128026 20240731103353.0 doi 10.1007/s40808-023-01851-x sideral 135210 ART-2023-135210 eng Milla-Val, Jaime Economical microscale predictions of wind over complex terrain from mesoscale simulations using machine learning 2023 Access copy available to the general public Unrestricted The ability to assess detailed wind patterns in real time is increasingly important for a variety of applications, including wind energy generation, urban comfort and environmental health, and drone maneuvering in complex environments. Machine Learning techniques are helping to develop accurate and reliable models for predicting local wind patterns. In this paper, we present a method for obtaining wind predictions with a higher resolution, similar to those from computational fluid dynamics (CFD), from coarser, and therefore less expensive, mesoscale predictions of wind in real weather conditions. This is achieved using supervised learning techniques. Four supervised learning approaches are tested: linear regression (SGD), support vector machine (SVM), k-nearest neighbors (KNn) and random forest (RFR). Among the four tested approaches, SVM slightly outperforms the others, with a mean absolute error of 1.81 m/s for wind speed and 40.6 for wind direction. KNn however achieves the best results in predicting wind direction. Speedup factors of about 290 are achieved by the model with respect to using CFD. info:eu-repo/grantAgreement/ES/MICIN/DIN2019-010452 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 0.677 2023 Agricultural and Biological Sciences (miscellaneous) 2023 Q1 Environmental Science (miscellaneous) 2023 Q2 Statistics, Probability and Uncertainty 2023 Q2 Computers in Earth Sciences 2023 Q2 6.3 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Montañés, Carlos Universidad de Zaragoza (orcid)0000-0002-6948-2314 Fueyo, Norberto Universidad de Zaragoza (orcid)0000-0001-6205-5160 5001 600 Universidad de Zaragoza Dpto. Ciencia Tecnol.Mater.Fl. Área Mecánica de Fluidos 10 (2023), 1407–1421 Modeling Earth Systems and Environment 2363-6203 3593893 http://zaguan.unizar.es/record/128026/files/texto_completo.pdf Versión publicada 2438070 http://zaguan.unizar.es/record/128026/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:128026 articulos driver 2024-07-31-09:55:18 ARTICLE