Energy disaggregation using ensemble of classifiers

We study an approach towards energy disaggregation using ensemble of classifiers, a supervised machine learning method. Specifically we identify different appliance loads from the aggregated power usage data. Experimental results on a public data sets show the accuracy of ensemble of classifiers using diverse features in identifying appliance loads.

[1]  Eric C. Larson,et al.  Disaggregated End-Use Energy Sensing for the Smart Grid , 2011, IEEE Pervasive Computing.

[2]  Robert E. Schapire,et al.  The strength of weak learnability , 1990, Mach. Learn..

[3]  Milde M. S. Lira,et al.  Combining Multiple Artificial Neural Networks Using Random Committee to Decide upon Electrical Disturbance Classification , 2007, 2007 International Joint Conference on Neural Networks.

[4]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[5]  Omar Abou Khaled,et al.  Machine learning approaches for electric appliance classification , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[6]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Suhuai Luo,et al.  Literature review of power disaggregation , 2011, Proceedings of 2011 International Conference on Modelling, Identification and Control.

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[10]  A.C. Liew,et al.  Neural-network-based signature recognition for harmonic source identification , 2006, IEEE Transactions on Power Delivery.

[11]  Yi Du,et al.  A review of identification and monitoring methods for electric loads in commercial and residential buildings , 2010, 2010 IEEE Energy Conversion Congress and Exposition.

[12]  Ashfaqur Rahman,et al.  Novel Layered Clustering-Based Approach for Generating Ensemble of Classifiers , 2011, IEEE Transactions on Neural Networks.