Long term persistence in daily wind speed series using fractal dimension
DOI:
https://doi.org/10.1260/1750-9548.7.2.87Abstract
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.
References
S. Harrouni. Fractal analysis to quantify wind speed fluctuations. 3rd Conference on Nonlinear Science and Complexity (NSC 3rd), Ankara, Turkey, 28-31 July 2010.
S. Harrouni, and A. Guessoum. Using fractal dimension to quantify long-range persistence in global solar radiation. Chaos. Soliton. Fract. 2009, 41 (3): 1520-1530. https://doi.org/10.1016/j.chaos.2008.06.016
B. D. Malamud, and D. l. Turcotte. Self-affine time series: measures of weak and strong persistence. J. Stat. Plan. Infer. 1999, 80 (1):173-196. https://doi.org/10.1016/s0378-3758(98)00249-3
P. Maragos, and FK. Sun. Measuring the fractal dimension of signals: morphological covers and iterative optimization. IEEE. T. Signal. Process. 1993, 41 (1): 108-121. https://doi.org/10.1109/tsp.1993.193131
C. Tricot, JF. Quiniou, D. Wehbi, C. Roques-Carmes, and B. Dubuc. Evaluation de la dimension fractale d'un graphe. Rev. Phys. 1988, 23: 111-124. https://doi.org/10.1051/rphysap:01988002302011100
B. Dubuc, F. Quiniou, C. Roques-Carmes, C. Tricot, and S. W. Zucker. Evaluating the fractal dimension of profiles. Phys Rev A. 1989, 39 (3): 1500-1512. https://doi.org/10.1103/physreva.39.1500
S. Harrouni, A. Guessoum, and A. Maafi. Classification of daily solar irradiation by fractional analysis of 10-min-means of solar irradiance. Theor. Appl. Climatol. 2005, 80 (1): 27-36. https://doi.org/10.1007/s00704-004-0085-0
S. Harrouni, and A. Guessoum. New method for estimating the time series fractal dimension: Application to solar irradiances signals. In: Solar Energy: New research, (Tom P. Hough. (Ed)). Nova Science Publishers, New York, 2006, 277-307.
S. Harrouni. Fractal classification of typical meteorological days from global solar irradiance: Application to five sites of different climates. In: Modeling solar irradiance at earth surface, (Viorel Badescue. (Ed)). Springer Verlag Berlin Heidelberg, Germany, 2008, 29-54. https://doi.org/10.1007/978-3-540-77455-6_2
G. Bouligand. Ensembles impropres et nombre dimensionnel. Bull Sci Math. 1928, 52 (2): 320-344, 361-376. http://www.meteo-paris.com/ile-de-france/station-meteo-paris/pro/
T.P Chang, H-H Ko, F-J Liu, P-H Chen, Y-P Chang, H-Y Liang, H-Y Jang, T-C Lin and Y-H Chen. Fractal dimension of wind speed time series. Appl. Energ. 2012, 93: 742-749. https://doi.org/10.1016/j.apenergy.2011.08.014
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