[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123944-en":3,"doc-seo-123944-105":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},123944,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine Learning Approaches for Wind Speed Prediction - A Case Study in Ajman, UAE - Abstract","The study proposes a novel methodology to error-correct publicly available NASA wind data to improve accuracy for location-specific wind resource assessments in Ajman, UAE. The method combines maximum climate and environmental variables with data-driven techniques and machine learning algorithms to address inaccuracies inherent in satellite-derived observations. Error correction factors are established for satellite-derived wind speed data, increasing the reliability of wind speed inputs for sustainable wind resource assessment and power production forecasting. Results support decision-making and sustainability initiatives for wind energy stakeholders and government bodies.","Issue Topic: “Artificial Intelligence & Machine Learning in Computer Vision Applications “  \nMachine Learning Approaches for Wind Speed Prediction: A Case Study in Ajman, UAE  \nKais Muhammed Fasel University of Bolton School of Civil Engineering and Built Environment [kk1res@bolton.ac.uk](kk1res@bolton.ac.uk)  \nPeter Farrell University of Bolton School of Civil Engineering and Built Environment [p.farrell@bolton.ac.uk](p.farrell@bolton.ac.uk)  \nAbdul Salam Darwish University of Bolton School of Civil Engineering and Built Environment [a.darwish@bolton.ac.uk](a.darwish@bolton.ac.uk)  \nAbstract—This study presents a novel methodology for error-correcting publicly available NASA wind data and making it more accurate for location-specific wind resource assessments for the emirate of Ajman, UAE. The approach integrates maximum climate and environmental variables, data-driven techniques and machine learning algorithms, addressing the inherent errors in publicly available NASA satellite data. The study establishes error correction factors for satellite-derived wind speed data, enhancing the dependability of wind speed data for sustainable wind resource assessment and power production forecasting. The findings of this study have significant implications for wind energy industry stakeholders and the government for decision-making and sustainability initiatives.  \nKeywords—Machine learning, renewable energy, wind resource assessment, data analysis, meteorological variables, sustainability, Ajman, UAE  \nI. INTRODUCTION & BACKGROUND  \nThe impact of climate change has influenced the world to shift towards sustainable energy sources that have tremendously accelerated wind power development. Accurate wind speed prediction and resource assessment are crucial for successfully planning and managing wind energy projects. Machine learning (ML) techniques were tested for sitespecific wind speed prediction to enhance resource assessment, leveraging their ability to identify patterns, capture non-linear relationships, and handle large datasets [1, 2] . As per [3] various ML algorithms, namely the Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) algorithm [3] got promising results in wind speed prediction and resource assessment [4, 5, 6].These algorithms can utilise wind speed data, other climate parameters, and environmental parameters to develop predictive models for predicting future wind speeds and estimating the site’s wind energy.  \nHowever, the correctness of wind speed estimates mainly depends on bias-free availability of input data [7]  \nSatellite-derived wind speed data, such as those provided by NASA, offer a valuable resource for regions with limited ground-based measurements[8] . Nevertheless, satellitederived data often have lower accuracy than ground-based measurements due to the limitations of remote sensing  \ntechniques [9] . Correction Factor Analysis (CFA) is used to correct the errors and biases of NASA's publicly available satellite wind speed data [10] .  \nStarting from 2010, the Emirate of Ajman is in the forefront of striving to implement a renewable energy mix [11] . Given its coastal location and exposure to strong winds, wind energy presents a promising opportunity for diversifying Ajman's energy mix. However, successful wind energy development in Ajman requires site-specific accurate and reliable wind resource assessments that consider the region's unique environmental characteristics and challenges with data availability.  \nRecent studies by [12, 13] have highlighted the need for site-specific, error-corrected wind resource assessments in the emirate of Ajman. Building upon these findings, the current study aims to investigate the application of ML techniques for wind speed prediction and data correction to support the sitespecific wind resource assessment and development of sustainable wind energy systems in the Emirate.  \nII. OBJECTIVES OF THE STUDY The main objectives ofthis study ","cbCaieVXZfJcR1FL","https://ap.wps.com/l/cbCaieVXZfJcR1FL","pdf",487749,1,9,"English","en",105,"# Introduction & Background\n## Objectives of the Study\n# Literature Review","[{\"question\":\"What problem does the study address for wind energy in Ajman, UAE?\",\"answer\":\"The study addresses the need for accurate, site-specific wind speed prediction and wind resource assessment despite limitations and errors in publicly available satellite-derived data.\"},{\"question\":\"How does the methodology improve NASA satellite wind data quality?\",\"answer\":\"It applies Correction Factor Analysis (CFA) and error correction factors to reduce bias and errors in NASA-derived wind speed measurements for Ajman.\"},{\"question\":\"Which machine learning algorithms are evaluated in the study?\",\"answer\":\"The study evaluates Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) for wind speed prediction using wind data and meteorological variables.\"}]","Machine Learning Approaches for Wind Speed Prediction - A Case Study in Ajman, UAE - Abstract | PDF",1785819371,23,{"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},"machine-learning-approaches-for-wind-speed-prediction-a-case-study-in-ajman-uae-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/machine-learning-approaches-for-wind-speed-prediction-a-case-study-in-ajman-uae-abstract/123944/",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},"What problem does the study address for wind energy in Ajman, UAE?","Question",{"text":75,"@type":76},"The study addresses the need for accurate, site-specific wind speed prediction and wind resource assessment despite limitations and errors in publicly available satellite-derived data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the methodology improve NASA satellite wind data quality?",{"text":80,"@type":76},"It applies Correction Factor Analysis (CFA) and error correction factors to reduce bias and errors in NASA-derived wind speed measurements for Ajman.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms are evaluated in the study?",{"text":84,"@type":76},"The study evaluates Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) for wind speed prediction using wind data and meteorological variables.","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"]