[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117772-en":3,"doc-seo-117772-105":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},117772,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Solar Power Prediction Using Machine Learning","A machine-learning approach is presented to predict solar power generation with high accuracy, achieving 99% AUC. The pipeline spans data collection, preprocessing, feature selection, model selection, training, evaluation, and deployment. Weather data, solar irradiance, and historical generation records are cleaned through outlier removal, missing-value handling, and normalization. Temperature, humidity, wind speed, and irradiance are used as key features. Support Vector Machines, Random Forest, and Gradient Boosting are assessed with AUC plus precision, recall, and F1-score, then deployed for real-time forecasting to help operators reduce costs and improve efficiency.","SOLAR POWER PREDICTION USING  \nMACHINE LEARNING  \nE. Subramanian, M.Mithun Karthik ,G.Prem Krishna,V.Sugesh Kumar, D.Vaisnav Prasath  \nDepartment of Computer Science Sri Shakthi Institute of Engineering and Technology Coimbatore, India  \nABSTRACT  \nThis paper presents a machine learningbased approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, preprocessing, feature selection, modelselection, training, evaluation, and deployment. High-quality data from multiple sources, including weather data, solar irradiance data, and historical solar power generation data, are collected and pre-processed to remove outliers, handle missing values, and normalize the data. Relevant features such as temperature, humidity, wind speed, and solar irradiance are selected for model training. Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used as machine learning algorithms to produce accurate predictions. The models are trained on a large dataset of historical solar power generation data and other relevant features. The performance of the models is evaluated using AUC and other metrics such as precision, recall, and F1-score. The trained machine learning models are then deployed in a production environment, where they can be used to make real-time predictions about solar power generation. The results show that the proposed approach achieves a 99% AUC for solar power generation prediction, which can help energy companies better manage their solar power systems, reduce costs, and improve energy efficiency.  \nINTRODUCTION  \nThe added demand for renewable energy sources has led to a significant growth in solar power generation. Solar power generation systems are complex, and their operation depends on many factors such as rainfall conditions, solar irradiance, temperature, and moisture. Accurate valuation of solar power generation is pivotal for energy companies to balance supply and demand, reduce costs, and ameliorate energy effectiveness. Machinelearning-based approaches have shown promising results in directly prognosticating solar power generation. Still, achieving a high position of delicacy, similar to 99 AUC (Area Under the Wind) , requires a combination of data collection, preprocessing, point selection, model selection, training, evaluation, and deployment methods. This paper presents a machinelearning-based approach for prognosticating solar power generation with high delicacy using a 99 AUC metric. The approach includes collecting high-quality data from multiple sources, opting for applicable features, choosing applicable machine learning algorithms, and training the modelson a large dataset of literal solar power generation data and other applicable features. The performance of the models is estimated using AUC and other similar criteria such as perfection, recall, and F1-score. The trained machine-learning models are also stationed in product terrain, where they can be used to make real-time prognostications about solar power generation. The proposed approach can help energy companies better manage their solar  \npower systems, reduce costs, and improve energy effectiveness. To overcome these failings, accurate PV power ventilation is needed. Either way, it also could give a reference for power grid dispatching and operation of PV power stations, which is significant for security and profitable effectiveness (5) . PV power generation vaccinations can be distributed as ultrashort-term ( 1 h) or short-term machine literacy styles, similar to Artificial Neural Networks (ANNs) (13–15), Support Vector Machines (SVMs) (16–18), Multilayer Oerceptrons (MLPs) (19–21), which are the most effective ways for PV power soothsaying. In dealing with non-linear data, limitations of statistical methods due to variable meteorological factors have led to the operation of artificial neural networks for prognosticating PV power. This will i","cbCaieSEjhAAW31L","https://ap.wps.com/l/cbCaieSEjhAAW31L","pdf",448776,1,7,"English","en",105,"# Abstract\n# Introduction\n# Literature Survey","[{\"question\":\"What accuracy metric is used to evaluate the solar power prediction models?\",\"answer\":\"Models are evaluated using AUC (Area Under the Curve), achieving a reported 99% AUC for solar power generation prediction.\"},{\"question\":\"Which data sources and features are used for training?\",\"answer\":\"Training uses weather data, solar irradiance data, and historical solar power generation. Selected features include temperature, humidity, wind speed, and solar irradiance.\"},{\"question\":\"Which machine learning algorithms are compared in the proposed approach?\",\"answer\":\"Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used and assessed for accurate predictions.\"}]","Solar Power Prediction Using Machine Learning | PDF",1785679476,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"solar-power-prediction-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/solar-power-prediction-using-machine-learning/117772/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What accuracy metric is used to evaluate the solar power prediction models?","Question",{"text":75,"@type":76},"Models are evaluated using AUC (Area Under the Curve), achieving a reported 99% AUC for solar power generation prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data sources and features are used for training?",{"text":80,"@type":76},"Training uses weather data, solar irradiance data, and historical solar power generation. Selected features include temperature, humidity, wind speed, and solar irradiance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms are compared in the proposed approach?",{"text":84,"@type":76},"Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used and assessed for accurate predictions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]