Estimating Site-Specific Wind Speeds Using Gridded Data: A Comparison of Multiple Machine Learning Models

Abstract

Accurate site-specific estimations of surface wind speeds (SWS) would greatly aid clean energy development. The quality of estimation depends on the method of interpolating gridded SWS data to derive the wind speed at a given location. This work uses multiple machine learning (ML) and deep learning (DL) methods to estimate wind speeds at locations across eastern China using the gridded fifth-generation data from the European Centre for Medium-Range Weather Forecasts. The root-mean-square error (RMSE) of these models’ estimates for summer and winter are, respectively, reduced by 23% and 16% on average against simple linear interpolation. A deep convolution neural network (DCNN) consistently performs best among the considered models, reducing the RMSE by 26% and 23% for summer and winter data, respectively. We further examine the dependence of the models’ estimations on altitude, land use category, and local mean SWS. And found that the DCNN can reflect the nonlinear relationships among these variables and SWS. Threfore, it can be used for site-specific wind speed estimates over a large area like eastern China.