Integrating the Spatial Autocorrelation Structure of Irregularly Distributed Wind Measurement Stations to Model Wind Patterns Using R

A novel approach leveraging spatial autocorrelation and advanced statistical modeling in R successfully mapped wind patterns across Sri Lanka, particularly in the Central Highlands. By analyzing 23 irregularly distributed wind measurement stations with diurnal data from 2015, the study identified seasonal variations and diurnal changes in wind speed and direction, including significant coastal and highland dynamics. The use of semi-variograms—spherical and exponential models—and Ordinary Kriging enabled precise spatial interpolation, generating 48 weekly wind maps that pinpoint potential wind farming areas. This method not only advances wind energy resource planning but also demonstrates R's capability for analyzing complex geographic phenomena with high uncertainty.

Welikanna, D. R. (2022). Integrating the Spatial Autocorrelation Structure of Irregularly Distributed Wind Measurement Stations to Model Wind Patterns Using R. Journal of Geospatial Surveying, 2(2), 30–42. https://doi.org/10.4038/jgs.v2i2.42