基于自相关分析的风速最佳预报长度度量方法研究

The measurement study of the optimal wind speed predictable length based on autocorrelation analysis

风速预报对于大规模风电并网具有重要的意义。基于历史数据的统计建模方法是目前最常用的风速预报方法。然而,采用该方法进行风速预报的可预报性问题,尤其是风速最佳预报时长问题,目前的研究中没有相应的定量描述方法。文章基于自相关分析的方法对风速的最佳预报长度问题进行了研究,利用大量的实测风速数据进行了实验分析。研究分析发现:随着相关长度的增加,预报误差先下降后上升;误差转折点对应的相关尺度约为4 h,可以作为利用历史数据进行风速预报的最佳预报长度。因此,可以利用自相关分析对可预报性进行度量,进一步地细化风速预报研究。

Wind speed prediction is of great significance for wind power integration. The most common prediction method is statistical modeling method based on historical data. However,the study of predictability is scarce in current literature,especially the quantitative measurement of the optimal predictable length in the modeling process. In the present study,the autocorrelation analysis was introduced to analyze the optimal wind speed predictable length. The experimental results based on a large number of measured wind speed data shown that there exists a specific correlation length for the nonlinear wind speed time series. With the increasing of the correlation length, the prediction error gradually decreased and then increased. Thus,the correlation length corresponding to the turning point, which is approximate 4 h, could be recognized as the optimal predictable length. Therefore,autocorrelation analysis could be used to measure the predictability of wind speed,which made a contribution to the further study of wind speed prediction in detail.