[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125249-en":3,"doc-seo-125249-105":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},125249,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning - Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations","Renewable electricity expansion increases demand for accurate hub-height wind forecasts, yet numerical modeling errors grow because forecasts at these levels were historically uncommon and the nocturnal boundary-layer collapse plus daytime growth of the layer introduce complex dynamics. This study evaluates machine-learning methods to forecast and correct WRF Model winds and temperature at hub height across critical periods for blackouts and black start grid operations. Results show major day-2 wind MSE reduction via a multioutput neural network and accurate 60-min error-based adjustments using LSTM. Ten-minute nowcasting with LSTM yields very low error and strong skill for extreme wind-temperature values across the wind turbine plant area.","SEPTEMBER 2023 HUGE BA C K E T A L . 1553  \nMachine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black  \nStart Operations  \nKYLE K. HUGEBACK,a WILLIAM A. GALLUS JR.,a AND HUGO N. VILLEGAS PICOb  \na Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa  \nb Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa  \n(Manuscript received 7 February 2023, in final form 9 June 2023, accepted 13 June 2023)  \nABSTRACT: The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.  \nKEYWORDS: Forecasting techniques; Nowcasting; Short-range prediction; Model errors; Neural networks  \n1. Introduction  \nAccording to the Department of Energy (DOE), the wind energy industry in the United States added more than 13 gigawatts (GW) of generation capacity in 2021, with the national total rising to over 135 GW, equal to 9% of the nation’s total generation capacity. The states of Kansas, Oklahoma, and North Dakota now receive 30% of their energy needs from wind, while Iowa and South Dakota now get more than 50% of their electricity from wind generation (DOE 2022) . With the change to wind-dominant generation practices in different parts of the United States, accurate forecasting of such resources has become especially critical, with a need for increasingly fine resolution in the forecasts as wind can vary greatly in both space and time.  \nForecasting of wind is highly dependent on the diurnal cycle. Some hurdles to accurate forecasting include summertime convection, stratification of the planetary boundary layer (PBL) and the frequent occurrence of the nocturnal low-level jet (LLJ) . The frequency strength and duration of LLJ events varies seasonally (Weaver et al. 2009; Liang et al. 2015) . The height of the LLJ maximum wind speed is commonly below 500 m (e.g., Song et al. 2005; Shapiro et al. 2016; Smith et al. 2019) and in some cases will affect the rotor sweep area (Aird et al. 2021) . In a recent study looking at large eddy simulations of LLJs at different heights within the turbine rotor  \n Supplemental information related to this paper is available atthe Journals Online website: [https://doi.org/10.1175/WAF-D-23-](https://doi.org/10.1175/WAF-D-23-)[ ](https://doi.org/10.1175/WAF-D-23-)[0023.s1](0023.s1.)[.](0023.s1.)  \nCorresponding author: Kyle Hugeback, [hugeback@iastate.edu](hugeback@iastate.edu)  \nsweep area, the generative ability of turbines beyond the firstrow was highly dependent on the relative height of the LLJ (Gadde and Stevens 2021) . If the core of the LLJ is situated above the rotor sweep area, the high turbulence aids in wake recovery and thus increases power generation. Opposite of that, when the LLJ is positioned below the rotor area, ","cbCaieeXPpObKa3T","https://ap.wps.com/l/cbCaieeXPpObKa3T","pdf",1171100,1,9,"English","en",105,"# Introduction\n## Forecasting needs and boundary-layer influences\n## Numerical weather prediction and data limitations\n# Methods and machine-learning approach\n## Forecasting and correcting WRF outputs\n# Results and impacts on black start operations\n## Day-2 wind improvements and short-range nowcasting","[{\"question\":\"Why are hub-height wind forecasts especially challenging for numerical modeling?\",\"answer\":\"Errors increase because hub-height forecasts became common only recently and boundary-layer structure changes strongly between night collapse and daytime growth through turbine-relevant levels.\"},{\"question\":\"Which machine-learning models are used to improve WRF forecasts and corrections?\",\"answer\":\"The study explores several algorithms, including a multioutput neural network for day-2 wind improvements and an LSTM for 60-minute forecasts of WRF error used to adjust forecasts.\"},{\"question\":\"What performance benefits are reported for short-term nowcasting?\",\"answer\":\"An LSTM is used for 10-minute nowcasting of temperature and wind speed, producing very low error and especially skillful forecasts of maximum and minimum values across the turbine plant area.\"}]","Machine Learning - Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations | PDF",1785897710,23,{"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-adjusted-wrf-forecasts-to-support-wind-energy-needs-in-black-start-operations","",{"@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-adjusted-wrf-forecasts-to-support-wind-energy-needs-in-black-start-operations/125249/",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},"Why are hub-height wind forecasts especially challenging for numerical modeling?","Question",{"text":75,"@type":76},"Errors increase because hub-height forecasts became common only recently and boundary-layer structure changes strongly between night collapse and daytime growth through turbine-relevant levels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning models are used to improve WRF forecasts and corrections?",{"text":80,"@type":76},"The study explores several algorithms, including a multioutput neural network for day-2 wind improvements and an LSTM for 60-minute forecasts of WRF error used to adjust forecasts.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance benefits are reported for short-term nowcasting?",{"text":84,"@type":76},"An LSTM is used for 10-minute nowcasting of temperature and wind speed, producing very low error and especially skillful forecasts of maximum and minimum values across the turbine plant area.","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"]