[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119538-en":3,"doc-seo-119538-105":29,"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119538,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine learning techniques for solar energy generation prediction in photovoltaic systems","Accurate prediction of photovoltaic (PV) power output is essential for reliable and effective solar power operations under changing environmental conditions. The study reviews recent machine learning algorithms for forecasting solar energy, focusing on methodology, strengths, and limitations. Algorithm performance is assessed using metrics such as RMSE, MAE, and MSE for models including support vector machines, decision trees, and linear regression. Findings indicate improved prediction accuracy, reduced operational uncertainty, and better real-time decision support, while highlighting gaps for future research and development.","Machine learning techniques for solar energy generation prediction in photovoltaic systems  \nJ. Sumithra1, J. C. Vinitha2, M. J. Suganya3, M. Anuradha4, P. Sivakumar5, R. Balaji6  \n1Department of Electronics and Communication Engineering, R.M.D. Engineering College, Tamil Nadu, India 2Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Chennai, India 3Department of Electrical and Electronics Engineering, Panimalar Engineering College, Chennai, India 4Department of Computer Science and Engineering, S.A. Engineering College, Chennai, India 5Department of Mathematics, Panimalar Engineering College, Chennai, India 6Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,  \nSaveetha University, Chennai, Tamil Nadu, India  \n\n| Article history:\u003Cbr>Received Nov 23, 2024 Revised May 31, 2025 Accepted Jul 23, 2025 | For photovoltaic (PV) systems to be as effective and dependable as they possibly can be, it is vital to make an accurate prediction of the amount of power that will be generated by the sun. Using machine learning, it is now much simpler to forecast the amount of solar energy that will be generated. These approaches are more accurate and are able to adapt to the everchanging conditions of the nature of the environment. We take a look at the most recent machine learning algorithms for predicting solar energy and examine their methodology, as well as their strengths and drawbacks, in this paper. Using performance metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) makes it possible to evaluate important algorithms like support vector machines, decision trees, and linear regression. The results show that machine learning could help make predictions more accurate, lower the amount of uncertainty in operations, and help people make decisions in real time for PV systems. The study also points out important areas where research is lacking and suggests ways to move forward with the use of machine learning in systems that produce renewable energy.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Machine learning algorithms Performance metrics Photovoltaic systems Smart grid\u003Cbr>Solar energy |  |\n\nCorresponding Author:  \nM. J. Suganya  \nDepartment of Electrical and Electronics Engineering, Panimalar Engineering College Varadharajapuram, Poonamallee, Chennai 600123, India  \nEmail: [sugi.mj@gmail.com](sugi.mj@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThere has been a lot of research on energy production forecasting models since there is an increasing need for renewable energy sources, especially solar electricity. The fact that machine learning algorithms can effectively predict how much solar electricity will be generated by looking at complicated data patterns like weather, time of day, and location has made them more popular. We need these models if we wish to manage energy better, connect the grid better, and store energy in the best way possible. Machine learning has made solar energy systems far more efficient and reliable for a wide range of uses [1]-[5] .  \nIn the last few years, machine learning has made a lot of progress in the field of predicting solar energy. Several studies [5]-[10] have used machine learning to guess how much solar power will be generated. Researchers have been looking into how to utilize machine learning to predict how much energy smart buildings would use, with a focus on how well this method works for solar power systems. There has been a lot of research on how to predict electricity from solar photovoltaic (PV) systems. This research has  \nlooked at how well a number of different machine-learning algorithms work. Studies have shown that using methods like support vector machines and random forests can make solar power estimations more accurate. When the outside world is likely to change, it makes sense to t","cbCaicjVGRuQrmUg","https://ap.wps.com/l/cbCaicjVGRuQrmUg","pdf",685473,1,"English","en",105,"# Article Info\n## Abstract\n## Introduction\n## Related Work","[{\"question\":\"Why is solar power generation prediction important for photovoltaic (PV) systems?\",\"answer\":\"Accurate forecasts improve reliability and effectiveness by accounting for constantly changing environmental conditions. They also support real-time operational decisions and planning for PV power generation.\"},{\"question\":\"Which machine learning models and performance metrics are discussed for prediction?\",\"answer\":\"The paper evaluates algorithms such as support vector machines, decision trees, and linear regression using RMSE, MAE, and MSE. These metrics help quantify prediction accuracy and error behavior.\"},{\"question\":\"What benefits does machine learning offer compared with traditional approaches?\",\"answer\":\"Machine learning can produce more accurate and dependable predictions by learning complex patterns from data such as weather, time of day, and location. It also helps reduce uncertainty and address variability in solar output.\"}]","Machine learning techniques for solar energy generation prediction in photovoltaic systems | PDF",1785724840,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"machine-learning-techniques-for-solar-energy-generation-prediction-in-photovoltaic-systems","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-techniques-for-solar-energy-generation-prediction-in-photovoltaic-systems/119538/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is solar power generation prediction important for photovoltaic (PV) systems?","Question",{"text":75,"@type":76},"Accurate forecasts improve reliability and effectiveness by accounting for constantly changing environmental conditions. They also support real-time operational decisions and planning for PV power generation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models and performance metrics are discussed for prediction?",{"text":80,"@type":76},"The paper evaluates algorithms such as support vector machines, decision trees, and linear regression using RMSE, MAE, and MSE. These metrics help quantify prediction accuracy and error behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"What benefits does machine learning offer compared with traditional approaches?",{"text":84,"@type":76},"Machine learning can produce more accurate and dependable predictions by learning complex patterns from data such as weather, time of day, and location. 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