Statistical identification of local and regional wind regimes

Abstract

Distinct wind conditions driven by prevailing weather patterns exist in every region around the globe. Knowledge of these conditions can be used to select and place turbines within a wind project, design controls, and build space-time models for wind forecasting. Identifying regimes quantitatively and comparing the performance of different regime identification methods are the goals of this research. The ability of statistical clustering techniques to correctly assign hourly observations to a particular regime and to select the correct number of regimes is studied through simulation. Pressure and the horizontal and vertical wind components are simulated under different regimes with a first-order Markov-switching vector autoregressive model, and the following five clustering algorithms are applied: (1) classification based on wind direction, (2) k-means, (3) a nonparametric mixture model, and (4,5) a Gaussian mixture model (GMM) with one of two covariance structures. The GMM with an unconstrained covariance matrix has the lowest misclassification rate and the highest proportion of instances in which the correct number of regimes are selected. This method is applied to one year of averaged hourly wind data observed at twenty meteorological stations. The lagged wind speed correlations between neighboring sites under upwind and downwind regimes are shown to differ substantially.