Earticle

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

키워드

저자

• 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 ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605