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A transformer station shows overloading signs, and historical load data are used to quantify overloading magnitude and timing. Temperature load dependency is analyzed and four prominent consumers are selected. Multiple linear regression and support vector machines forecast load profiles during overload periods, then flexibility scenarios are created and compared with average-based baselines. Results identify three potential consumers and show machine learning models outperform average-based forecasting, enabling more realistic flexibility estimates for overloaded stations with limited consumer sets.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-machine-learning-application-for-the-energy-flexibility-assessment-of-a-distribution-network-for-consumers-a-step-by-step-assessment-approach/290972/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-machine-learning-application-for-the-energy-flexibility-assessment-of-a-distribution-network-for-consumers-a-step-by-step-assessment-approach/290972.png","ImageObject",300,407,{"name":92,"@type":93},"Seraphina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address in distribution networks?","Question",{"text":112,"@type":113},"It addresses transformer overloading and congestion in a distribution network by assessing residential consumers’ energy flexibility potential.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are consumers selected for the flexibility assessment?",{"text":117,"@type":113},"Four prominent consumers are chosen based on historical load data, followed by evaluation of temperature load dependency.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning methods are used and how are results validated?",{"text":121,"@type":113},"The study uses multiple linear regression and support vector machines to forecast load profiles during overload occurrences, then evaluates and compares generated flexibility models against forecasts based on average past-day loads.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290972,1789643071,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":31,"preview_page_count":4},962075114101,"https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165","energies   \nArticle  \nA Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers  \nJaka Rober 1, Leon Maruša 2 and Miloš Bekovi´c 1, *  \nCitation: Rober, J.; Maruša, L.; Bekovi´c, M. A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers. Energies 2023, 16, 6168. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/en16176168](10.3390/en16176168)  \nAcademic Editor: Abu-Siada Ahmed  \nReceived: 1 August 2023  \nRevised: 14 August 2023  \nAccepted: 21 August 2023  \nPublished: 24 August 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; [jaka.rober@student.um.si](jaka.rober@student.um.si)  \n2 Elektro Celje d.d, Vrunˇceva ulica 2a, 3000 Celje, Slovenia; [leon.marusa@elektro-celje.si](leon.marusa@elektro-celje.si)  \n* [Correspondence: milos.bekovic@um.si](Correspondence: milos.bekovic@um.si); Tel.: +386-2-220-7172  \nAbstract: This paper presents a step-by-step approach to assess the energy flexibility potential of residential consumers to manage congestion in the distribution network. A case study is presented where a selected transformer station exhibits signs of overloading. An analysis has been performed to evaluate the magnitude of the overloading and the timing of the overload occurrence based on their historical load data. Based on the historical load data, the four most prominent consumers have been chosen for the flexibility assessment. Temperature load dependency has been evaluated for the selected consumers. The paper's novel approach focuses on selecting individual consumers with the highest energy flexibility potential, and analysing their load patterns to address transformer overloading. To achieve this, machine learning algorithms, specifically, multiple linear regression and support vector machines, were used for load profile forecasting during the overload occurrences. Based on the forecast and measured load patterns, flexibility scenarios were created for each consumer. The generated models were evaluated and compared with the forecasting based on the average load of the past days. In the results, three potential consumers were identified who could resolve the transformer overloading problem. The machine learning models outperformed the average-based forecasting method, providing more realistic estimates of flexibility potential. The proposed approach can be applied to other overloaded transformer stations, but with a limited number of consumers.  \nKeywords: flexibility; baseline; demand response; distribution transformer; congestion management; power flow control; peak shaving; load shifting; predictive models; machine learning  \n1. Introduction  \nThe electric power system has undergone drastic changes in the last two decades. The distribution network (DN) has, specifically, been put under huge constraints because of the increasing number of distributed energy resources, such as solar and wind powerplants [1,2] . This is part of the European Green Deal programme that aims to make Europe carbon neutral and sustainable by 2050 with carbon-neutral electric energy production. The same programme also aims to decarbonise the transportation and housing sector through electrification. In such a way, the introduction of electric vehicles has been proposed as an alternative to gas-powered vehicles. However, integrating such high-intensity loads into the DN causes many problems. As was pointed out in [2], the stochastic load pattern of electric cars and ageing or inadequate infrastructure limits the integration ","cbCairIRA6XJOnQJ","https://ap.wps.com/l/cbCairIRA6XJOnQJ","pdf",9463819,"English","# Abstract\n# Introduction\n## Distribution network constraints and energy flexibility\n## Motivation from residential loads and infrastructure limits","[{\"question\":\"What problem does the paper address in distribution networks?\",\"answer\":\"It addresses transformer overloading and congestion in a distribution network by assessing residential consumers’ energy flexibility potential.\"},{\"question\":\"How are consumers selected for the flexibility assessment?\",\"answer\":\"Four prominent consumers are chosen based on historical load data, followed by evaluation of temperature load dependency.\"},{\"question\":\"Which machine learning methods are used and how are results validated?\",\"answer\":\"The study uses multiple linear regression and support vector machines to forecast load profiles during overload occurrences, then evaluates and compares generated flexibility models against forecasts based on average past-day loads.\"}]","A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers - A step-by-step assessment approach | PDF"]