First Order Non-homogeneous Markov Chain Model for Generation of Wind Speed and Direction Synthetic Time Series

This paper presents a non-homogeneous Markov Chain (MC) model for generation of wind speed (WS) and wind direction (WD) synthetic time series taking into account their daily, monthly and seasonal characteristics. The bivariate nature of the wind process, represented by WS and WD, is modelled by means of an equivalent univariate random variable W, capable of taking into account the statistical dependency existing between WS and WD. A statistical characterization of the wind energy resource at the specific considered site demonstrates the time non-stationarity of the wind process over the year and over the seasons, so twelve monthly transition probability matrices of the variable W are developed. One thousand synthetic time series, each of three years length, are generated in a Monte Carlo framework, demonstrating the excellent performances and overall robustness of the presented model, also using new non-conventional metrics based on Markov transition matrices.