modeling
UR - https://www.scopus.com/pages/publications/105009826056
U2 - 10.1016/j.esr.2025.101749
DO - 10.1016/j.esr.2025.101749
M3 - Article
AN - SCOPUS:105009826056
SN - 2211-467X
VL - 60
JO - Energy Strategy Reviews
JF - Energy Strategy Reviews
M1 - 101749
ER -