A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks
<p>Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-momen...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513567180455936 |
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| author | Yassine Himeur (14158821) |
| author2 | Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| author2_role | author author author |
| author_facet | Yassine Himeur (14158821) Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yassine Himeur (14158821) Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| dc.date.none.fl_str_mv | 2022-11-22T21:15:13Z |
| dc.identifier.none.fl_str_mv | 10.1007/s12559-020-09764-y |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Novel_Approach_for_Detecting_Anomalous_Energy_Consumption_Based_on_Micro-Moments_and_Deep_Neural_Networks/21597699 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Applied computing Psychology Biological psychology Energy consumption Micro-moments Deep neural network Anomalies detection Visualization Energy efficiency |
| dc.title.none.fl_str_mv | A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.</p><h2>Other Information</h2> <p> Published in: Cognitive Computation<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s12559-020-09764-y" target="_blank">http://dx.doi.org/10.1007/s12559-020-09764-y</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_aa265fd2e6f98742464e10e648faf765 |
| identifier_str_mv | 10.1007/s12559-020-09764-y |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597699 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural NetworksYassine Himeur (14158821)Abdullah Alsalemi (6951986)Faycal Bensaali (12427401)Abbes Amira (6952001)Information and computing sciencesApplied computingPsychologyBiological psychologyEnergy consumptionMicro-momentsDeep neural networkAnomalies detectionVisualizationEnergyefficiency<p>Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.</p><h2>Other Information</h2> <p> Published in: Cognitive Computation<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s12559-020-09764-y" target="_blank">http://dx.doi.org/10.1007/s12559-020-09764-y</a></p>2022-11-22T21:15:13ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s12559-020-09764-yhttps://figshare.com/articles/journal_contribution/A_Novel_Approach_for_Detecting_Anomalous_Energy_Consumption_Based_on_Micro-Moments_and_Deep_Neural_Networks/21597699CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215976992022-11-22T21:15:13Z |
| spellingShingle | A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks Yassine Himeur (14158821) Information and computing sciences Applied computing Psychology Biological psychology Energy consumption Micro-moments Deep neural network Anomalies detection Visualization Energy efficiency |
| status_str | publishedVersion |
| title | A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks |
| title_full | A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks |
| title_fullStr | A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks |
| title_full_unstemmed | A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks |
| title_short | A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks |
| title_sort | A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks |
| topic | Information and computing sciences Applied computing Psychology Biological psychology Energy consumption Micro-moments Deep neural network Anomalies detection Visualization Energy efficiency |