Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression

• URL: http://arxiv.org/abs/2401.09492v1
• Date: Tue, 16 Jan 2024 22:01:24 GMT
• Title: Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression
• Authors: Rub\'en Antonio Garc\'ia-Ruiz, Jos\'e Luis Blanco-Claraco, Javier L\'opez-Mart\'inez, \'Angel Jes\'us Callej\'on-Ferre
• Abstract summary: The aim of this work is to overcome the loss of accuracy of a low cost hot-wire anemometer caused by the changes of air temperature. By performing a calibration of the hot-wire anemometer taking into account air temperature, the wind speed can be estimated for the typical range of ambient temperatures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Expensive ultrasonic anemometers are usually required to measure wind speed accurately. The aim of this work is to overcome the loss of accuracy of a low cost hot-wire anemometer caused by the changes of air temperature, by means of a probabilistic calibration using Gaussian Process Regression. Gaussian Process Regression is a non-parametric, Bayesian, and supervised learning method designed to make predictions of an unknown target variable as a function of one or more known input variables. Our approach is validated against real datasets, obtaining a good performance in inferring the actual wind speed values. By performing, before its real use in the field, a calibration of the hot-wire anemometer taking into account air temperature, permits that the wind speed can be estimated for the typical range of ambient temperatures, including a grounded uncertainty estimation for each speed measure.

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