Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

• Published in: International journal of intelligent systems Vol. 36; no. 6; pp. 2865 - 2894
• Main Authors: Himeur, Yassine, Alsalemi, Abdullah, Bensaali, Faycal, Amira, Abbes
• Format: Journal Article
• Language: English
• Published: New York Hindawi Limited 01.06.2021
• Subjects: Abnormalities; Algorithms; Anomalies; anomaly detection; Buildings; Datasets; Energy consumption; improved K‐nearest neighbors; Intelligent systems; Machine learning; micromoments; one‐class support vector machine; Power consumption; rule‐based algorithm; Support vector machines
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1002/int.22404

Anomaly 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.