The Gust Factor Models Involving Wind Speed and Temperature Profiles for Wind Gust Estimation

Abstract

This study analyzed the deviation of the gust factor (GF) method in gust estimation and the differences in the vertical distribution of upper‐level wind speed and temperature when varying deviations occurred using observed data from 11 stations in central‐eastern China from January 2017 to December 2020. A unified upper‐level gust impact model was developed through multiple regression (GF‐L) and machine learning (GF‐M) methods based on data from these stations to improve gust estimation accuracy. The effectiveness of the GF, GF‐L, and GF‐M methods was compared using data from January 2021 to October 2022. Results showed that integrating the upper‐level gust impact model into the GF method reduced underestimation and improved accuracy, with the GF‐M method showing the most significant improvement. The GF‐L method reduced the root mean square error by 5.1% on average compared to the GF method, while the GF‐M method achieved a 9.2% reduction. The study confirmed the applicability of the unified upper‐level gust impact model across different stations, with the GF‐M method demonstrating better results.