[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128021-en":3,"doc-seo-128021-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128021,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning in Smart Grids - A Systematic Review, Novel Taxonomy, and Comparative Performance Evaluation","State-of-the-art research synthesizes machine learning methods and applications in smart grids for energy management prediction and optimisation. The study addresses reliability, security, efficiency, and real-time communication challenges, proposing a new taxonomy that classifies ML models by method and domain. It surveys techniques and key use cases including demand response, energy forecasting, fault detection, and grid optimisation, evaluating accuracy, interpretability, and computational efficiency. Limitations and future trends are also analysed to support practical deployment of efficient, dependable smart-grid systems.","PRO PUBLICO BONO – Public Administration, 2024/1, 53–83. • DOI: 10. 32575/ppb.2024.1.3  \nRituraj Rituraj – Dániel T. Várkonyi –Amir Mosavi – József Pap – Annamária R. Várkonyi-Kóczy – Csaba Makó  \nMACHINE LEARNING IN SMART GRIDS:  \nA SYSTEMATIC REVIEW, NOVEL TAXONOMY,  \nAND COMPARATIVE PERFORMANCE  \nEVALUATION  \nRituraj Rituraj, John von Neumann Faculty of Informatics, óbuda University Budapest, Hungary  \nDániel T. Várkonyi, Faculty of Informatics, Eötvös Loránd University Budapest, Hungary  \nAmir Mosavi, Research Fellow, Ludovika University of Public Service, Institute of the Information Society, [mosavi.amirhosein@uni-nke.hu](mosavi.amirhosein@uni-nke.hu)  \nJózsef Pap, Széchenyi University Doctoral School of Management (SzEEDSM), Győr, Hungary  \nAnnamária R. Várkonyi Kóczy, Professor, óbuda University, Institute of Software Design and Software Development, varkonyi-koczy@uni-obuda.hu  \nCsaba Makó, Professor, Ludovika University of Public Service, Institute of the Information Society, [mako.csaba@uni-nke.hu](mako.csaba@uni-nke.hu)  \nThis article presents a state-of-the-art review of machine learning (ML) methods and applications used in smart grids to predict and optimise energy management. The article discusses the challenges facing smart grids, and how ML can help address them, using a new taxonomy to categorise ML models by method and domain. It describes the different ML techniques used in smart grids as well as examining various smart grid use cases, including demand response, energy forecasting, fault detection, and grid optimisation, and explores how ML can improve these cases. The article proposes a new taxonomyfor categorising ML models and evaluates their performance based on accuracy, interpretability, and computational efficiency. Finally, it discusses some of the limitations and challenges of using ML in smart grid applications and attempts to predict future trends. Overall, the article highlights how ML can enable efficient and reliable smart grid systems.  \nS t u d i e s •  \nPRO PUBLIC O BONO – PUBLIC ADMINISTRATION • 2 0 2 4 / 1 53  \nS t u d i e s •  \nKeywords:  \nmachine learning, smart grids, artificial intelligence, big data, soft computing, data science  \nINTRODUCTION  \nThe smart grid (SG) is an upgraded type of electrical grid that improves reliability, security, and efficiency using advanced technology, facilitating real-time communication for managing power supply and demand. It promotes the integration of renewable energy sources and supports electric vehicles and distributed energy resources, reducing reliance on fossil fuels. It also enhances grid resilience and security, potentially transforming the electricity sector into a more sustainable and dependable energy system. In Ahmad et al.’s (2007) review,1 they highlight the unique challenges arising from the growing integration of energy storages and renewable energy sources into the conventional power systems and AI. This shift requires forward-thinking investmentsin SG technologies, integrating advanced measurement equipments, controllable transmission assets, and software control systems. Kwak and Heo (2007) stress the importance of creating a resilient and adaptable infrastructure capable of responding to both internal and external changes, given the intricate interconnectedness of modern infrastructure systems, which can magnify the impact of local disruptions into broader cascade failures.2 The vision for the SG, as presented by Bari et al. (2014),3 is of a profound transformation in the electric power sector. This transformation centres on the integration of bidirectional power and information flows, addressing critical factors like capacity, efficiency, reliability, sustainability, consumer engagement, and the evergrowing energy demand. It promotes a range of generation and storage solutions, and advocates for environmentally responsible practices.4 Ardito et al. (2013) add that the development of the SG entails enhancing the existing network ","cbCaiat4qnCeA1St","https://ap.wps.com/l/cbCaiat4qnCeA1St","pdf",2384430,6,1,31,"English","en",105,"# Introduction\n## Smart grid characteristics and challenges\n## Machine learning methods and taxonomy overview\n# ML techniques and use cases in smart grids\n## Demand response\n## Energy forecasting\n## Fault detection\n## Grid optimisation\n# Evaluation criteria and performance comparison\n## Accuracy\n## Interpretability\n## Computational efficiency\n# Limitations, challenges, and future trends","[{\"question\":\"What is the purpose of the proposed taxonomy for machine learning in smart grids?\",\"answer\":\"It categorises ML models by both method and domain, providing a structured way to organise approaches used in smart grid applications.\"},{\"question\":\"Which smart-grid use cases are covered in the review?\",\"answer\":\"The review examines demand response, energy forecasting, fault detection, and grid optimisation as representative smart grid tasks.\"},{\"question\":\"How are ML approaches in smart grids evaluated in the study?\",\"answer\":\"Performance is assessed using accuracy, interpretability, and computational efficiency to compare different techniques fairly.\"}]","Machine Learning in Smart Grids - A Systematic Review, Novel Taxonomy, and Comparative Performance Evaluation | PDF",1785944010,78,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-in-smart-grids-a-systematic-review-novel-taxonomy-and-comparative-performance-evaluation","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-in-smart-grids-a-systematic-review-novel-taxonomy-and-comparative-performance-evaluation/128021/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the purpose of the proposed taxonomy for machine learning in smart grids?","Question",{"text":77,"@type":78},"It categorises ML models by both method and domain, providing a structured way to organise approaches used in smart grid applications.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which smart-grid use cases are covered in the review?",{"text":82,"@type":78},"The review examines demand response, energy forecasting, fault detection, and grid optimisation as representative smart grid tasks.",{"name":84,"@type":75,"acceptedAnswer":85},"How are ML approaches in smart grids evaluated in the study?",{"text":86,"@type":78},"Performance is assessed using accuracy, interpretability, and computational efficiency to compare different techniques fairly.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]