[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122809-en":3,"doc-seo-122809-105":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},122809,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning - Article Abstract","Ahead-of-time forecasting of power plant output is crucial for electricity grid stability and uninterrupted service, yet predicting renewable energy is challenging because natural energy sources vary chaotically. The paper proposes a short-term solar irradiance estimation method using sky images, combining feature extraction with learning-based techniques. Evaluation on two public datasets (350,000+ images from 2004–2020) uses global horizontal irradiance (GHI) as ground truth, achieving competitive nowcasting and up-to-4-hour forecasting with substantially lower computational complexity than heavier state-of-the-art approaches.","arXiv :2310 . 17356v1 [ cs .CV] 26 Oct 2023  \nArticle  \nSky Imager-Based Forecast of Solar Irradiance Using Machine Learning  \nAnas Al-lahham 1, *, Obaidah Theeb 1, Khaled Elalem 1, Tariq A. Alshawi 1 and Saleh A. Alshebeili 1,2  \n1 Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia; [entheeb@outlook.com](entheeb@outlook.com) (O.T.); [Khaledalem71@gmail.com](Khaledalem71@gmail.com) (K.E.); [talshawi@ksu.edu.sa](talshawi@ksu.edu.sa) (T.A.A.); [dsaleh@ksu.edu.sa](dsaleh@ksu.edu.sa) (S.A.A.)  \n2 King Abdulaziz City for Science and Technology (KACST)-Technology Innovation Center (TIC) in Radio Frequency and Photonics (RFTONICS), King Saud University, Riyadh 11421, Saudi Arabia  \n* Correspondence: [anas.hkj@outlook.com](anas.hkj@outlook.com)  \nReceived: 11 September 2020; Accepted: 12 October 2020; Published: 16 October 2020  \n􀀁􀀂􀀃􀀁􀀄 􀀆􀀇􀀈  \n􀀁􀀂􀀃􀀄􀀅􀀆􀀇  \nAbstract: Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents anew approach to estimate short-term solar irradiance from sky images. The proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.  \nKeywords: global horizontal irradiance (GHI); photovoltaics (PV); solar energy; solar irradiance forecasting  \n1. Introduction  \nPhotovoltaic (PV) systems have attained a rapid increase in popularity and utilization to face the challenges of climate change and energy insecurity, as they bring a potential displacement for fossil fuel due to its merits of being pollution-free and its role of limiting global warming. However, the volatility and uncertainty of solar power resources are some of the main challenges that affect the PV power output, which, along with inaccurate forecasting, may impact the stability of the power grid [1,2] . Therefore, accurate irradiance forecasting may help power system operators to perform different actions in the grid operation, such as load following, scheduling of spinning reserves or unit commitment [3] .  \nPV power output mainly depends on the amount of solar irradiance on a collection plane. However, the amount of solar irradiance is affected by various weather conditions such as clouds and dust. Thus, solar irradiance may be prone to rapid fluctuations in various regions [4] . Various models have been proposed to forecast solar irradiance; these forecasting models are classified into parametric and statistical models. The main difference between these two models is the dependency on historical data; the parametric, physical or “white box” models do not need any historical data to generate the prediction of solar irradiance. They generate the prediction according to meteorological processes and weather conditions, such as cloud formation, wind, and temperature. The most well-known physical model is the numerical weather prediction (NWP), which, as the time horizon increases, offers greater accuracy over statistical models. Hybrid methods are also popular as they combine a mix of both models [5] .  \nSeveral physical and statistical methods have been proposed in the literature for solar irradiance forecasting. Larson et al. [6] proposed a methodology to generate a day-ahead power output forecast of two P","cbCaiviDmBGpvET6","https://ap.wps.com/l/cbCaiviDmBGpvET6","pdf",2895959,1,14,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Forecasting challenges for photovoltaic systems\n## Categorizing solar irradiance forecasting models\n## Prior work and datasets\n# Method overview","[{\"question\":\"Why is short-term solar irradiance forecasting important for photovoltaic power systems?\",\"answer\":\"Accurate irradiance forecasts help operators manage grid operations such as load following, scheduling spinning reserves, and unit commitment. Solar power volatility and uncertainty can otherwise reduce grid stability.\"},{\"question\":\"What data source does the proposed method use to estimate solar irradiance?\",\"answer\":\"The method estimates short-term solar irradiance from sky images. Extracted image features are used with learning-based techniques to predict irradiance.\"},{\"question\":\"How is model performance evaluated in the paper?\",\"answer\":\"Performance is evaluated using two publicly available sky-image datasets covering 2004–2020. Each image is labeled with global horizontal irradiance (GHI) as ground truth, enabling comparison against state-of-the-art approaches.\"}]","Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning - Article Abstract | PDF",1785813019,35,{"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-imager-based-forecast-of-solar-irradiance-using-machine-learning-article-abstract","",{"@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-imager-based-forecast-of-solar-irradiance-using-machine-learning-article-abstract/122809/",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","2026-08-04",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 short-term solar irradiance forecasting important for photovoltaic power systems?","Question",{"text":75,"@type":76},"Accurate irradiance forecasts help operators manage grid operations such as load following, scheduling spinning reserves, and unit commitment. Solar power volatility and uncertainty can otherwise reduce grid stability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source does the proposed method use to estimate solar irradiance?",{"text":80,"@type":76},"The method estimates short-term solar irradiance from sky images. Extracted image features are used with learning-based techniques to predict irradiance.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated in the paper?",{"text":84,"@type":76},"Performance is evaluated using two publicly available sky-image datasets covering 2004–2020. Each image is labeled with global horizontal irradiance (GHI) as ground truth, enabling comparison against state-of-the-art approaches.","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"]