Long term persistence in daily wind speed series using fractal dimension

In the assessment of wind turbines installations efficiency long series of wind speed data are necessary. Such data are not usually available it is then important to generate them. In this paper we examine the long-term persistence of daily wind speed data with many years of record using the fractal dimension. The persistence measures the correlation between adjacent values within the time series. Values of a time series can affect other values In the time series that are not only nearby In time but also far away In time. For this purpose, a new method to measure the fractal dimension of temporal discrete signals is presented. The fractal dimension is then used as criterion in an approach we have elaborated to detect the long term correlation In wind speed series. The results show that daily wind speed are anti-persistent.