An ensemble learning framework for anomaly detection in building energy consumption

During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate ene...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Energy and buildings Ročník 144; s. 191 - 206
Hlavní autoři:	Araya, Daniel B., Grolinger, Katarina, ElYamany, Hany F., Capretz, Miriam A.M., Bitsuamlak, Girma
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Lausanne Elsevier B.V 01.06.2017 Elsevier BV
Témata:	Alarm systems Anomalies Anomaly detection Autoencoder Building automation Building energy consumption Classifiers Consumption patterns Energy conservation Energy consumption Energy efficiency Energy usage Ensemble learning Fault diagnosis Faults Random forest Sliding Smart buildings Support vector regression Canada Brampton Ontario Canada Support vector regression Random forest Building energy consumption Ensemble learning Autoencoder Anomaly detection
ISSN:	0378-7788, 1872-6178
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!