Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors
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 informatio...
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| Published in: | International journal of intelligent systems Vol. 36; no. 6; pp. 2865 - 2894 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
John Wiley & Sons, Inc
01.06.2021
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| Subjects: | |
| ISSN: | 0884-8173, 1098-111X |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | 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: Qatar University – sequence: 2 givenname: Abdullah orcidid: 0000-0001-7574-4766 surname: Alsalemi fullname: Alsalemi, Abdullah organization: Qatar University – sequence: 3 givenname: Faycal orcidid: 0000-0002-9273-4735 surname: Bensaali fullname: Bensaali, Faycal organization: Qatar University – sequence: 4 givenname: Abbes orcidid: 0000-0003-1144-630X surname: Amira fullname: Amira, Abbes organization: De Montfort University |
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| SubjectTerms | 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 |
| Title | Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors |
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