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Towards Better Wind Resource Modeling in Complex Terrain: A k-Nearest Neighbors Approach

1
School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL CP64689, Mexico
2
Wind Energy Department, Technical University of Denmark, 4000 Roskilde, Denmark
*
Author to whom correspondence should be addressed.
Academic Editors: Davide Astolfi and Eugen Rusu
Energies 2021, 14(14), 4364; https://doi.org/10.3390/en14144364
Received: 28 May 2021 / Revised: 1 July 2021 / Accepted: 15 July 2021 / Published: 20 July 2021
(This article belongs to the Section B2: Wind, Wave and Tidal Energy)
Wind turbines are often placed in complex terrains, where benefits from orography-related speed up can be capitalized. However, accurately modeling the wind resource over the extended areas covered by a typical wind farm is still challenging over a flat terrain, and over a complex terrain, the challenge can be even be greater. Here, a novel approach for wind resource modeling is proposed, where a linearized flow model is combined with a machine learning approach based on the k-nearest neighbor (k-NN) method. Model predictors include combinations of distance, vertical shear exponent, a measure of the terrain complexity and speedup. The method was tested by performing cross-validations on a complex site using the measurements of five tall meteorological towers. All versions of the k-NN approach yield significant improvements over the predictions obtained using the linearized model alone; they also outperform the predictions of non-linear flow models. The new method improves the capabilities of current wind resource modeling approaches, and it is easily implemented. View Full-Text
Keywords: wind resource; machine learning; similarity; complex terrain; WAsP; WindSim wind resource; machine learning; similarity; complex terrain; WAsP; WindSim
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MDPI and ACS Style

Quiroga-Novoa, P.; Cuevas-Figueroa, G.; Preciado, J.L.; Floors, R.; Peña, A.; Probst, O. Towards Better Wind Resource Modeling in Complex Terrain: A k-Nearest Neighbors Approach. Energies 2021, 14, 4364. https://doi.org/10.3390/en14144364

AMA Style

Quiroga-Novoa P, Cuevas-Figueroa G, Preciado JL, Floors R, Peña A, Probst O. Towards Better Wind Resource Modeling in Complex Terrain: A k-Nearest Neighbors Approach. Energies. 2021; 14(14):4364. https://doi.org/10.3390/en14144364

Chicago/Turabian Style

Quiroga-Novoa, Pedro, Gabriel Cuevas-Figueroa, José L. Preciado, Rogier Floors, Alfredo Peña, and Oliver Probst. 2021. "Towards Better Wind Resource Modeling in Complex Terrain: A k-Nearest Neighbors Approach" Energies 14, no. 14: 4364. https://doi.org/10.3390/en14144364

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