Energies | |
Towards Better Wind Resource Modeling in Complex Terrain: A k-Nearest Neighbors Approach | |
Pedro Quiroga-Novoa1  Gabriel Cuevas-Figueroa1  José Luis Preciado1  Oliver Probst1  Alfredo Peña2  Rogier Floors2  | |
[1] School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL CP64689, Mexico;Wind Energy Department, Technical University of Denmark, 4000 Roskilde, Denmark; | |
关键词: wind resource; machine learning; similarity; complex terrain; WAsP; WindSim; | |
DOI : 10.3390/en14144364 | |
来源: DOAJ |
【 摘 要 】
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.
【 授权许可】
Unknown