Research on Basic Wind Pressure Calculation Method for Lacking Long‐Term Wind Speed Data

Abstract

In engineering structural design, wind load is of particular importance, and the basic wind pressure is the basis for wind load calculation. Specifications of many countries fail to use the given method to calculate the basic wind pressure for areas or engineering projects with only short-term wind speed data. At the same time, there are few studies on short-term wind speed data fitting by using the extreme value Type III distribution as the distribution law. In this study, the extreme value Type III distribution with variable substitution method and least square method as parameter estimation method is used to fit and analyze the daily, weekly, and monthly maximum wind speed data of 13 major cities in China, when using the Kolmogorov–Smirnov test method to test the results of the fit, it finds that in the wind speed samples of most cities, only the monthly maximum wind speed data obeys the extreme value Type III distribution. By comparing the optimal criterion of estimation, it can be seen that the extreme value Type III distribution fits well with the monthly maximum wind speed data of each city, and the variable substitution method is the most optimal parameter estimation method. The above results show that in areas where long-term wind speed data are insufficient, short-term wind speed data is an available option. Taking the extreme value Type III distribution as the probability distribution, the method of calculating the basic wind pressure of different return periods based on short-term wind speed data is summarized by comparing the specifications of various countries in the world.

The Return Period Wind Speed Prediction of Beijing Urban Area Based on Short-Term Measured Wind Speed

“…Based on the measured wind speed data, there is often a deficiency in the number of wind speed samples for RPWS prediction. And, when the annual maximum wind speed samples are selected to predict the RPWS, it is advisable to have no less than 10 years of measured wind speed data, with 25 years or more of data generally required to ensure prediction accuracy of the RPWS [1,2]. Therefore, when the observation period of the measured wind speed data is relatively short, making the most of existing measured wind speed information for RPWS prediction is a critical issue.…”