Clustering Analysis of Electric Load Series Using Clustering Algorithm of Multi-Hierarchy and Detailed Decomposition Based on Data Mining

A load clustering algorithm (CA) using multi-hierarchy and detailed decomposition based on data mining (DM) and its performance evaluation index are proposed, in which the Euclidean distances and mean square deviation of difference series of load series are used to construct optimal criterion of intersection, at the same time the multi-hierarchy and detailed decomposition clustering is controlled by adding requirements of corresponding parameters according to the sensitivity of random factors to loads, and it is an important base for improving load forecasting precision to cluster the detailed extent of load curve’s contour similarity. The performance of the proposed CA is evaluated and compared with that based on the Euclidean distance and that based on Kohonen neural network by simulation, and it is proved that the proposed CA possesses both high sensitivity to loads in different seasons and high recognition ability to complicated relativity of the load time series affected by high temperature and meteorological factors. So the presented CA can improve effectively the load forecasting precision.