Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations

dc.contributor.author Hugeback, Kyle
dc.contributor.author Gallus, William
dc.contributor.author Villegas Pico, Hugo
dc.contributor.department Department of the Earth, Atmosphere, and Climate
dc.contributor.department Department of Electrical and Computer Engineering
dc.date.accessioned 2025-05-28T15:35:11Z
dc.date.available 2025-05-28T15:35:11Z
dc.date.issued 2023-09
dc.description.abstract 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.
dc.description.comments This article is published as Hugeback, K. K., W. A. Gallus, and H. N. Villegas Pico, 2023: Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations. Wea. Forecasting, 38, 1553–1561, https://doi.org/10.1175/WAF-D-23-0023.1
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/erLKd3ev
dc.language.iso en
dc.publisher American Meteorological Society
dc.rights © Copyright 2023 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy).
dc.source.uri https://doi.org/10.1175/WAF-D-23-0023.1 *
dc.subject.disciplines DegreeDisciplines::Physical Sciences and Mathematics::Oceanography and Atmospheric Sciences and Meteorology::Meteorology
dc.subject.disciplines DegreeDisciplines::Engineering::Electrical and Computer Engineering::Power and Energy
dc.subject.keywords Forecasting techniques
dc.subject.keywords Nowcasting
dc.subject.keywords Short-range prediction
dc.subject.keywords Model errors
dc.subject.keywords Neural networks
dc.title Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 29272786-4c4a-4d63-98d6-e7b6d6730c45
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
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