A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries
<p dir="ltr">According to statistics, developing countries all over the world have suffered significant non-technical losses (NTLs) both in natural gas and electricity distribution. NTLs are thought of as energy that is consumed but not billed e.g., theft, meter tampering, meter reve...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2021
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513560294457344 |
|---|---|
| author | Ammar Yousaf Kharal (16891470) |
| author2 | Hassan Abdullah Khalid (16891473) Adel Gastli (14151273) Josep M. Guerrero (3743698) |
| author2_role | author author author |
| author_facet | Ammar Yousaf Kharal (16891470) Hassan Abdullah Khalid (16891473) Adel Gastli (14151273) Josep M. Guerrero (3743698) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ammar Yousaf Kharal (16891470) Hassan Abdullah Khalid (16891473) Adel Gastli (14151273) Josep M. Guerrero (3743698) |
| dc.date.none.fl_str_mv | 2021-06-02T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3085501 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Novel_Features-Based_Multivariate_Gaussian_Distribution_Method_for_the_Fraudulent_Consumers_Detection_in_the_Power_Utilities_of_Developing_Countries/24042441 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Resources engineering and extractive metallurgy Information and computing sciences Data management and data science Machine learning Developing countries Meters Companies Tariffs Gaussian distribution Smart meters Feature extraction |
| dc.title.none.fl_str_mv | A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">According to statistics, developing countries all over the world have suffered significant non-technical losses (NTLs) both in natural gas and electricity distribution. NTLs are thought of as energy that is consumed but not billed e.g., theft, meter tampering, meter reversing, etc. The adaptation of smart metering technology has enabled much of the developed world to significantly reduce their NTLs. Also, the recent advancements in machine learning and data analytics have enabled a further reduction in these losses. However, these solutions are not directly applicable to developing countries because of their infrastructure and manual data collection. This paper proposes a tailored solution based on machine learning to mitigate NTLs in developing countries. The proposed method is based on a multivariate Gaussian distribution framework to identify fraudulent consumers. It integrates novel features like social class stratification and the weather profile of an area. Thus, achieving a significant improvement in fraudulent consumer detection. This study has been done on a real dataset of consumers provided by the local power distribution companies that have been cross-validated by onsite inspection. The obtained results successfully identify fraudulent consumers with a maximum success rate of 75%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3085501" target="_blank">https://dx.doi.org/10.1109/access.2021.3085501</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_487b99526cf20fe960a5182ea052785b |
| identifier_str_mv | 10.1109/access.2021.3085501 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24042441 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing CountriesAmmar Yousaf Kharal (16891470)Hassan Abdullah Khalid (16891473)Adel Gastli (14151273)Josep M. Guerrero (3743698)EngineeringElectrical engineeringResources engineering and extractive metallurgyInformation and computing sciencesData management and data scienceMachine learningDeveloping countriesMetersCompaniesTariffsGaussian distributionSmart metersFeature extraction<p dir="ltr">According to statistics, developing countries all over the world have suffered significant non-technical losses (NTLs) both in natural gas and electricity distribution. NTLs are thought of as energy that is consumed but not billed e.g., theft, meter tampering, meter reversing, etc. The adaptation of smart metering technology has enabled much of the developed world to significantly reduce their NTLs. Also, the recent advancements in machine learning and data analytics have enabled a further reduction in these losses. However, these solutions are not directly applicable to developing countries because of their infrastructure and manual data collection. This paper proposes a tailored solution based on machine learning to mitigate NTLs in developing countries. The proposed method is based on a multivariate Gaussian distribution framework to identify fraudulent consumers. It integrates novel features like social class stratification and the weather profile of an area. Thus, achieving a significant improvement in fraudulent consumer detection. This study has been done on a real dataset of consumers provided by the local power distribution companies that have been cross-validated by onsite inspection. The obtained results successfully identify fraudulent consumers with a maximum success rate of 75%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3085501" target="_blank">https://dx.doi.org/10.1109/access.2021.3085501</a></p>2021-06-02T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3085501https://figshare.com/articles/journal_contribution/A_Novel_Features-Based_Multivariate_Gaussian_Distribution_Method_for_the_Fraudulent_Consumers_Detection_in_the_Power_Utilities_of_Developing_Countries/24042441CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240424412021-06-02T00:00:00Z |
| spellingShingle | A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries Ammar Yousaf Kharal (16891470) Engineering Electrical engineering Resources engineering and extractive metallurgy Information and computing sciences Data management and data science Machine learning Developing countries Meters Companies Tariffs Gaussian distribution Smart meters Feature extraction |
| status_str | publishedVersion |
| title | A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries |
| title_full | A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries |
| title_fullStr | A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries |
| title_full_unstemmed | A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries |
| title_short | A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries |
| title_sort | A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries |
| topic | Engineering Electrical engineering Resources engineering and extractive metallurgy Information and computing sciences Data management and data science Machine learning Developing countries Meters Companies Tariffs Gaussian distribution Smart meters Feature extraction |