Preliminary Research of Chaotic Characteristics and Prediction of Short-Term Wind Speed Time Series

Abstract

Short-term wind speed prediction has its special significance in wind power industry. However, due to the characteristics of the wind system itself, it is not easy to predict the short-term wind speed accurately. In order to solve the problem, this paper studies the chaotic characteristics and prediction of short-term wind speed time series. The short-term wind speed data at four time scales are collected as the research object. The predictability of short-term wind speed time series is determined by the Hurst exponent. The chaotic characteristics of short-time wind speed at different time scales are analyzed by the 0–1 test method for chaos and the maximum Lyapunov exponent method. The results show that the short-term wind speed time series has chaotic characteristics at different time scales. The phase-space reconstruction technology is introduced; delay time is determined by the C–C method; embedding dimension is obtained by the G–P method. Echo state network is improved to suppress the influence of input noise on prediction performance. At the same time, an improved grey Wolf optimization algorithm is proposed to optimize the parameters of reserve pool of the echo state network. The results of a case study show that, compared with state-of-the-art methods, the proposed prediction method improves the prediction accuracy and reduces the predictive errors.