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논문검색

Load Pattern Window Aware Power Supply Device Clustering

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Load Pattern Window Aware Clustering
  3.1. Overview
  3.2. Algorithm
 4. Experimental Evaluation
  4.1. Setting
  4.2. Experimental Analysis
 5. Conclusions
 Acknowledgements
 References

저자정보

  • Wanxing Sheng China Electric Power Research Institute, Beijing 100192, China
  • Ke-yan Liu China Electric Power Research Institute, Beijing 100192, China
  • Yixi Yu Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Renmin University of China, Beijing 100872, China, School of Information, Renmin University of China 100872, China
  • Rungong An Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Renmin University of China, Beijing 100872, China, School of Information, Renmin University of China 100872, China
  • Ningnan Zhou Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Renmin University of China, Beijing 100872, China, School of Information, Renmin University of China 100872, China
  • Xiao Zhang Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Renmin University of China, Beijing 100872, China, School of Information, Renmin University of China 100872, China

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