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

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Abstract
Expensive ultrasonic anemometers are usually required to measure wind speed accurately. The aim of this work is overcome the loss of accuracy of a low cost hot-wire anemometer caused by 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 a 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 hotwire anemometer taking into account air temperature, permits that 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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