A hidden Markov model based procedure for identifying household electric loads

In automated energy management systems, to make instantaneous decisions based on the appliance status information, continuous data access is a key requirement. With the advances in sensor and communication technologies, it is now possible to remotely monitor the power consumption data. However, before an appliance is actively monitored, it must be identified using the obtained power consumption data. Appropriate methods are required to analyse power consumption patterns for proper appliance recognition. The focus of this work is to provide the model structure for storing and distinguishing the recurring footprints of the household appliances. Hidden Markov model based method is proposed to recognize the individual appliances from combined load. It is found that the proposed method can efficiently differentiate the power consumption patterns of appliances from their combined profiles.

[1]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[2]  Lawrence J. Hill.  Residential time-of-use pricing as a load management strategy: Effectiveness and applicability , 1991 .

[3]  Biing-Hwang Juang,et al.  Hidden Markov Models for Speech Recognition , 1991 .

[4]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[5]  James L. Kirtley,et al.  A multiscale transient event detector for nonintrusive load monitoring , 1993, Proceedings of IECON '93 - 19th Annual Conference of IEEE Industrial Electronics.

[6]  A. T. de Almeida,et al.  Advanced monitoring technologies for the evaluation of demand-side management programs , 1994 .

[7]  Thorsten Brants.  Estimating Markov model structures , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[8]  A. Koski.  Modelling ECG signals with hidden Markov models , 1996, Artif. Intell. Medicine.

[9]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[10]  Igor E. Kheidorov,et al.  Speaker Identification Using Autoregressive Hidden Markov Models and Adaptive Vector Quantisation , 2000, TSD.

[11]  A. McMakin,et al.  Motivating Residents to Conserve Energy without Financial Incentives , 2002 .

[12]  A. Prudenzi,et al.  A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[13]  T. Onoda,et al.  Experimental analysis of support vector machines with different kernels based on non-intrusive monitoring data , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[14]  Takashi Onoda,et al.  Estimation of power consumption for household electric appliances , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[15]  Steven B. Leeb,et al.  Power signature analysis , 2003 .

[16]  Hiroshi Murata,et al.  Comparison of Machine Learning Techniques for Estimating the Power Consumption of Household Electric Appliances , 2003 .

[17]  M. Newborough,et al.  Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design , 2003 .

[18]  M. Baranski,et al.  Genetic algorithm for pattern detection in NIALM systems , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[19]  Ching Y. Suen,et al.  On the structure of hidden Markov models , 2004, Pattern Recognit. Lett..

[20]  Gernot A. Fink,et al.  Markov Models for Pattern Recognition: From Theory to Applications , 2007 .

[21]  Alex Bateman,et al.  An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.

[22]  P. Palensky,et al.  A simulation platform for distributed energy optimization algorithms , 2008, 2008 6th IEEE International Conference on Industrial Informatics.

[23]  Mani B. Srivastava,et al.  ViridiScope: design and implementation of a fine grained power monitoring system for homes , 2009, UbiComp.

[24]  Jian Liang,et al.  Automatic appliance load signature identification by statistical clustering , 2009 .

[25]  Dietmar Bruckner,et al.  Behavior Learning in Dwelling Environments With Hidden Markov Models , 2010, IEEE Transactions on Industrial Electronics.