Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection
As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing....
Uloženo v:
| Vydáno v: | Computers, materials & continua Ročník 74; číslo 3; s. 5431 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Henderson
Tech Science Press
01.01.2023
|
| Témata: | |
| ISSN: | 1546-2218, 1546-2226 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing, the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure, communication issues, ohmic losses, and energy burnout during transmission and propagation of energy are TLs. NTL’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft, along with tampering with AMI for bill reduction by fraudulent customers. This research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load profile. In our proposed methodology, a hybrid Genetic Algorithm and Support Vector Machine (GA-SVM) model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London, UK, for theft detection. A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%, compared to studies conducted on small and limited datasets. |
|---|---|
| AbstractList | As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing, the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure, communication issues, ohmic losses, and energy burnout during transmission and propagation of energy are TLs. NTL’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft, along with tampering with AMI for bill reduction by fraudulent customers. This research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load profile. In our proposed methodology, a hybrid Genetic Algorithm and Support Vector Machine (GA-SVM) model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London, UK, for theft detection. A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%, compared to studies conducted on small and limited datasets. |
| Author | Zubair, Muhammad Hameed, Hala Saeed, Faisal Umair, Muhammad Zafar Saeed Ishtiaq, Hiba |
| Author_xml | – sequence: 1 givenname: Muhammad surname: Umair fullname: Umair, Muhammad – sequence: 2 fullname: Zafar Saeed – sequence: 3 givenname: Faisal surname: Saeed fullname: Saeed, Faisal – sequence: 4 givenname: Hiba surname: Ishtiaq fullname: Ishtiaq, Hiba – sequence: 5 givenname: Muhammad surname: Zubair fullname: Zubair, Muhammad – sequence: 6 givenname: Hala surname: Hameed fullname: Hameed, Hala |
| BookMark | eNo9jcFrwjAYxcNwMHW77xjYud2XfmlMjs6pGwg72J0lTb9qpaYuTRn77yc4dnqPH7zfm7CR7zwx9iggxUyBfHYnl2aQYQqIWssbNha5VEmWZWr034W-Y5O-PwKgQgNjViw9hf0PLw5UR_5KkVxsOs8bz7cnGyJfh6bq-XcTD3xNnmLj-Lzdd-ECTsmL7aniK7JxCMS31F7X9-y2tm1PD385ZZ-rZbF4SzYf6_fFfJOchcaYCKgQAZ3OrZAVaSBlgbQsdVlpEKUyM3SoVKUwrxHlzJpckgFLDpRRGqfs6eo9h-5roD7ujt0Q_OVyh8IYjVJLwF_qHFNm |
| ContentType | Journal Article |
| Copyright | 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | 7SC 7SR 8BQ 8FD ABUWG AFKRA AZQEC BENPR CCPQU DWQXO JG9 JQ2 L7M L~C L~D PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS |
| DOI | 10.32604/cmc.2023.033884 |
| DatabaseName | Computer and Information Systems Abstracts Engineered Materials Abstracts METADEX Technology Research Database ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central ProQuest One ProQuest Central Korea Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China |
| DatabaseTitle | Publicly Available Content Database Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central China METADEX Computer and Information Systems Abstracts Professional ProQuest Central Engineered Materials Abstracts ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic Advanced Technologies Database with Aerospace ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1546-2226 |
| GroupedDBID | 7SC 7SR 8BQ 8FD AAFWJ ABUWG ACIWK ADMLS AFKRA ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR CCPQU DWQXO EBS EJD J9A JG9 JQ2 L7M L~C L~D OK1 P2P PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS RTS TUS |
| ID | FETCH-LOGICAL-p183t-10d3303c85a14de80e6a0e84b8bd801b6973c366d635f3347a954e90aec069683 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000992517400009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1546-2218 |
| IngestDate | Mon Jun 30 11:05:49 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-p183t-10d3303c85a14de80e6a0e84b8bd801b6973c366d635f3347a954e90aec069683 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://www.proquest.com/docview/3199834840?pq-origsite=%requestingapplication% |
| PQID | 3199834840 |
| PQPubID | 2048737 |
| ParticipantIDs | proquest_journals_3199834840 |
| PublicationCentury | 2000 |
| PublicationDate | 20230101 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – month: 01 year: 2023 text: 20230101 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Henderson |
| PublicationPlace_xml | – name: Henderson |
| PublicationTitle | Computers, materials & continua |
| PublicationYear | 2023 |
| Publisher | Tech Science Press |
| Publisher_xml | – name: Tech Science Press |
| SSID | ssj0036390 |
| Score | 2.3084438 |
| Snippet | As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| StartPage | 5431 |
| SubjectTerms | Advanced metering infrastructure Big Data Cloud computing Customers Data analysis Datasets Genetic algorithms Human influences Smart grid Support vector machines Theft |
| Title | Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection |
| URI | https://www.proquest.com/docview/3199834840 |
| Volume | 74 |
| WOSCitedRecordID | wos000992517400009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1546-2226 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0036390 issn: 1546-2218 databaseCode: BENPR dateStart: 20040101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1546-2226 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0036390 issn: 1546-2218 databaseCode: PIMPY dateStart: 20040101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwELagZWChPMWjIA-soW7sxM6EWmgBCaqIFlSmKrGdUommJUn5_ZxTR0gMLEwZrCSWfXff3dn3HUKXflsokTBpsjbSkGpzJ3C5dFyWCMWJ4l5JYPr6yAcDMR4HoU245fZaZWUTS0OtFtLkyFvUFINRBvHI9fLTMV2jzOmqbaGxieqGqQzkvN7tDcLnyhZTwN-yJNJjvuMCmq0PKsFlIawl54bC0KVXBMI0w276yxiXCNNv_Hduu2jH-pa4sxaGPbSh033UqPo2YKvGB2jUKwv-MIhIUuBbXZTXsVI8S_FwDqKE77KZyrFJ0WJDSw2fw52PKfyxeJ87XcA9hY3ruMo0HpZ9dODtQ_TS741u7h3bXcFZghoXYH8VBfySwovaTGlBtB8RLVgsYgWwFfsBp5L6vgKXJKGU8SjwmA5IpCUxjDr0CNXSRaqPEeYcgttYaRbFhGnuCp9oUGxJgwgilkicoGa1bhOrIvnkZ9FO_x4-Q9tmm9Z5jyaqFdlKn6Mt-VXM8uzC7jg8w4en8O0bowq1Ng |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1NTwIxEJ0omuhF_IwfqD3ocbVuy7Z7MAYFlAiEBDR6wt22KImsCIvGP-VvdLrsxsSDNw-eN9ukndc3nWnnDcCBdyK17HFlszbKimoLx3eFclzek1pQLYqJgOltXTSb8u7Ob83AZ1YLY59VZpyYELV-UTZHfsxsMRjjGI-cDV8d2zXK3q5mLTSmsLg2H-8Yso1Pa2W076HrViudiysn7SrgDBG-MfKOxhieKVkMTrg2khovoEbyUIYa6Tr0fMEU8zyNrrjHGBeBX-TGp4FR1CrJMBx3Fua4BXsO5lq1Rus-436G_j4pwSxyz3HRe04vRvGIRPmxGljJRJcdUQwLrZrqD_JPPFo1_9_WYhmW0rMzKU3BvgIzJlqFfNaXgqQ0tQadSlLQSHAL9GJSNnHy3Cwi_Yi0B7hVyOWor8fEpqCJld3G4Ujp-RFnGD8NnHP065rYo_FkZEg76ROEf6_DzZ_MbQNy0UtkNoEIgcF7qA0PQsqNcKVHDRKXYn6AEVkgt6CQ2ambUsC4-22k7d8_78PCVadR79ZrzesdWLQQmeZ4CpCLRxOzC_PqLe6PR3sp2gg8_LVRvwD7ow5S |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Energy+Theft+Detection+in+Smart+Grids+with+Genetic+Algorithm-Based+Feature+Selection&rft.jtitle=Computers%2C+materials+%26+continua&rft.au=Umair%2C+Muhammad&rft.au=Zafar+Saeed&rft.au=Saeed%2C+Faisal&rft.au=Ishtiaq%2C+Hiba&rft.date=2023-01-01&rft.pub=Tech+Science+Press&rft.issn=1546-2218&rft.eissn=1546-2226&rft.volume=74&rft.issue=3&rft.spage=5431&rft_id=info:doi/10.32604%2Fcmc.2023.033884 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1546-2218&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1546-2218&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1546-2218&client=summon |