Energy disaggregation for real-time building flexibility detection

Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy.

[1]  F. Sultanem,et al.  Using appliance signatures for monitoring residential loads at meter panel level , 1991 .

[2]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[3]  Jack Kelly,et al.  Metadata for Energy Disaggregation , 2014, 2014 IEEE 38th International Computer Software and Applications Conference Workshops.

[4]  Tommaso Melodia,et al.  Energy monitoring in residential spaces with audio sensor nodes: TinyEARS , 2013, Ad Hoc Networks.

[5]  Muhd Zaimi Abd Majid,et al.  A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries) , 2015 .

[6]  Muhammad Ali Imran,et al.  Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey , 2012, Sensors.

[7]  Matthew J. Johnson,et al.  Bayesian nonparametric hidden semi-Markov models , 2012, J. Mach. Learn. Res..

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

[9]  Pascal Vincent,et al.  Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines , 2010, AISTATS.

[10]  Gabriela Hug,et al.  A moving horizon state estimator in the control of thermostatically controlled loads for demand response , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

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

[12]  Geoffrey E. Hinton,et al.  Using fast weights to improve persistent contrastive divergence , 2009, ICML '09.

[13]  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.

[14]  Michael Zeifman,et al.  Nonintrusive appliance load monitoring: Review and outlook , 2011, IEEE Transactions on Consumer Electronics.

[15]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[16]  Tommi S. Jaakkola,et al.  Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation , 2012, AISTATS.

[17]  Naoki Tanaka,et al.  Distributed non-intrusive load monitoring , 2011, ISGT 2011.

[18]  Chi Zhou,et al.  Leveraging smart meters for residential energy disaggregation , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[19]  Manish Marwah,et al.  Unsupervised Disaggregation of Low Frequency Power Measurements , 2011, SDM.

[20]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[21]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[22]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[23]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[24]  Andrew Y. Ng,et al.  Energy Disaggregation via Discriminative Sparse Coding , 2010, NIPS.

[25]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[26]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[27]  Wil L. Kling,et al.  Comparison of machine learning methods for estimating energy consumption in buildings , 2014, 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

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