[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119049-en":3,"doc-seo-119049-105":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},119049,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Power PV Forecasting using Machine Learning Algorithms Based on Weather Data in Semi-Arid Climate","Rising energy demand has accelerated adoption of renewable generation, and photovoltaic (PV) systems convert solar radiation into electricity to reduce reliance on conventional sources and lower carbon emissions. Effective PV scheduling depends on intelligent forecasting that improves cost and energy efficiency, reliability, power optimization, and smart-grid operation. Machine learning methods estimate PV output power by learning complex parameter relationships. This study evaluates CatBoost, LightGBM, XGBoost, and Random Forest using weather data for semi-arid conditions, finding that LightGBM improves prediction accuracy while reducing uncertainty and strengthening PV efficiency, reliability, and economic viability.","Power PV Forecasting using Machine Learning Algorithms Based on Weather Data in Semi-Arid Climate  \nMohamed boujoudar1,2*, Ibtissam Bouarfa2,3, Abdelmounaim Dadda2,4, Massaab Elydrissi1, Amine Moulay Taj², MounirAbraim², Hicham Ghennioui1 andEl Ghali Bennouna2  \n1 Laboratory of Signals, Systems, and Components, Sidi Mohamed Ben Abdellah University, Fez, Morocco.  \n2 Green Energy Park research platform (um6p/iresen), Ben Guerir, Morocco.  \n3 Laboratory of Innovative Technologies, Sidi Mohamed Abdellah University, Fez, Morocco.  \n4 Mohammed V University in Rabat, ERTE, ENSAM, Rabat, Morocco.  \nAbstract. As the energy demand continues to rise, renewable energy sources such as photovoltaic (PV) systems are becoming increasingly popular. PV systems convert solar radiation into electricity, making them an attractive option for reducing reliance on traditional electricity sources and decreasing carbon emissions. To optimize the usage of PV systems, intelligent forecasting algorithms are essential. They enable better decisionmaking regarding cost and energy efficiency, reliability, power optimization, and economic smart grid operations. Machine learning algorithms have proven to be effective in estimating the power of PV systems, improving accuracy by allowing models to understand complex relationships between parameters and evaluate the output power performance of photovoltaic cells. This work presents a study on the use of machine learning algorithms Catboost, LightGBM, XGboost and Random Forest to improve prediction. The study results indicate that using machine learning algorithms LightGBM can improve the accuracy of PV power prediction, which can have significant implications for optimizing energy usage. In addition to reducing uncertainty, machine learning algorithms improve PV systems' efficiency, reliability, and economic viability, making  \nthem more attractive as renewable energy sources.  \n1 Introduction  \nIn the 2020s, energy strategies have been focusing on ensuring that power systems are sustainable, reliable, and adaptable. The European Union's energy strategy places a significant emphasis on the role of PV technology in achieving climate objectives [1] . However, the rapid increase in PV installations, due to its dependency on weather conditions, presents grid management challenges for operators [2] . This has made the advancement of precise solar forecasting techniques a critical area of research, matching the importance and volume of studies.  \n* [Corresponding author: mohamed.boujoudar1@usmba.ac.ma](Corresponding author: mohamed.boujoudar1@usmba.ac.ma)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nForecasting methods of PV power output can be categorized into statistical methods and machine learning approaches. Statistical methods have become popular due to their simplicity in implementation, minimal data requirements, and lower computational costs compared to traditional methods. Initially, forecasting models for solar PV power relied exclusively on historical solar irradiance data, operating under the assumption that solar irradiance was the sole factor that influences PV system performance[3,4] . However, Recent interest has grown in using machine learning (ML) methods to predict PV power production. A thorough inspection of the literature reveals that various models that the use of ML have been applied for this purpose. For instance, [Scott et al.in](Scott et al.in) [5] present a comprehensive analysis of the utilization of machine learning algorithms for forecasting the power output of PV systems. The authors perform a detailed evaluation of several machine learning approaches, employing techniques such as Random Forest, Support Vector Machines, Neural Networks, and Linear Regression, to determine their efficacy","cbCaiizRCZCIs9Me","https://ap.wps.com/l/cbCaiizRCZCIs9Me","pdf",1197612,1,12,"English","en",105,"# Introduction\n## Forecasting approaches for PV power\n## Machine learning models in PV forecasting\n## Motivation and research gap\n## Study objective and scope","[{\"question\":\"Why is accurate PV power forecasting important for operators?\",\"answer\":\"PV installations are highly dependent on weather conditions, creating grid management challenges. Accurate forecasting supports better decisions for cost, energy efficiency, reliability, and smart-grid operation.\"},{\"question\":\"Which machine learning algorithms are evaluated in this study?\",\"answer\":\"CatBoost, LightGBM, XGBoost, and Random Forest are used to improve PV power prediction based on weather data in a semi-arid climate.\"},{\"question\":\"What conclusion does the study draw about forecasting accuracy?\",\"answer\":\"The results indicate that LightGBM delivers improved accuracy for PV power prediction, which helps reduce uncertainty and supports PV system efficiency and reliability.\"}]","Power PV Forecasting using Machine Learning Algorithms Based on Weather Data in Semi-Arid Climate | PDF",1785722086,30,{"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},"power-pv-forecasting-using-machine-learning-algorithms-based-on-weather-data-in-semi-arid-climate","",{"@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/power-pv-forecasting-using-machine-learning-algorithms-based-on-weather-data-in-semi-arid-climate/119049/",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","2026-08-03",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},"Why is accurate PV power forecasting important for operators?","Question",{"text":75,"@type":76},"PV installations are highly dependent on weather conditions, creating grid management challenges. Accurate forecasting supports better decisions for cost, energy efficiency, reliability, and smart-grid operation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are evaluated in this study?",{"text":80,"@type":76},"CatBoost, LightGBM, XGBoost, and Random Forest are used to improve PV power prediction based on weather data in a semi-arid climate.",{"name":82,"@type":73,"acceptedAnswer":83},"What conclusion does the study draw about forecasting accuracy?",{"text":84,"@type":76},"The results indicate that LightGBM delivers improved accuracy for PV power prediction, which helps reduce uncertainty and supports PV system efficiency and reliability.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"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"]