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

arXiv: https://arxiv.org/abs/2401.09492 · HTML: https://mlsanity.com/paper/2401.09492/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Automatic Construction and Natural-Language Description of Nonparametric Regression Models](https://mlsanity.com/paper/1402.4304/) (2014)
- [A regression tree approach to identifying subgroups with differential treatment effects](https://mlsanity.com/paper/1410.1932/) (2014)

## Abstract (arXiv)

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.