Fundamental limits of nonintrusive load monitoring

Roy Dong, Lillian Ratliff, Henrik Ohlsson, S. Shankar Sastry

Research output: Contribution to conferencePaperpeer-review

Abstract

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. Copyright is held by the flowner/author(s).

Original languageEnglish (US)
Pages11-18
Number of pages8
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event2014 3rd ACM International Conference on High Confidence Networked Systems, HiCoNS 2014, Part of CPSWeek 2014 - Berlin, Germany
Duration: Apr 15 2014Apr 17 2014

Other

Other2014 3rd ACM International Conference on High Confidence Networked Systems, HiCoNS 2014, Part of CPSWeek 2014
CountryGermany
CityBerlin
Period4/15/144/17/14

ASJC Scopus subject areas

  • Computer Networks and Communications

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