Research on Massive Data Analysis of Power Utilization Based on Improved K-Means Algorithm and Cloud Computing

For such difficulties as low mining efficiency and large amount of data that the data mining of residential electricity data has to be faced with, the research on improved K-means algorithm and cloud computing-based analysis on massive data of power utilization is performed. Firstly, as the initial cluster center and the value K is difficult to determine in traditional K-means algorithm, an improved K-means algorithm based on density is proposed. Defining the product of sample density, the reciprocal of the average distance between the samples in the cluster, and the distance between the clusters as weight product, the initial center is determined successively according to the maximum weight product method and the accuracy of the clustering is improved. Then, the parallelization of improved K-means algorithm based on MapReduce model is realized and the efficiency of clustering is improved. Finally, the mining experiment of massive power utilization data is carried out on basis of 400 households' electricity data. Taking family as unit, calculate such features as electricity consumption rate during peak hour, load rate, valley load coefficient and the percentage of power using during normal hour, and establish the data vector dimension, completing the clustering of similar user types, while analyzing the behavioral characteristics of each types of users. Experiment results on Hadoop cluster show that the improved algorithm can be stable and efficient operated and it can achieve better clustering effect.