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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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Energy Strategy Reviews |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X25001129 |
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| _version_ | 1849240617184395264 |
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| author | Ameni Boumaiza Kenza Maher |
| author_facet | Ameni Boumaiza Kenza Maher |
| author_sort | Ameni Boumaiza |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a8cdae5b879347d596bdb64d1f31269c |
| institution | Kabale University |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj-art-a8cdae5b879347d596bdb64d1f31269c2025-08-20T04:00:32ZengElsevierEnergy Strategy Reviews2211-467X2025-07-016010174910.1016/j.esr.2025.101749An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimizationAmeni Boumaiza0Kenza Maher1Corresponding author.; Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, 34110, QatarQatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, 34110, QatarThe 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.http://www.sciencedirect.com/science/article/pii/S2211467X25001129Home Energy ManagementPeer-to-peer tradingMachine learningSmart gridsOptimizationUncertainty modeling |
| spellingShingle | Ameni Boumaiza Kenza Maher An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization Energy Strategy Reviews Home Energy Management Peer-to-peer trading Machine learning Smart grids Optimization Uncertainty modeling |
| title | An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization |
| title_full | An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization |
| title_fullStr | An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization |
| title_full_unstemmed | An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization |
| title_short | An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization |
| title_sort | adaptive home energy management system for prosumers in peer to peer trading networks with machine learning optimization |
| topic | Home Energy Management Peer-to-peer trading Machine learning Smart grids Optimization Uncertainty modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X25001129 |
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