Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
<p dir="ltr">Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understan...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2021
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إضافة وسم
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| _version_ | 1864513561786580992 |
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| author | Yassine Himeur (14158821) |
| author2 | Khalida Ghanem (16931787) Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| author2_role | author author author author |
| author_facet | Yassine Himeur (14158821) Khalida Ghanem (16931787) Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yassine Himeur (14158821) Khalida Ghanem (16931787) Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| dc.date.none.fl_str_mv | 2021-04-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.apenergy.2021.116601 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Artificial_intelligence_based_anomaly_detection_of_energy_consumption_in_buildings_A_review_current_trends_and_new_perspectives/24083181 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Machine learning Energy consumption in buildings Anomaly detection Machine learning Deep abnormality detection Energy saving |
| dc.title.none.fl_str_mv | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors’ knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.</p><h2>Other Information</h2><p dir="ltr">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.2021.116601" target="_blank">https://dx.doi.org/10.1016/j.apenergy.2021.116601</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4bdb9960e105c17a16c8673571706660 |
| identifier_str_mv | 10.1016/j.apenergy.2021.116601 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24083181 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectivesYassine Himeur (14158821)Khalida Ghanem (16931787)Abdullah Alsalemi (6951986)Faycal Bensaali (12427401)Abbes Amira (6952001)EngineeringControl engineering, mechatronics and roboticsInformation and computing sciencesArtificial intelligenceMachine learningEnergy consumption in buildingsAnomaly detectionMachine learningDeep abnormality detectionEnergy saving<p dir="ltr">Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors’ knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.</p><h2>Other Information</h2><p dir="ltr">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.2021.116601" target="_blank">https://dx.doi.org/10.1016/j.apenergy.2021.116601</a></p>2021-04-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.apenergy.2021.116601https://figshare.com/articles/journal_contribution/Artificial_intelligence_based_anomaly_detection_of_energy_consumption_in_buildings_A_review_current_trends_and_new_perspectives/24083181CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240831812021-04-01T00:00:00Z |
| spellingShingle | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives Yassine Himeur (14158821) Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Machine learning Energy consumption in buildings Anomaly detection Machine learning Deep abnormality detection Energy saving |
| status_str | publishedVersion |
| title | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives |
| title_full | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives |
| title_fullStr | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives |
| title_full_unstemmed | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives |
| title_short | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives |
| title_sort | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives |
| topic | Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Machine learning Energy consumption in buildings Anomaly detection Machine learning Deep abnormality detection Energy saving |