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 | |
relation.isAuthorOfPublication | 782ee936-54e9-45de-a7e6-2feb462aea2a | |
relation.isOrgUnitOfPublication | 29272786-4c4a-4d63-98d6-e7b6d6730c45 | |
relation.isOrgUnitOfPublication | a75a044c-d11e-44cd-af4f-dab1d83339ff |
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