Work

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

Year: 2025

Type: article

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.

Source: Energy Strategy Reviews

DOI: 10.1016/j.esr.2025.101749

Authors: Ameni Boumaiza, Kenza Maher

Institution: Hamad bin Khalifa University

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $4,180