Application of the WRF Model for Operational Wind Power Forecasting in Northeast Brazil

Thiago Silva (), Alexandre Costa, Olga C. Vilela, Ramiro Willmersdorf, José Vailson dos Santos Júnior, Luís Henrique Bezerra Alves, Pedro Tyaquiçã, Mateus Francisco Silva de Lima, Herbert Rafael Barbosa de Souza and Doris Veleda
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Thiago Silva: Center for Renewable Energy, Federal University of Pernambuco (CER-UFPE), Recife 50670-901, Pernambuco, Brazil
Alexandre Costa: Center for Renewable Energy, Federal University of Pernambuco (CER-UFPE), Recife 50670-901, Pernambuco, Brazil
Olga C. Vilela: Center for Renewable Energy, Federal University of Pernambuco (CER-UFPE), Recife 50670-901, Pernambuco, Brazil
Ramiro Willmersdorf: Mechanical Engineering Department, Federal University of Pernambuco, Av. da Arquitetura S/N, Recife 50740-550, Pernambuco, Brazil
José Vailson dos Santos Júnior: Center for Renewable Energy, Federal University of Pernambuco (CER-UFPE), Recife 50670-901, Pernambuco, Brazil
Luís Henrique Bezerra Alves: Center for Renewable Energy, Federal University of Pernambuco (CER-UFPE), Recife 50670-901, Pernambuco, Brazil
Pedro Tyaquiçã: Center for Renewable Energy, Federal University of Pernambuco (CER-UFPE), Recife 50670-901, Pernambuco, Brazil
Mateus Francisco Silva de Lima: Center for Renewable Energy, Federal University of Pernambuco (CER-UFPE), Recife 50670-901, Pernambuco, Brazil
Herbert Rafael Barbosa de Souza: Center for Renewable Energy, Federal University of Pernambuco (CER-UFPE), Recife 50670-901, Pernambuco, Brazil
Doris Veleda: Center for Renewable Energy, Federal University of Pernambuco (CER-UFPE), Recife 50670-901, Pernambuco, Brazil

Energies, 2025, vol. 18, issue 21, 1-21

Abstract: Northeastern Brazil (NEB) has a high potential for wind energy generation, making it a strategic area for the development of this renewable source. However, the region’s complex wind regime, driven by interactions between large-scale atmospheric systems, local circulations, and coastal topography, presents significant challenges for weather forecasting and wind energy applications. Despite this, detailed assessments of forecast performance using mesoscale models remain limited. The main objective was to develop an efficient strategy that enables satisfactory results by optimizing data assimilation, land use and topography information as well as improvements in physical parameterizations and post-processing, optimizing computational effort. Forecasting conducted during the year 2020 were validated with data from 20 anemometric measurement towers (AMTs), located at strategic points across various wind power complexes. The model’s performance was evaluated using statistical metrics such as MBE, MAE, nRMSE, standard deviation ratio, and correlation. Additionally, the impact of bias removal was assessed using two approaches: one that eliminates the mean error per forecasted time step and another employing artificial intelligence for bias removal training. The results revealed distinct characteristics for each analyzed location, with errors of diverse nature due to the local nuances of the measurements. However, both bias removal approaches showed significant improvements in wind characterization across all complexes.

Keywords: hourly wind forecasting; weather research and forecasting (WRF); Northeast Brazil (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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