This paper describes some evidence of fractal order features in wind speed time series recorded at different observation stations both in USA and in Italy. Analysis were performed by using mono-fractal, multi-fractal and power spectra approaches. Results show that the average value of the box dimension for daily and hourly mean wind speed is D = 1.19 and D = 1.41 respectively, thus indicating that this kind of time series are fractal. The estimated average value of the Hurst exponent is H = 0.81 and H = 0.75 for daily and hourly time series respectively. From these Hurst exponents it is possible to infer the persistent behavior of wind speed. Furthermore, multi-fractal analysis shows that wind speed exhibits a bell-like shape spectrum with average width Δα = 0.47. Power spectra analysis has pointed out that wind speed time series behave as 1/f13 noise with average value of the β exponent of 0.46 and 1.37 for daily mean and hourly mean time series respectively. These latter results can be interpreted by saying that wind speed time series are Brown noise like.

Fractal order evidences in wind speed time series

GUARISO, GIORGIO
2014-01-01

Abstract

This paper describes some evidence of fractal order features in wind speed time series recorded at different observation stations both in USA and in Italy. Analysis were performed by using mono-fractal, multi-fractal and power spectra approaches. Results show that the average value of the box dimension for daily and hourly mean wind speed is D = 1.19 and D = 1.41 respectively, thus indicating that this kind of time series are fractal. The estimated average value of the Hurst exponent is H = 0.81 and H = 0.75 for daily and hourly time series respectively. From these Hurst exponents it is possible to infer the persistent behavior of wind speed. Furthermore, multi-fractal analysis shows that wind speed exhibits a bell-like shape spectrum with average width Δα = 0.47. Power spectra analysis has pointed out that wind speed time series behave as 1/f13 noise with average value of the β exponent of 0.46 and 1.37 for daily mean and hourly mean time series respectively. These latter results can be interpreted by saying that wind speed time series are Brown noise like.
2014
Fractional Differentiation and Its Applications (ICFDA)
978-1-4799-2591-9
Hurst coefficient, box counting, persitence, similarity
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/961259
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