Abstract:

The computational fluid dynamics (CFD) method is one of the major ways of wind fields patial pattern simulation at present. This method is more often applied to wind field simulations and analyses on small scales because of the limitations of the hardware and software. Meanwhile, the precision and accuracy of this method depends on the precision of 3D building model sand the accuracy of iterated computation models, and there are significant differences existing between the simulation results and the real wind field situation. With the development of Internet of Things (IoT) technology, we can provide precise parameters to real-time simulation of regional wind fields pace distribution, by using a large number of real-time field sensor nodes. Spatial interpolation can be used to simulate the spatial distribution of all the regional environment factors. In order to determine the optimal method for real-time wind fields imulation, this research took the monthly average wind speed data of November, 2011, which are collected from 32 wind speed sensors in Institute of Urban Environment, CAS, as the example. Then, we make a comprehensive analysis of Inverse Distance Weight method, Global Polynomial method, Local Polynomial method, Radial Basis Function method, Nearest Neighbor method and Ordinary Kriging method, and compare the results of the six different methods by Cross-Validation. The result shows that the Inverse Distance Weight method is better than the other methods in the simulation error range, the simulation accuracy and the ability to reflect extreme value, which provides a reference for wind field simulation on small scales.

Key words: wind field, Internet of Things, spatial interpolation