000128026 001__ 128026
000128026 005__ 20240731103353.0
000128026 0247_ $$2doi$$a10.1007/s40808-023-01851-x
000128026 0248_ $$2sideral$$a135210
000128026 037__ $$aART-2023-135210
000128026 041__ $$aeng
000128026 100__ $$aMilla-Val, Jaime
000128026 245__ $$aEconomical microscale predictions of wind over complex terrain from mesoscale simulations using machine learning
000128026 260__ $$c2023
000128026 5060_ $$aAccess copy available to the general public$$fUnrestricted
000128026 5203_ $$aThe 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.
000128026 536__ $$9info:eu-repo/grantAgreement/ES/MICIN/DIN2019-010452
000128026 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000128026 592__ $$a0.677$$b2023
000128026 593__ $$aAgricultural and Biological Sciences (miscellaneous)$$c2023$$dQ1
000128026 593__ $$aEnvironmental Science (miscellaneous)$$c2023$$dQ2
000128026 593__ $$aStatistics, Probability and Uncertainty$$c2023$$dQ2
000128026 593__ $$aComputers in Earth Sciences$$c2023$$dQ2
000128026 594__ $$a6.3$$b2023
000128026 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000128026 700__ $$0(orcid)0000-0002-6948-2314$$aMontañés, Carlos$$uUniversidad de Zaragoza
000128026 700__ $$0(orcid)0000-0001-6205-5160$$aFueyo, Norberto$$uUniversidad de Zaragoza
000128026 7102_ $$15001$$2600$$aUniversidad de Zaragoza$$bDpto. Ciencia Tecnol.Mater.Fl.$$cÁrea Mecánica de Fluidos
000128026 773__ $$g10 (2023), 1407–1421$$tModeling Earth Systems and Environment$$x2363-6203
000128026 8564_ $$s3593893$$uhttps://zaguan.unizar.es/record/128026/files/texto_completo.pdf$$yVersión publicada
000128026 8564_ $$s2438070$$uhttps://zaguan.unizar.es/record/128026/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000128026 909CO $$ooai:zaguan.unizar.es:128026$$particulos$$pdriver
000128026 951__ $$a2024-07-31-09:55:18
000128026 980__ $$aARTICLE