@Article{EE.2020.011126,
AUTHOR = {Xing Deng, Haijian Shao, Xia Wang},
TITLE = {Seasonal Characteristics Analysis and Uncertainty Measurement for Wind Speed Time Series},
JOURNAL = {Energy Engineering},
VOLUME = {117},
YEAR = {2020},
NUMBER = {5},
PAGES = {289--299},
URL = {http://www.techscience.com/energy/v117n5/40114},
ISSN = {1546-0118},
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.},
DOI = {10.32604/EE.2020.011126}
}