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. Resu...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Energy Strategy Reviews |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X25001129 |
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| Summary: | 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. |
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| ISSN: | 2211-467X |