Open Access

ARTICLE

Seasonal Characteristics Analysis and Uncertainty Measurement for Wind Speed Time Series

by Xing Deng, Haijian Shao, Xia Wang

1 School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
2 School of Automation, Key Laboratory of Measurement and Control for CSE, Ministry of Education, Southeast University, Nanjing, 210096, China
3 School of Information Science and Technology, Nantong University, Nantong, 226019, China
4 Department of Electrical and Computer Engineering, National University of Singapore, 117583, Singapore

* Corresponding Author: Haijian Shao. Email:

Energy Engineering 2020, 117(5), 289-299. https://doi.org/10.32604/EE.2020.011126

Received 22 April 2020; Accepted 09 July 2020; Issue published 07 September 2020

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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Keywords

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