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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