RT info:eu-repo/semantics/article
T1 Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks, Mesoscale Models, and LiDAR Measurements
A1 García Gutiérrez, Adrián
A1 López Rodríguez, Deibi
A1 Domínguez Fernández, Diego
A1 Gonzalo de Grado, Jesús
A2 Ingenieria Aeroespacial
K1 Aeronáutica
K1 Atmospheric boundary layer
K1 Wind vertical profile
K1 LiDAR
K1 Machine learning
K1 3301 Ingeniería y Tecnología Aeronáuticas
AB [EN] This paper introduces a novel methodology that estimates the wind profile within the ABL by using a neural network along with predictions from a mesoscale model in conjunction with a single near-surface measurement. A major advantage of this solution compared to other solutions available in the literature is that it requires only near-surface measurements for prediction once the neural network has been trained. An additional advantage is the fact that it can be potentially used to explore the time evolution of the wind profile. Data collected by a LiDAR sensor located at the University of León (Spain) is used in the present research. The information obtained from the wind profile is valuable for multiple applications, such as preliminary calculations of the wind asset or CFD modeling.
PB MDPI
LK https://hdl.handle.net/10612/17744
UL https://hdl.handle.net/10612/17744
NO García-Gutiérrez, A., López, D., Domínguez, D., & Gonzalo, J. (2023). Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks, Mesoscale Models, and LiDAR Measurements. Sensors (Basel, Switzerland), 23(7). https://doi.org/10.3390/S23073715
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 20-may-2024