Outlier Detection in Energy Disaggregation Using Subspace Learning and Gaussian Mixture Model

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%.

[1]  Runze Li,et al.  Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery , 2006, math/0602133.

[2]  Jianbo Yu,et al.  Bearing performance degradation assessment using locality preserving projections , 2011, Expert Syst. Appl..

[3]  Manish Marwah,et al.  A Temporal Motif Mining Approach to Unsupervised Energy Disaggregation: Applications to Residential and Commercial Buildings , 2013, AAAI.

[4]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[5]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[6]  Narayanaswamy Balakrishnan,et al.  Expectation Maximization Algorithm for One Shot Device Accelerated Life Testing with Weibull Lifetimes, and Variable Parameters over Stress , 2013, IEEE Transactions on Reliability.

[7]  Michael Zeifman,et al.  Disaggregation of home energy display data using probabilistic approach , 2012, IEEE Transactions on Consumer Electronics.

[8]  Jie Yu.  A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes , 2012 .

[9]  William J. Knottenbelt,et al.  Disaggregating Multi-State Appliances from Smart Meter Data , 2012 .

[10]  Longbing Cao,et al.  SVDD-based outlier detection on uncertain data , 2012, Knowledge and Information Systems.

[11]  Deng Cai,et al.  Statistical and computational analysis of locality preserving projection , 2005, ICML.

[12]  Junghui Chen,et al.  Removal of the effects of outliers in batch process data through maximum correntropy estimator , 2012 .

[13]  Jay Lee,et al.  A modified support vector data description based novelty detection approach for machinery components , 2013, Appl. Soft Comput..

[14]  Marco Vannucci,et al.  Outlier Detection Methods for Industrial Applications , 2008, ICRA 2008.

[15]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[16]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[17]  Ghassan Hamarneh,et al.  Augmenting Auto-context with Global Geometric Features for Spinal Cord Segmentation , 2013, MLMI.

[18]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.