[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119828-en":3,"doc-seo-119828-105":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},119828,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Photovoltaic energy prediction using machine learning techniques","Solar energy is becoming a major power source for residential, commercial, and industrial applications. In photovoltaic (PV) systems, PV cells convert solar irradiation into electricity, either as standalone supply or in grid-connected operation. A central challenge for PV integration is managing uncertainty and short-term fluctuations in energy production, which depends strongly on changing solar radiation. This paper analyzes and compares statistical time-series methods and machine learning techniques for forecasting photovoltaic power output for individual installations and regional network areas.","Photovoltaic energy prediction using machine learning techniques  \nGonzalo Surribas Sayago 1[0009−0004−6988−4825], Jose David  \nFern´andez-Rodr´ıguez 1[0000−0003−3702−2230], and Enrique  \nDominguez 1[0000−0002−2232−4562]  \nDept. of Computer Science, University of Malaga, 29071-Malaga, Spain {surribasg,josedavid,[enriqued](enriqued}@uma.es)[}](enriqued}@uma.es)[@uma.es](enriqued}@uma.es)  \nAbstract. Solar energy is becoming one of the most promising power sources in residential, commercial, and industrial applications. Solar photovoltaic (PV) facilities use PV cells that convert solar irradiation into electric power. PV cells can be used in either standalone or grid-connected systems to supply power for home appliances, lighting, and commercial and industrial equipment. Managing uncertainty and fluctuations in energy production is a key challenge in integrating PV systems into power grids and using them as steady, standalone power sources. For this reason, it is very important to forecast solar energy power output. In this paper, we analyze and compare various methods to predict the production of photovoltaic energy for individual installations and network areas around the world, using statistical methods for time series and different machine learning techniques.  \nKeywords: forecasting · photovoltaic energy · machine learning  \n1 Introduction  \nIn the last years, dramatic drops in the total cost of ownership for many types of renewable energy power generation have translated into significantly increased rates of installed power generation, both standalone and connected to the power grid. In this regard, solar energy has grown enormously, and it is considered to still have a considerable growth potential, as more and more solar power is installed to help meet energy demands at a worldwide scale [1] . However, solar energy comes with serious challenges: its maximum power output is very susceptible to the amount of solar radiation reaching the solar panels’ availability. As both power grids and standalone facilities require electric power flows to beas steady as possible, accurate forecasts of available solar radiation become very important for managing solar facilities. In the case of commercial operators directly selling their output into the electricity market, accurate predictions are even more relevant, as their profit margins can be significantly affected by inaccuracies in the predictions [4] . Numerous approaches have been proposed in the literature to predict the availability of solar radiation [5] . Most are based on simple, empirical mathematical models that are easy to compute. These  \n2 Gonzalo Surribas Sayago et al.  \nFig. 1. Elements of a time series (Series, Trend, Seasonal, Irregular)  \nare widely regarded in the industry as valuable heuristics to predict average daily global solar radiation. Nevertheless, these simple models cannot accurately predict short-term solar radiation availability, as localized and rapid changes in weather conditions (such as cloud cover, intermittent rain, etc.) can significantly impact this availability. Furthermore, these models have been shown tobe unable to reflect the complex and nonlinear relationships among dependent and independent variables in humid regions where solar radiation is strongly affected by heavy clouds throughout rainy days [2] . In this work, we propose several approaches based on machine learning to predict short-term solar radiation availability. The proposed techniques are analyzed, and their performance is compared using a common dataset.  \n2 Dataset and time series  \nThe dataset used in this work has been provided by the SunLab platform [7], a collection of on-field PV laboratories installed throughout Portugal with the goal of characterizing the relative performance of various PV technologies. SunLab was set up by Energias De Portugal (EDP), a Portuguese power generation company, in order to support its business units in the acquisition of knowledge in the ","cbCaiuMC69BP2dPj","https://ap.wps.com/l/cbCaiuMC69BP2dPj","pdf",883700,1,12,"English","en",105,"# Abstract\n# Introduction\n# Dataset and time series\n## Time series elements\n## Autoregressive models","[{\"question\":\"Why is forecasting photovoltaic energy output important?\",\"answer\":\"Accurate forecasts are needed to manage uncertainty and fluctuations in PV production when integrating PV systems into power grids and to support stable standalone power supply.\"},{\"question\":\"What data source is used for the experiments?\",\"answer\":\"The study uses datasets provided by the SunLab platform, consisting of on-field PV laboratory data collected over multiple years in Portugal.\"},{\"question\":\"How do the authors approach the forecasting problem?\",\"answer\":\"They compare statistical time-series methods with several machine learning techniques to predict short-term solar radiation availability and PV-related power output.\"}]","Photovoltaic energy prediction using machine learning techniques | 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is forecasting photovoltaic energy output important?","Question",{"text":75,"@type":76},"Accurate forecasts are needed to manage uncertainty and fluctuations in PV production when integrating PV systems into power grids and to support stable standalone power supply.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source is used for the experiments?",{"text":80,"@type":76},"The study uses datasets provided by the SunLab platform, consisting of on-field PV laboratory data collected over multiple years in Portugal.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the authors approach the forecasting problem?",{"text":84,"@type":76},"They compare statistical time-series methods with several machine learning techniques to predict short-term solar radiation availability and PV-related power 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