Se Hoon Jung, Chang Sun Shin, Young Yun Cho, Jang Woo Park, Myung Hye Park, Young Hyun Kim, Seung Bae Lee, Chun Bo Sim

KIPS Transactions on Software and Data Engineering, Vol. 6, No. 10, pp. 465-472, Oct. 2017
10.3745/KTSDE.2017.6.10.465,   PDF Download:
Keywords: Data Analysis, Electric Power, Unsupervised Learning, Outlier, PCA
Abstract

In the past, researchers mainly used the supervised learning technique of machine learning to analyze power data and investigated the identification of patterns through the data mining technique. Data analysis research, however, faces its limitations with the old data classification and analysis techniques today when the size of electric power data has increased with the possible real-time provision of data. This study thus set out to propose a clustering architecture to analyze large-sized electric power data. The clustering process proposed in the study supplements the K-means algorithm, an unsupervised learning technique, for its problems and is capable of automating the entire process from the collection of electric power data to their analysis. In the present study, power data were categorized and analyzed in total three levels, which include the row data level, clustering level, and user interface level. In addition, the investigator identified K, the ideal number of clusters, based on principal component analysis and normal distribution and proposed an altered K-means algorithm to reduce data that would be categorized as ideal points in order to increase the efficiency of clustering.

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