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.

翻译：昂贵的高精度超声波风速计通常是准确测量风速所必需的。本研究旨在通过采用高斯过程回归的概率校准方法，克服低成本热线风速仪因气温变化导致精度下降的问题。高斯过程回归是一种非参数、贝叶斯监督学习方法，用于根据一个或多个已知输入变量预测未知目标变量。该方法通过真实数据集验证，在推断实际风速值方面取得了良好性能。在实际现场使用前，通过考虑空气温度对热线风速仪进行校准，即可在典型环境温度范围内估算风速，并为每次速度测量提供有依据的不确定度估计。