First-order Markov chain approach to wind speed modelling

Abstract

Abstract Hourly wind speed data in northwestern region of Turkey are simulated by using transition matrix approach of the first-order Markov chain method. For this purpose, the wind speed time series is divided into various states depending on the arithmetic average and the standard deviation. Once the control and validation of the model is confirmed, it is then used for generating synthetic series of various lengths of any desired duration. Wind speed measurement parameters are used to generate synthetic series with the preservation of the statistical parameters and the first-order autocorrelation coefficient. It is observed that for short periods, the parametric results obtained from the synthetic time series are close to the measured values. It is concluded in this paper that first-order Markov chain, despite its simplicity, accounts for more than 90% of the statistical parameters in the synthetic wind speed time series at most of the 10 stations considered. However, the discrepancy of the results is due to the use of first-order Markov chain as a first approximation. Since, hourly wind speed data are used in addition to the first-order autocorrelation coefficient second and even third-order autocorrelation coefficients are also significant. Therefore, in the future hourly wind speed modelling at least second- or preferably third-order, Markov chain must be tried.