Abstract
Wind speed’s distribution nature such as uncertainty and randomness imposes a challenge in high accuracy forecasting. Based on the energy distribution about the extracted amplitude and associated frequency, the uncertainty measurement is processed through Rényi entropy analysis method with time-frequency nature. Nonparametric statistical method is used to test the randomness of wind speed, more precisely, whether or not the wind speed time series is independent and identically distribution (i.i.d) based on the output probability. Seasonal characteristics of wind speed are analyzed based on self-similarity in periodogram under scales range generated by wavelet transformation to reasonably divide the original dataset and effectively reflect the seasonal distribution characteristics. Experimental evaluation based on the dataset from National Renewable Energy Laboratory (NREL) is given to demonstrate the performance of the proposed approach.
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