Earticle

초록

영어

Special Complex non-Gaussian processes may have dynamic operation scenario shifts so that the traditional Outlier detection approaches become ill-suited. This paper proposes a new outlier detection approach based on using subspace learning and Gaussian mixture model(GMM) in energy disaggregation. Locality preserving projections(LPP) of subspace learning can optimally preserve the neighborhood structure, reveal the intrinsic manifold structure of the data and keep outliers far away from the normal sample compared with the principal component analysis (PCA). The results show proposed approach can significantly improve performance of outlier detection in energy disaggregation, increase the fraction true-positive from 93.8% to 97%, decrease the fraction false-positive from 35.48% to 25.8%.

목차

Abstract
 1. Introduction
 2. LPP of Subspace Learning
 3. Outlier Detection Using GMM Based On LPP
 4. Experiment and Result Analysis
  4.1. Data Description of Energy Disaggregation In House
  4.2. Experiments and Result Analysis
 5. Conclusion and Future Work
 Acknowledgements
 References

키워드

저자

• Xiu-ming Tang [ School of Electrical engineering, Wuhan University, Wuhan 430072, P.R. China ]
• Rong-xiang Yuan [ School of Electrical engineering, Wuhan University, Wuhan 430072, P.R. China ]
• Jun Chen [ Institute of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, P.R. China ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605