Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression
| Autor: | Rubén A. García-Ruiz, José Luis Blanco-Claraco, Ángel-Jesús Callejón-Ferre, Javier López-Martínez |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | IEEE Sensors Journal. 19:7515-7524 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2019.2915093 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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