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, Oliver Probst*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
Article numberSuzlon
Issue number14
Pages (from-to)4364
Number of pages19
Publication statusPublished - 2021


  • Wind resource
  • Machine learning
  • Similarity
  • Complex terrain
  • WAsP
  • WindSim


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