Simulation of non-stationary wind speed and direction time series

Abstract

The simulation of synthetic wind time series is strictly necessary, among others, to assess the risk of structures and systems, to model the dynamic of morphological features like dunes, or to investigate the potential of wind energy in a certain location. For all these applications, a tool able to simulate time series of wind speed and direction is required. In this paper we propose a methodology for the simulation of bivariate non-stationary time series of wind speed and direction. This methodology takes into account the circular nature of the wind direction, as well as the mean annual cycle of both wind speed and direction. For modelling the joint distribution of the two variables, wind speed is modelled conditioned to the wind direction. For modelling short-term self- and cross-dependency among the variables a Vector Autoregressive (VAR) model of order p is used, with an innovation process at time t that depends on the value taken by the variables at time t−1, based on the use of a mixture of multivariate normal distributions. The proposed methodology is applied to a case study, simulating several synthetic series, of the same length as the original series. The simulated series satisfactorily captures the characteristics of averaging or common data (i.e. with low or moderate speed) and, under certain independence conditions, the directional extreme value distribution of the wind speed is also properly reproduced.