Fundamental limits of nonintrusive load monitoring

Provided an arbitrary nonintrusive load monitoring (NILM) algorithm, we seek bounds on the probability of distinguishing between scenarios, given an aggregate power consumption signal. We introduce a framework for studying a general NILM algorithm, and analyze the theory in the general case. Then, we specialize to the case where the error is Gaussian. In both cases, we are able to derive upper bounds on the probability of distinguishing scenarios. Finally, we apply the results to real data to derive bounds on the probability of distinguishing between scenarios as a function of the measurement noise, the sampling rate, and the device usage.

[1]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[2]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[3]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[4]  S. R. Shaw,et al.  Transient event detection in spectral envelope estimates for nonintrusive load monitoring , 1995 .

[5]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[6]  J. Cardoso.  Infomax and maximum likelihood for blind source separation , 1997, IEEE Signal Processing Letters.

[7]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[8]  G. T. Gardner,et al.  The Short List: The Most Effective Actions U.S. Households Can Take to Curb Climate Change , 2008 .

[9]  Shwetak N. Patel,et al.  ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home , 2010, UbiComp.

[10]  Kamalika Chaudhuri,et al.  Sample Complexity Bounds for Differentially Private Learning , 2011, COLT.

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

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

[13]  Geir E. Dullerud,et al.  Differentially private iterative synchronous consensus , 2012, WPES '12.

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

[15]  Alex Rogers,et al.  Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types , 2012, AAAI.

[16]  S. Shankar Sastry,et al.  A game theory model for electricity theft detection and privacy-aware control in AMI systems , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[17]  K. Armel,et al.  Is disaggregation the holy grail of energy efficiency? The case of electricity , 2013 .

[18]  Henrik Ohlsson,et al.  A dynamical systems approach to energy disaggregation , 2013, 52nd IEEE Conference on Decision and Control.

[19]  Henrik Ohlsson,et al.  Energy disaggregation via adaptive filtering , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[20]  George J. Pappas,et al.  Differentially Private Filtering , 2012, IEEE Transactions on Automatic Control.