Chaotic Analysis and Prediction of Wind Speed Based on Wavelet Decomposition

Li Lin,Dandan Xia,Qingsong Zheng,Liming Dai,Zhiqin Qin

doi:10.3390/pr9101793

Abstract

Studying the characteristics of wind speed is essential in wind speed prediction. Based on long-term observed wind speed data, fractal dimension analysis of wind speed was first conducted at different scales, and persistence in wind speed was evaluated based on fractal dimensions in this paper. To propose a more accurate model for wind speed prediction, the wavelet decomposition method was applied to separate the high-frequency dynamics of wind speed data from the low-frequency dynamics. Chaotic behaviors were studied for each decomposed component using the largest Lyapunov exponents method. A proposed hybrid prediction method combining wavelet decomposition, a chaotic prediction method and a Kalman filter method was investigated for short-term wind speed prediction. Simulation results showed that the proposed method can significantly improve prediction accuracy.

Highlights

The main purpose of the chaotic analysis of wind speed data is to provide an accurate understanding of wind characteristics for wind speed prediction, which is the most essential factor for a reliable forecast of wind power
The analysis of wind speed data has been increasingly essential in wind engineering due to its significance in wind-induced vibration, the design of structures and wind power industry areas
Long-term wind speed data measured in Pingtan, China, were considered subject to persistence analysis, chaotic analysis and prediction

Summary

Introduction

Due to numerous meteorological factors, wind speed shows fluctuations on all time scales, resulting in an effect on wind power generation. Studies on wind speed characteristics have been conducted regarding wind–structure interactions, wind power generation, and wind speed prediction. Chaotic characteristics have been proven to exist in wind speed data using the largest Lyapunov exponent, power spectrum, and fractal and dimensional analytical methods [5,6,7,8]. The main purpose of the chaotic analysis of wind speed data is to provide an accurate understanding of wind characteristics for wind speed prediction, which is the most essential factor for a reliable forecast of wind power. Studies on wind prediction models can be classified into physics-based models and statistical models.

Methods

Results

Conclusion