Abstract
In this paper, we apply clustering analysis of data mining into power system. We adapt K-means clustering algorithm to analyze customer load, analyzing similar behavior between customer of electricity, and we adapt principal component analysis to get the clustering result visible, Simulation and analysis using matlab, and this well verify cluster rationality. The conclusion of this paper can provide important basis to the peak for the power system, stable operation the power system security.
Highlights
On the one hand, in the age of big data such a massive information, data affects our works and lives every second, data mining and clustering analysis is becoming more and more important, on the other hand, With the rapid development of our national economy, the power consumption is larger and larger
Our current power source is mainly rely on thermal power, in order to ensure the stable operation of power system, power dispatch and peak becomes more and more important
We discover that the K-MEANS program and the FCM program have good comprehensive performance, the FCM program are too complex for us to use
Summary
In the age of big data such a massive information, data affects our works and lives every second, data mining and clustering analysis is becoming more and more important, on the other hand, With the rapid development of our national economy, the power consumption is larger and larger. The clustering analysis to customer power load is a key link in power decision. We select K-means clustering algorithm to analyze the customer power load. May balance power load according to different classification. This can provide different service to different kinds of customers. The characteristic of this article is: every detail is analyzed from rom the generation of customer power load to data clustering
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