[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124926-en":3,"doc-seo-124926-105":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},124926,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Sky Images for Short-Term Solar Irradiance Forecast - A Comparative Study of Linear Machine Learning Models","Accurate short-term solar irradiance forecasting is essential for reliable operation of electrical grids with growing photovoltaic integration. This study compares seven linear machine learning models for forecasting short-term irradiance using sky image–derived features as exogenous inputs. Features are reconstructed from a representative three-year dataset of 1-minute sky images (2014–2016) and combined with clear-sky indices to predict ground-level solar radiance up to 30 minutes ahead. Results evaluate model accuracy across multiple forecast horizons, reporting low average errors and execution times under 7 s, and identifying avenues for improvement via cloud detection.","Sky Images for Short-Term Solar Irradiance Forecast  \nCitation for published version (APA):  \nShirazi, E. , Gordon, I. , Reinders, A. H. M. E. , & Catthoor, F. V. M. (2024) . Sky Images for Short-Term Solar Irradiance Forecast: A Comparative Study of Linear Machine Learning Models. IEEE Journal of Photovoltaics , 14(4), 691-698 . [https://doi.org/10.1109/JPHOTOV.2024.3398365](https://doi.org/10.1109/JPHOTOV.2024.3398365)  \nDocument license:  \nTAVERNE  \nDOI:  \n10.1109/JPHOTOV.2024.3398365  \nDocument status and date:  \nPublished: 01/07/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. 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Jun. 2025  \nSky Images for Short-Term Solar Irradiance Forecast: A Comparative Study of Linear Machine  \nLearning Models  \nElham Shirazi , Member, IEEE, Ivan Gordon, Angele Reinders , Senior Member, IEEE, and Francky Catthoor  \nAbstract—An accurate solar irradiance forecast is critical to the reliable operation of electrical grids with increasing integration of photovoltaic systems. This study compares short-term solar irradiance forecasts based on sky images using seven different linear machine learning algorithms. In the first step, several features are extracted from sky images, reconstructed, and next used as exogenous inputs to seven machine learning algorithms, i.e., linear regression, least absolute shrinkage and selection operator (Lasso) regression, ridge regression, Bayesian ridge (BR) regression, stochastic gradient descent (SGD), generalized linear model (GLM) regression, and random sample consensus (RANSAC). A representative dataset of three years of sky images with 1-minute resolution from 2014 to 2016 serves for comparison together with the clear sky indexes as inputs to forecast ground-level solar radiances for up to30minutes ahead. The results ofthe abovementioned algorithms are compared, where for 5 and 10 minutes ahead, Lasso has the highest accuracy with a root-mean-square error (RMSE) of 0.05 and 0.062 kW/m2 , while for 15 to 30 minutes ahead, stochastic gradient descent provides the most accurateforecast with an RMSE of 0.067, 0.071, 0.074, and 0.076 kW/m2 for 15, 20, 25, and 30 minutes ahead horizons, respectively. For all the time horizons, Bayesian ridge ","cbCaifce6IE8UWyv","https://ap.wps.com/l/cbCaifce6IE8UWyv","pdf",2007343,1,9,"English","en",105,"# Introduction\n## Related Work and Motivation\n# Methodology\n## Feature Extraction from Sky Images\n## Linear Machine Learning Models and Inputs\n# Experimental Setup\n## Dataset Description and Forecast Horizons\n# Results and Discussion\n## Model Accuracy Across Horizons\n## Computational Overhead and Practical Considerations\n# Conclusion and Outlook","[{\"question\":\"How are sky images used to forecast short-term solar irradiance in this study?\",\"answer\":\"Sky images are processed to extract and reconstruct features, which are then used as exogenous inputs to linear machine learning models. Clear-sky indices are included to forecast ground-level solar radiance up to 30 minutes ahead.\"},{\"question\":\"Which linear models perform best at different forecast horizons?\",\"answer\":\"For 5 and 10 minutes ahead, Lasso regression achieves the highest accuracy. For 15 to 30 minutes ahead, stochastic gradient descent provides the most accurate forecasts, while Bayesian ridge remains among the top models across horizons.\"},{\"question\":\"What is the reported accuracy and computational overhead of the forecasting approach?\",\"answer\":\"The models achieve relatively low average instantaneous errors ranging from 0.05 to 0.1 kW/m2 depending on horizon and model. Execution time overhead is kept low, reported as less than 7 seconds.\"}]","Sky Images for Short-Term Solar Irradiance Forecast - A Comparative Study of Linear Machine Learning Models | PDF",1785895421,23,{"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},"sky-images-for-short-term-solar-irradiance-forecast-a-comparative-study-of-linear-machine-learning-models","",{"@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/sky-images-for-short-term-solar-irradiance-forecast-a-comparative-study-of-linear-machine-learning-models/124926/",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","2026-08-05",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},"How are sky images used to forecast short-term solar irradiance in this study?","Question",{"text":75,"@type":76},"Sky images are processed to extract and reconstruct features, which are then used as exogenous inputs to linear machine learning models. Clear-sky indices are included to forecast ground-level solar radiance up to 30 minutes ahead.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which linear models perform best at different forecast horizons?",{"text":80,"@type":76},"For 5 and 10 minutes ahead, Lasso regression achieves the highest accuracy. For 15 to 30 minutes ahead, stochastic gradient descent provides the most accurate forecasts, while Bayesian ridge remains among the top models across horizons.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the reported accuracy and computational overhead of the forecasting approach?",{"text":84,"@type":76},"The models achieve relatively low average instantaneous errors ranging from 0.05 to 0.1 kW/m2 depending on horizon and model. Execution time overhead is kept low, reported as less than 7 seconds.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]