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

Energies

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