Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression
This work addresses accuracy issues for users of low-cost wind speed measurement devices, but it is incremental as it applies an existing method to a specific calibration problem.
The paper tackled the problem of inaccuracies in low-cost hot-wire anemometers due to air temperature changes by using Gaussian Process Regression for probabilistic calibration, resulting in good performance in inferring actual wind speeds with grounded uncertainty estimates.
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.