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      • Load Pattern Window Aware Power Supply Device Clustering

        Wanxing Sheng,Ke-yan Liu,Yixi Yu,Rungong An,Ningnan Zhou,Xiao Zhang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.8

        Data-driven decision in big data era is becoming ubiquitous in electronic grid. In particular, daily collected power consumption records enable workload aware device clustering, which is crucial for critical domain applications such as device functionality identification. In this paper, we propose a load pattern window aware method for clustering power supply devices. Our approach overcomes the drawbacks in existing works, such as fuzzy based clustering, K-means based clustering and neutral network based clustering. After investigating the large scale records from power supply devices, our approach partitions device records into disjoint time intervals with parameterized window size, which indicate the load pattern feature for a period of time given a specific device. Devices are then decomposed into a mixture of these features, and those devices with similar dominating features are grouped together. The experimental results demonstrate the effectiveness and efficiency of our solution based on the real data collected from power grid in China.

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