Machine learning approach for turbulence forecasting using support vector machine

Abstract

The turbulence can be expressed as small-scale, short term and frequent change to the velocity of air. The turbulence forecasting in the atmospheric boundary layer is very influenced by gradient Richardson number values. In this case, we use the data set were retrieved from Indonesia Meteorology, Climatology and Geophysics in synoptic hour 0000H. In particular, our methods will use Richardson number value and machine learning approaches by using a support vector machine to forecast the Richardson number value and identification the stability of the layer based on the turbulence forecasting. The results will be practically beneficial as utilities can use the predicted values to generate an adequate amount of Richardson number value to avoid grid outages as well as construct dynamic pricing schemes based upon future turbulence.