Exogenous atmospheric variables as wind speed predictors in machine learning

Abstract

The use of exogenous meteorological variables in wind speed prediction is currently enjoying much attention, particularly with the continued proliferation of wind power onto electricity networks. Typically, the selection of appropriate forecasting regimes or downscaling variables, along with the appropriate domain size, is based on expert knowledge and/or physical principles. There is however little consensus as to which variables are the best predictors. In this paper the wind speed predictive skill provided by 16 exogenous meteorological variables in machine learning algorithms is investigated as a function of: choice of regression model; choice of domain size; forecast period; meteorological variable elevation (i.e. pressure level); and testing site location. The principal components of numeric reanalysis datasets for each of 16 variables, along with lagged winds speeds, were employed as predictors at each of the test sites. The mean best performing wind speed predictors were found to be 950 hPa- vertical velocity; divergence; the u- & v-wind speed components; and geopotential heights. The ability to investigate and rank the forecast skill provided by meteorological predictors is important in gaining insight into physical processes influencing the wind resource and the improvement of wind speed forecasts. Though distinct predictors, domains and approaches were identified to significantly improve forecasting accuracy for a specific site, generalisations were found to be difficult to make, even in the small geographic area investigated. Therefore, it is recommended is that process described in paper is repeated for individual sites in selecting exogenous predictor variables.