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

Pedro Quiroga-Novoa, Gabriel Cuevas-Figueroa, José Luis Preciado, Rogier Floors, Alfredo Peña and Oliver Probst
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Pedro Quiroga-Novoa: School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL CP64689, Mexico
Gabriel Cuevas-Figueroa: School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL CP64689, Mexico
José Luis Preciado: School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL CP64689, Mexico
Rogier Floors: Wind Energy Department, Technical University of Denmark, 4000 Roskilde, Denmark
Alfredo Peña: Wind Energy Department, Technical University of Denmark, 4000 Roskilde, Denmark
Oliver Probst: School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL CP64689, Mexico

Energies, 2021, vol. 14, issue 14, 1-19

Abstract: 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.

Keywords: wind resource; machine learning; similarity; complex terrain; WAsP; WindSim (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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