Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68701
Title: Wind power station wind speed prediction method based on wavelet analysis and system thereof
Other Titles: 基于小波分析的风电场风速预测方法及系统
Authors: Dong, CY 
Huang, JB 
Meng, K 
Issue Date: 30-May-2012
Publisher: 中华人民共和国国家知识产权局
Source: 中国专利 ZL 201010560929.1 How to cite?
Abstract: The invention relates to a wind power station wind speed prediction method based on wavelet analysis and a system thereof. The method comprises the following steps: according to a specific prediction time interval, determining an input and an output variable of a prediction model; reading a historical wind speed value and correcting an incomplete point in the historical wind speed value so as to acquire a training sample value sequence of a wind speed prediction model; carrying out rapid wavelet decomposition to the training sample value sequence so as to acquire an approximation detail component value sequence; establishing the wind speed prediction model according to the approximation detail component value sequence so as to carry out the wind speed prediction. According to the wind power station wind speed prediction method based on the wavelet analysis and the system of the invention, through the wavelet decomposition, the training sample value sequence is decomposed into different layers according to a scale so that a trend term, a period term and a random term are separated. Each layer is individually analyzed and predicted and finally the corresponding prediction value can be obtained through reconstruction. By using the method, any prediction interval can be selected according to different demands. The wind speed prediction which is many steps ahead and has high precision can be performed.
本发明涉及基于小波分析的风电场风速预测方法及系统,该方法包括以下步骤:根据一特定的预测时间间隔,确定预测模型的输入和输出变量;读取历史风速值,修正所述历史风速值中的残缺点,以得到风速预测模型的训练样本值序列;对所述训练样本值序列进行快速小波分解,以得到近似细节分量值序列;根据所述近似细节分量值序列,建立所述风速预测模型,以进行风速预测。本发明的基于小波分析的风电场风速预测方法及系统,通过小波分解,将训练样本值序列依尺度分解成不同层次,使趋势项、周期项和随机项分离,对每一层进行单独分析与预测,最后重构得到相应的预测值。而且本方法可以按照不同需求,选择任意的预测间隔,进行超前多步,高精度风速预测。
URI: http://hdl.handle.net/10397/68701
Rights: 专利权人: The Hong Kong Polytechnic University.
Appears in Collections:Patent

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