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المؤلفHimeur, Yassine
المؤلفGhanem, Khalida
المؤلفAlsalemi, Abdullah
المؤلفBensaali, Faycal
المؤلفAmira, Abbes
تاريخ الإتاحة2022-12-29T07:34:45Z
تاريخ النشر2021
اسم المنشورApplied Energy
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.apenergy.2021.116601
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37841
الملخصEnormous 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. 2021 The Authors
راعي المشروعThis paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library
اللغةen
الناشرElsevier
الموضوعAnomaly detection
Deep abnormality detection
Energy consumption in buildings
Energy saving
Machine learning
العنوانArtificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
النوعArticle Review
رقم المجلد287
dc.accessType Open Access


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