[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125775-en":3,"doc-seo-125775-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125775,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-based electricity theft detection using support vector machines - article","Electricity theft is a serious challenge for utilities, especially in developing regions where non-technical losses can form a large portion of overall loss. The document presents a machine learning approach for electricity theft detection using support vector machines with four kernel functions—polynomial, sigmoid, radial basis function (RBF), and linear kernels. By analyzing electricity consumption patterns from a dataset of a Pakistani utility company, the method predicts user categories and reports detection accuracies of 83%, 79%, 80%, and 76% respectively, supporting effective theft detection, improved revenue management, and service reliability.","Machine learning-based electricity theft detection using support  \nvector machines  \nSafdar Ali Abro1, Lyu Guang Hua2, Javed Ahmed Laghari3, Muhammad Akram Bhayo3,  \nAbdul Aziz Memon4  \n1Department of Electrical Engineering Technology, The Benazir Bhutto Shaheed University of Technology and Skill Development  \nKhairpur Mis, Khairpur, Pakistan  \n2Power China Huadong Engineering Corporation Limited, Hangzhou, China  \n3Department of Electrical Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan 4Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan  \nArticle history:  \nReceived May 25, 2023 Revised Oct 23, 2023 Accepted Nov 29, 2023  \nKeywords:  \nMachine learning Support vector machine Electricity theft detection sigmoid  \nPolynomial  \nRadial basis function Linear kernel function  \nCorresponding Author:  \nElectricity theft is a serious issue that many nations face, especially in developing areas where non-technical losses can make up a significant percentage of the overall losses sustained by utilities. Electricity theft detection (ETD) is a very challenging task because it frequently introduces irregularities in customer electricity consumption patterns. In recent times, machine learning (ML) techniques have been investigated as a potential solution for ETD. In this research, author propose electricity theft detection based on four kernel functions of support vector machines (SVM) . The proposed method analyzes the electricity consumption patterns and then predicts the category of the user. The kernel functions utilized includes polynomial, sigmoid, radial basis function (RBF) and linear kernel function. For experimentation and model training, a dataset of Pakistani utility company is used, which contains the electricity consumption information. The results highlight SVM method works well for accurate ETD. The detection accuracy of the various kernel functions of SVM is 83%, 79%, 80%, and 76% for RBF, polynomial, sigmoid, and linear kernel functions, respectively, demonstrating the effectiveness of the proposed SVM-based method for theft detection. By leveraging these ML-based methods, utility companies can strengthen their ability to detect and prevent electricity theft, leading to improved revenue management and dependability of services.  \nThis is an open access article under the CC BY-SA license.  \nSafdar Ali Abro  \nDepartment of Electrical Engineering Technology, Faculty of Engineering and Technology, The Benazir Bhutto Shaheed University of Technology and Skill Development Khairpur Mis  \n66020, Sindh, Pakistan  \nEmail: [safdar@bbsutsd.edu.pk](safdar@bbsutsd.edu.pk)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nElectricity theft is indeed a serious issue in many countries, and it has significant economic, social, and environmental implications. Traditional methods for detecting electricity theft often rely on manual inspections and periodic meter readings, which can be inefficient and may not effectively identify all instances of theft [1] . The World Bank estimates that theft of power costs the global economy $96 billion every year in lost income [2] . Electricity theft is especially common in developing countries, where nontechnical losses can make up a significant share of all utility losses [3] . Physical examination and manual meter reading are two outdated, expensive, time consuming and error-prone methods of detecting electricity theft [4] . As a result, there has been a growing interest in developing more advanced and technology-driven  \napproaches to address this problem. In recent times, machine learning (ML) techniques have been investigated as a potential solution for automatically detecting the electricity theft and it has shown the promising results. Machine learning algorithms can analyze large data sets, which can also identify patterns that might be signs of electricity theft [5], [6] . One of the commonly used machine learning algorithm i.e","cbCaikOR5EqCmCOs","https://ap.wps.com/l/cbCaikOR5EqCmCOs","pdf",485272,1,11,"English","en",105,"# Introduction\n## Electricity theft problem and limitations of traditional detection\n## Motivation for machine learning and SVM\n# Proposed SVM-based ETD approach\n## Kernel functions used\n## Dataset and experimentation\n# Method overview (SVM workflow)\n## Data collection\n## Feature extraction and preprocessing\n## Training and prediction","[{\"question\":\"Why is electricity theft detection difficult in utilities?\",\"answer\":\"It introduces irregularities in customer electricity consumption patterns, making detection challenging. Traditional approaches can miss cases and are inefficient and error-prone.\"},{\"question\":\"What model is used for electricity theft detection in this research?\",\"answer\":\"Support vector machines (SVM) with four kernel functions are used: polynomial, sigmoid, radial basis function (RBF), and linear kernels.\"},{\"question\":\"How accurate is the proposed method with different SVM kernels?\",\"answer\":\"Reported detection accuracies are 83% for RBF, 79% for polynomial, 80% for sigmoid, and 76% for the linear kernel function.\"}]","Machine learning-based electricity theft detection using support vector machines - article | PDF",1785901137,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-electricity-theft-detection-using-support-vector-machines-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-electricity-theft-detection-using-support-vector-machines-article/125775/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is electricity theft detection difficult in utilities?","Question",{"text":75,"@type":76},"It introduces irregularities in customer electricity consumption patterns, making detection challenging. Traditional approaches can miss cases and are inefficient and error-prone.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What model is used for electricity theft detection in this research?",{"text":80,"@type":76},"Support vector machines (SVM) with four kernel functions are used: polynomial, sigmoid, radial basis function (RBF), and linear kernels.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the proposed method with different SVM kernels?",{"text":84,"@type":76},"Reported detection accuracies are 83% for RBF, 79% for polynomial, 80% for sigmoid, and 76% for the linear kernel function.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]