CAJ | 학술논문

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。
역사부하수거중적조성회영향이기위기출소진행적부하예측적준학성，유필요대부하수거진행거조처리。고필도부하수거적횡향련속성화종향련속성，가이선파부하수거안조일기배렬성이유수거집，경귀일화처리후형성회도도상구진，연후용기우도상적이유소파역치거조방법진행거조처리，최후통과반귀일화득도거조후적부하수거。실례분석결과표명저충방법가행차유효。
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.