风电场短期风速的多变量局域预测法

风电场短期风速的统计预测方法大都基于单变量风速时间序列,预测精度有限,而在多变量预测中选取哪些变量又没有明确的方法。针对此问题,提出一种风电场短期风速的多变量局域预测法,该方法基于相关性原则来筛选多变量时间序列数据并构造多变量相空间,在该相空间中寻找预测状态点的邻域点并建立支持向量回归(support vectorregression,SVR)模型。采用风电场实测数据进行验证,结果表明:在构造相空间时,增加彼此相关程度低的变量数目,能够明显提升局域法的搜索能力,找到与预测点相似程度更高的邻域点并将其用于模型训练;同时结合SVR模型的高维非线性拟合能力,有效地提高了短期风速预测精度。

Multivariate Local Prediction Method for Short-term Wind Speed of Wind Farm

Most statistics prediction methods for short-term wind speed are based on univariate wind speed time series,they have limited prediction accuracy;however there is no specific method for selecting variables of multivariate prediction.This paper presented a multivariate local predictor for short-term wind speed prediction of wind farm.It sifted multivariate time series by correlation principle to reconstruct multivariate phase space,and searched the neighborhood of the prediction state points to build the support vector regression models.The data of real-world collected from a wind farm was applied to verify the conclusions.The example results show that the proposed method can improve the searching efficiency of local predictor that can find much more similar neighbor points.And combining with support vector regression(SVR) model that could provide good capability of nonlinear fitness,it can effectively improve the accuracy of short-term wind speed prediction.