基于时间序列二维分段云模型的配网用电模式划分方法的研究

Time Series Two-Dimensional Piecewise Cloud Model for Distribution Network Power Consumption Mode Partition

摘要: 在对配网用电模式进行划分时，为了解决用电量等时间序列数据长度过大及相似性度量不精确的问题，本文提出了一种基于二维分段云模型的时间序列分析方法。该方法首先将用电时间序列数据用二维的分段云模型来表示，然后在基于计算期望曲线重叠面积的方法上对不同云模型的相似度进行度量，最后通过K-最邻近算法对这些用电时间序列进行分类，并将实验结果与传统方法进行比较。实验结果表明：该方法能有效提高对配网用户侧用电模式分类的准确率。

Abstract: In order to solve the problem that the length of time series data such as electricity consumption is too large and the similarity measurement is not accurate, a time series analysis method based on two-dimensional piecewise cloud model is proposed in this paper. In this method, the power consumption time series data are first represented by a two-dimensional piecewise cloud model, and then the similarity of different cloud models is measured based on the method of calculating the overlapping area of the expected curve. Finally, these time series are classified by K-nearest neighbor algorithm, and the experimental results are compared with the traditional methods. The experimental results show that this method can effectively improve the accuracy of power consumption pattern classification on the user side of distribution network.