Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

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 anoma...

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Veröffentlicht in:	Applied energy Jg. 287; S. 116601
Hauptverfasser:	Himeur, Yassine, Ghanem, Khalida, Alsalemi, Abdullah, Bensaali, Faycal, Amira, Abbes
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.04.2021
Schlagworte:	Anomaly detection artificial intelligence data collection decision making Deep abnormality detection Energy consumption in buildings energy efficiency Energy saving energy use and consumption Machine learning surveys taxonomy Energy saving Energy consumption in buildings Anomaly detection Machine learning Deep abnormality detection
ISSN:	0306-2619, 1872-9118
Online-Zugang:	Volltext
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Abstract	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. •Anomaly detection of energy consumption in buildings.•Taxonomy of artificial intelligence based anomaly detection methods.•Current trends and new perspectives of anomaly detection.•Non-intrusive anomaly detection.•Multimodal anomaly detection visualization techniques.
AbstractList	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. •Anomaly detection of energy consumption in buildings.•Taxonomy of artificial intelligence based anomaly detection methods.•Current trends and new perspectives of anomaly detection.•Non-intrusive anomaly detection.•Multimodal anomaly detection visualization techniques. 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.
ArticleNumber	116601
Author	Ghanem, Khalida Amira, Abbes Himeur, Yassine Alsalemi, Abdullah Bensaali, Faycal
Author_xml	– sequence: 1 givenname: Yassine orcidid: 0000-0001-8904-5587 surname: Himeur fullname: Himeur, Yassine email: yassine.himeur@qu.edu.qa organization: Department of Electrical Engineering, Qatar University, Doha, Qatar – sequence: 2 givenname: Khalida surname: Ghanem fullname: Ghanem, Khalida email: kghanem@cdta.dz organization: Division Telecom, Center for Development of Advanced Technologies (CDTA), Algiers, Algeria – sequence: 3 givenname: Abdullah surname: Alsalemi fullname: Alsalemi, Abdullah email: a.alsalemi@qu.edu.qa organization: Department of Electrical Engineering, Qatar University, Doha, Qatar – sequence: 4 givenname: Faycal surname: Bensaali fullname: Bensaali, Faycal email: f.bensaali@qu.edu.qa organization: Department of Electrical Engineering, Qatar University, Doha, Qatar – sequence: 5 givenname: Abbes surname: Amira fullname: Amira, Abbes email: abbes.amira@dmu.ac.uk organization: Institute of Artificial Intelligence, De Montfort University, Leicester, United Kingdom
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Snippet	Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could...
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SubjectTerms	Anomaly detection artificial intelligence data collection decision making Deep abnormality detection Energy consumption in buildings energy efficiency Energy saving energy use and consumption Machine learning surveys taxonomy
Title	Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
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