Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916095354470400 |
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| author | García-Ruiz, Rubén Antonio Blanco-Claraco, José Luis López-Martínez, Javier Callejón-Ferre, Ángel Jesús |
| author_facet | García-Ruiz, Rubén Antonio Blanco-Claraco, José Luis López-Martínez, Javier Callejón-Ferre, Ángel Jesús |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09492 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression García-Ruiz, Rubén Antonio Blanco-Claraco, José Luis López-Martínez, Javier Callejón-Ferre, Ángel Jesús Machine Learning Applications 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. |
| title | Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2401.09492 |