[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120613-en":3,"doc-seo-120613-105":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":20,"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},120613,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning-Based Prediction of Daily Solar Radiation to Support Renewable Energy Development in Coastal Regions","Reliable estimation of surface solar radiation is essential for climate analysis and solar energy planning, especially in data-limited coastal regions such as coastal Africa. The study examines long-term variability of surface solar radiation and assesses machine learning model performance using a comprehensive reanalysis dataset covering 1940–2024. Five radiation components are analyzed to quantify atmospheric attenuation from clouds, aerosols, and water vapor. Five ML algorithms are evaluated via train–test split, k-fold cross-validation, and leave-one-out validation. Results show strong multi-decadal variability and that clear-sky radiation consistently exceeds all-sky radiation. Linear regression achieves near-perfect predictive performance (R² ≈ 1.0) with the lowest errors, while ANN suffers from overfitting, and tree/boosting ensembles remain highly accurate, supporting interpretable, efficient solar resource estimation for resilient renewable energy planning.","Machine Learning–Based Prediction of Daily Solar Radiation to Support Renewable Energy Development in Coastal Regions  \nMfon D. Umoh, 1,* Udoh F. Evans, 1 Sunday A. Akpan,2 Sunday E. Otene,2 and Akinola B. Olanrewaju 1  \n1: Directorate of Strategy, Research and Development, Maritime Academy of Nigeria, Oron, Nigeria  \n2: Department of General Studies, Maritime Academy of Nigeria, Oron, Nigeria  \nReceived January 12, 2026; Accepted February 3, 2026; Published March 5, 2026  \nReliable estimation of surface solar radiation is essential for climate  \nanalysis and solar energy planning, particularly in data-limited regions  \nsuch as coastal Africa. This study investigates the long-term variability of  \nsurface solar radiation and evaluates the performance of machine  \nlearning models for its prediction using a comprehensive reanalysis  \ndataset spanning 1940–2024. Five radiation components—net surface  \nsolar radiation (SSR), clear-sky net radiation (SSRC), downward surface  \nsolar radiation (SSRD), clear-sky downward radiation (SSRDC), and total  \nsurface radiation (TSR)—were analyzed to quantify the influence of  \natmospheric attenuation caused by clouds, aerosols, and water vapor.  \nFive machine learning algorithms—Linear Regression (LR), Gradient  \nBoosting (GB), Random Forest (RF), k-Nearest Neighbours (KNN) , and  \nArtificial Neural Network (ANN)—were implemented and evaluated using  \ntrain–test split, k-fold cross-validation, and leave-one-out validation. The  \nresults reveal strong interannual and multi-decadal variability in solar  \nradiation, with clear-sky radiation consistently exceeding all-sky radiation,  \nconfirming the dominant role of atmospheric modulation in the region.  \nAmong the tested models, Linear Regression achieved near-perfect  \npredictive performance (R ² ≈ 1.0) with the lowest error statistics,  \nindicating that surface solar radiation over coastal Africa is largely  \ngoverned by linear radiative processes. Gradient Boosting and Random  \nForest also demonstrated high accuracy (R² > 0.98), while the Artificial  \nNeural Network showed poor generalization due to overfitting. The  \nfindings demonstrate that computationally efficient and physically  \ninterpretable machine learning models can reliably estimate long-term  \nsolar radiation in coastal Africa. This provides a robust scientific basis for  \nsolar resource assessment, photovoltaic system design, and climate  \nresilient renewable energy planning across the region.  \nKeywords: Solar radiation; Machine learning; Linear regression; Ensemble methods; Coastal Africa; Renewable energy prediction  \nIntroduction  \nA clean and dependable alternative to fossil fuels, solar energy is employed all around the world. Only about 5 × 104 EJ of the enormous amount of energy that the sun's beams send to Earth is readily harvestable (1 EJ = 1018J) [1] . About 70% of the 342 W/m2 of solar energy that enters the earth's atmosphere may be harvested, with the  \nremaining 30% being scattered or reflected back into space. In addition to other factors like sun orientation, aerosol, temperature, wind speed, direction, and many more, the earth's rotation around the sun causes variations in solar irradiance, making solar energy an unpredictable resource [1] . The energy that the Sun emits and that travels across space to reach Earth is referred to as solar radiation. It is made up of electromagnetic waves, such as infrared (IR) radiation, ultraviolet (UV) rays, and visible light. Only a small portion of the solar energy that the Sun emits in all directions makes it to the Earth's atmosphere. The Earth's surface eventually receives some of the solar energy [2] . The critical need for energy diversification is highlighted by the depletion of traditional energy resources like fossil fuels, oil, and gas, as well as rising pollution and the consequences of climate change. A sustainable answer to these urgent problems is provided by incorporating renewable energy sources into our ene","cbCaiq2brr1j9V0s","https://ap.wps.com/l/cbCaiq2brr1j9V0s","pdf",1202609,1,13,"English","en",105,"# Introduction\n## Background and motivation\n## Energy diversification and the role of solar forecasting\n## Machine learning methods for solar radiation prediction\n# Methodology and evaluation","[{\"question\":\"Why is accurate daily solar radiation prediction important in coastal regions?\",\"answer\":\"It supports climate analysis and solar energy planning where observational data are limited, enabling better solar resource assessment and system design.\"},{\"question\":\"Which radiation components are analyzed in the study?\",\"answer\":\"Net surface solar radiation (SSR), clear-sky net radiation (SSRC), downward surface solar radiation (SSRD), clear-sky downward radiation (SSRDC), and total surface radiation (TSR).\"},{\"question\":\"How do the tested machine learning models perform, and which one is best?\",\"answer\":\"Linear regression delivers near-perfect predictive performance (R² ≈ 1.0) with the lowest errors; gradient boosting and random forest also show high accuracy, while the artificial neural network generalizes poorly due to overfitting.\"}]","Machine Learning-Based Prediction of Daily Solar Radiation to Support Renewable Energy Development in Coastal Regions | PDF",1785730899,33,{"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},"machine-learning-based-prediction-of-daily-solar-radiation-to-support-renewable-energy-development-in-coastal-regions","",{"@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/machine-learning-based-prediction-of-daily-solar-radiation-to-support-renewable-energy-development-in-coastal-regions/120613/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is accurate daily solar radiation prediction important in coastal regions?","Question",{"text":75,"@type":76},"It supports climate analysis and solar energy planning where observational data are limited, enabling better solar resource assessment and system design.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which radiation components are analyzed in the study?",{"text":80,"@type":76},"Net surface solar radiation (SSR), clear-sky net radiation (SSRC), downward surface solar radiation (SSRD), clear-sky downward radiation (SSRDC), and total surface radiation (TSR).",{"name":82,"@type":73,"acceptedAnswer":83},"How do the tested machine learning models perform, and which one is best?",{"text":84,"@type":76},"Linear regression delivers near-perfect predictive performance (R² ≈ 1.0) with the lowest errors; gradient boosting and random forest also show high accuracy, while the artificial neural network generalizes poorly due to overfitting.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]