[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291114-105":59,"doc-detail-291114-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","a-comparative-analysis-of-machine-learning-techniques-for-solar-power-forecasting","A Comparative Analysis of Machine Learning Techniques for Solar Power Forecasting","","Solar power generation relies heavily on unpredictable environmental factors, making accurate forecasting essential for grid integration. This analysis evaluates machine learning techniques for forecasting photovoltaic (PV) power output using multivariate weather data from 12 northern hemisphere sites, without solar irradiance data. It compares XGBoost, Random Forest, LightGBM, and a Stacking Regressor ensemble. Results show the stacking approach achieves the best test performance with RMSE 0.1370 and R-squared 0.637. The work highlights the importance of preprocessing, feature engineering, and ensemble optimization.",{"@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-comparative-analysis-of-machine-learning-techniques-for-solar-power-forecasting/291114/",{"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-comparative-analysis-of-machine-learning-techniques-for-solar-power-forecasting/291114.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","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 input data is used for solar PV power forecasting in this study?","Question",{"text":112,"@type":113},"The study forecasts PV power output using location and multivariate meteorological data from 12 northern hemisphere sites, without using measured solar irradiance values.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning models are compared?",{"text":117,"@type":113},"The analysis evaluates XGBoost Regression, Random Forest Regression, LightGBM, and a Stacking Regressor ensemble that combines the three tree-based models.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the stacking ensemble perform compared with the individual models?",{"text":121,"@type":113},"The Stacking Regressor achieves the best test-set performance, reporting RMSE of 0.1370 and R-squared of 0.637, attributed to leveraging complementary strengths of the component models.","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},291114,1789643495,{"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":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","A Comparative Analysis of Machine Learning Techniques for Solar Power Forecasting  \nKaran Sonawane  \nx22167005  \nAbstract  \nSolar power generation relies heavily on unpredictable environmental factors, making accurate forecasting essential for grid integration. This analysis evaluates machine learning techniques for forecasting photovoltaic (PV) power output using multivariate weather data from 12 northern hemisphere sites, without solar irradiance data that can have significant measurement errors. The methods examined include XGBoost Regression, Random Forest Regression, LightGBM, and an ensemble Stacking Regressor combining the models. The results demonstrate the Stacking Regressor achieves the best performance with an RMSE of 0.1370 and Rsquared of 0.637 on the test set by leveraging the strengths ofthe component models. The data preprocessing, feature engineering, and ensemble architecture optimization were critical to maximizing accuracy. Overall, the research provides empirical evidence that advanced machine learning, specifically a tailored stacking ensemble approach, can effectively forecast solar PV power utilizing meteorological data as inputs without needing irradiation values that require additional equipment. The framework demonstrates significant promise for integrating solar resources, with opportunities to enhance flexibility across locations through supplementary datasets.  \nKeywords: Solar power forecasting, Photovoltaic power prediction Machine Learning, Ensemble Learning,  \n1 Introduction  \nSolar power generation is rapidly expanding as a renewable energy source, but relies heavily on unpredictable environmental factors. Accurate forecasting of solar power production is therefore essential for integrating solar resources into the grid and planning the dispatch of complementary generating assets (Lave et al., 2015) [1] . The current analysis projects the power output from 12 northern hemisphere locations where horizontal photovoltaics have been installed. Irradiance, defined as the power per unit area received from the Sun in the form of electromagnetic radiation, is not used; only location and meteorological data are used. Although solar power output can be accurately predicted by irradiance, gathering this data at a site is frequently time-consuming and subject to large error rates. To avoid wasting time, money, or effort, more research must be done on the capability of predicting power output without irradiance data. This investigated the premise that avoided irradiation uncertainty  \ncombined with machine learning advancements could lead to precise power projection. Quantifying the effectiveness of this strategy was the work's goal (Pasion et al., 2020) [2] . Based on the type of photovoltaic panel and the following set of input variables, the methodology was applied in this work.  \nVarious machine learning techniques have shown promise for solar forecasting, but research is still ongoing into which performs best under different conditions. The machine learning methods examined are XGBoost Regressor, Random Forest Regressor, LightGBM Regressor, and a Stacking Regressor ensemble combining the above three models. These tree-based algorithms have shown promise for solar forecasting tasks, but research is still ongoing into which performs best under different conditions with multivariate weather data. This study provides a comparative analysis of the latest machine-learning approaches to address the research question: which machine learning algorithms provide the most accurate solar power forecasting?  \nThe paper is structured as follows: Section 2 provides background on solar forecasting-related work studied, and Section 3 describes the methodology including data collection, and model development. Section 4 presents techniques and architecture, Section 5 discusses practical implications and trade-offs for the techniques, Section 6 provides a comprehensive analysis of the results and main findings and Sectio","cbCaicHRqYK9gcXS","https://ap.wps.com/l/cbCaicHRqYK9gcXS","pdf",960647,14,"English","# 1 Introduction\n# 2 Related Work\n## Model comparison and performance insights\n# 3 Methodology\n## Data collection and model development\n# 4 Techniques and architecture\n# 5 Practical implications and trade-offs\n# 6 Results and main findings\n# 7 Conclusion and further research","[{\"question\":\"What input data is used for solar PV power forecasting in this study?\",\"answer\":\"The study forecasts PV power output using location and multivariate meteorological data from 12 northern hemisphere sites, without using measured solar irradiance values.\"},{\"question\":\"Which machine learning models are compared?\",\"answer\":\"The analysis evaluates XGBoost Regression, Random Forest Regression, LightGBM, and a Stacking Regressor ensemble that combines the three tree-based models.\"},{\"question\":\"How does the stacking ensemble perform compared with the individual models?\",\"answer\":\"The Stacking Regressor achieves the best test-set performance, reporting RMSE of 0.1370 and R-squared of 0.637, attributed to leveraging complementary strengths of the component models.\"}]","A Comparative Analysis of Machine Learning Techniques for Solar Power Forecasting | PDF",35]