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.

中文翻译：

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短期风速时间序列混沌特性及预测初探

短期风速预测在风电行业具有特殊的意义。但是，由于风系统本身的特点，准确预测短期风速并不容易。为了解决这个问题，本文研究了短期风速时间序列的混沌特性和预测。收集了四个时间尺度的短期风速数据作为研究对象。短期风速时间序列的可预测性由赫斯特指数决定。采用混沌0-1检验法和最大李雅普诺夫指数法分析了不同时间尺度下短时风速的混沌特性。结果表明，短期风速时间序列在不同时间尺度上具有混沌特征。引入相空间重构技术；延迟时间由 C-C 方法确定；嵌入维度是通过 G-P 方法获得的。改进回声状态网络以抑制输入噪声对预测性能的影响。同时，提出一种改进的灰狼优化算法来优化回声状态网络的储备池参数。案例研究的结果表明，与最先进的方法相比，所提出的预测方法提高了预测精度并减少了预测误差。提出一种改进的灰狼优化算法来优化回声状态网络的储备池参数。案例研究的结果表明，与最先进的方法相比，所提出的预测方法提高了预测精度并减少了预测误差。提出一种改进的灰狼优化算法来优化回声状态网络的储备池参数。案例研究的结果表明，与最先进的方法相比，所提出的预测方法提高了预测精度并减少了预测误差。