电力负荷数据预处理的二维小波阈值去噪方法

历史负荷数据中的噪声会影响以其为基础所进行的负荷预测的准确性,有必要对负荷数据进行去噪处理。考虑到负荷数据的横向连续性和纵向连续性,可以先把负荷数据按照日期排列成二维数据集,经归一化处理后形成灰度图像矩阵,然后用基于图像的二维小波阈值去噪方法进行去噪处理,最后通过反归一化得到去噪后的负荷数据。实例分析结果表明这种方法可行且有效。

A Two-dimension Wavelet Threshold De-noising Method for Electric Load Data Pre-processing

There are usually some noises in the historical load data,and the accuracy of load forecasting could then be impacted.Hence,it is necessary to de-noise the noises before the load data are used for load forecasting or power system analysis.Considering both horizontal and vertical continuities of electric loads,a new method for load de-noising is presented based on a two-dimension wavelet threshold de-noising method.First,the load data is transformed into a matrix of gray-scale images by normalization.Next,the images are processed by employing the two-dimension wavelet threshold de-noising method.Finally,the de-noised data are obtained after de-normalization.The feasibility and efficiency of the developed method are demonstrated by the improvement of the load forecasting accuracy of an actual example.