Show simple item record

AuthorHimeur, Yassine
AuthorAlsalemi, Abdullah
AuthorBensaali, Faycal
AuthorAmira, Abbes
Available date2022-12-29T07:34:41Z
Publication Date2021
Publication NameInternational Journal of Intelligent Systems
ResourceScopus
URIhttp://dx.doi.org/10.1002/int.22404
URIhttp://hdl.handle.net/10576/37797
AbstractAnomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end-users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one-class support vector machine, namely UAD-OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD-M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule-based algorithm is introduced to extract the micromoments representing the intent-rich moments, in which the end-users make decisions to consume energy; and (iii) an improved K-nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD-M2 achieves both a highest abnormality detection performance and real-time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real-world data set collected at the Qatar University energy lab. 2021 Wiley Periodicals LLC
SponsorThis 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.
Languageen
PublisherJohn Wiley and Sons Ltd
Subjectanomaly detection
energy consumption
improved K-nearest neighbors
micromoments
one-class support vector machine
rule-based algorithm
TitleSmart power consumption abnormality detection in buildings using micromoments and improved K-nearest neighbors
TypeArticle
Pagination2865-2894
Issue Number6
Volume Number36
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record