Continual learning for energy management systems: A review of methods and applications, and a case study
<p>An intelligent system must incrementally acquire, update, accumulate, and exploit knowledge to navigate the real world’s intricacies. This trait is frequently referred to as Continual Learning (CL), and it can be limited by catastrophic forgetting, a phenomenon in which learning a new task...
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1864513552084107264 |
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| author | Aya Nabil Sayed (17317006) |
| author2 | Yassine Himeur (14158821) Iraklis Varlamis (9288743) Faycal Bensaali (12427401) |
| author2_role | author author author |
| author_facet | Aya Nabil Sayed (17317006) Yassine Himeur (14158821) Iraklis Varlamis (9288743) Faycal Bensaali (12427401) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aya Nabil Sayed (17317006) Yassine Himeur (14158821) Iraklis Varlamis (9288743) Faycal Bensaali (12427401) |
| dc.date.none.fl_str_mv | 2025-02-10T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.apenergy.2025.125458 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Continual_learning_for_energy_management_systems_A_review_of_methods_and_applications_and_a_case_study/28425011 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Continual learning Lifelong learning Deep learning Catastrophic forgetting Energy management systems Non-intrusive load monitoring Demand-side management Fault/anomaly detection Load forecasting Renewable energy integration |
| dc.title.none.fl_str_mv | Continual learning for energy management systems: A review of methods and applications, and a case study |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>An intelligent system must incrementally acquire, update, accumulate, and exploit knowledge to navigate the real world’s intricacies. This trait is frequently referred to as Continual Learning (CL), and it can be limited by catastrophic forgetting, a phenomenon in which learning a new task acutely reduces the system’s performance on prior tasks. Numerous strategies have been developed to address this issue, as CL is essential for developing Artificial Intelligence (AI) systems that adapt to dynamic environments. This study examines the practical applications of CL, concentrating on energy management systems and their integration with Deep Learning (DL) models. Energy management systems are strategies and methods for monitoring, controlling, and optimizing energy use within a system or organization. The literature is systematically analyzed, highlighting methods such as replay techniques, regularization strategies, and architectural adaptations that address the challenges of catastrophic forgetting. Moreover, the review encompasses various energy-related applications, including non-intrusive load monitoring, demand-side management, fault/anomaly detection, load forecasting/prediction, and renewable energy integration. Additionally, a case study on anomaly detection in energy systems is conducted, comparing different CL approaches. The case study findings aim to bridge the gap between theoretical advancements and real-world applications, providing insights and guidelines for implementing CL in diverse fields. Finally, this survey identifies key challenges that impede the deployment of CL and suggests potential directions to enhance its implementation in the energy management sector.</p><h2>Other Information</h2> <p> Published in: Applied Energy<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.apenergy.2025.125458" target="_blank">https://dx.doi.org/10.1016/j.apenergy.2025.125458</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_36d3dda8fb791e78e730d14b13dd9f7b |
| identifier_str_mv | 10.1016/j.apenergy.2025.125458 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28425011 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Continual learning for energy management systems: A review of methods and applications, and a case studyAya Nabil Sayed (17317006)Yassine Himeur (14158821)Iraklis Varlamis (9288743)Faycal Bensaali (12427401)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceMachine learningContinual learningLifelong learningDeep learningCatastrophic forgettingEnergy management systemsNon-intrusive load monitoringDemand-side managementFault/anomaly detectionLoad forecastingRenewable energy integration<p>An intelligent system must incrementally acquire, update, accumulate, and exploit knowledge to navigate the real world’s intricacies. This trait is frequently referred to as Continual Learning (CL), and it can be limited by catastrophic forgetting, a phenomenon in which learning a new task acutely reduces the system’s performance on prior tasks. Numerous strategies have been developed to address this issue, as CL is essential for developing Artificial Intelligence (AI) systems that adapt to dynamic environments. This study examines the practical applications of CL, concentrating on energy management systems and their integration with Deep Learning (DL) models. Energy management systems are strategies and methods for monitoring, controlling, and optimizing energy use within a system or organization. The literature is systematically analyzed, highlighting methods such as replay techniques, regularization strategies, and architectural adaptations that address the challenges of catastrophic forgetting. Moreover, the review encompasses various energy-related applications, including non-intrusive load monitoring, demand-side management, fault/anomaly detection, load forecasting/prediction, and renewable energy integration. Additionally, a case study on anomaly detection in energy systems is conducted, comparing different CL approaches. The case study findings aim to bridge the gap between theoretical advancements and real-world applications, providing insights and guidelines for implementing CL in diverse fields. Finally, this survey identifies key challenges that impede the deployment of CL and suggests potential directions to enhance its implementation in the energy management sector.</p><h2>Other Information</h2> <p> Published in: Applied Energy<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.apenergy.2025.125458" target="_blank">https://dx.doi.org/10.1016/j.apenergy.2025.125458</a></p>2025-02-10T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.apenergy.2025.125458https://figshare.com/articles/journal_contribution/Continual_learning_for_energy_management_systems_A_review_of_methods_and_applications_and_a_case_study/28425011CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284250112025-02-10T12:00:00Z |
| spellingShingle | Continual learning for energy management systems: A review of methods and applications, and a case study Aya Nabil Sayed (17317006) Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Continual learning Lifelong learning Deep learning Catastrophic forgetting Energy management systems Non-intrusive load monitoring Demand-side management Fault/anomaly detection Load forecasting Renewable energy integration |
| status_str | publishedVersion |
| title | Continual learning for energy management systems: A review of methods and applications, and a case study |
| title_full | Continual learning for energy management systems: A review of methods and applications, and a case study |
| title_fullStr | Continual learning for energy management systems: A review of methods and applications, and a case study |
| title_full_unstemmed | Continual learning for energy management systems: A review of methods and applications, and a case study |
| title_short | Continual learning for energy management systems: A review of methods and applications, and a case study |
| title_sort | Continual learning for energy management systems: A review of methods and applications, and a case study |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Continual learning Lifelong learning Deep learning Catastrophic forgetting Energy management systems Non-intrusive load monitoring Demand-side management Fault/anomaly detection Load forecasting Renewable energy integration |