research-article

Monitoring massive appliances by a minimal number of smart meters

Authors:

Lu YuAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 13, Issue 2s

Article No.: 56, Pages 1 - 20

https://doi.org/10.1145/2544375.2544376

Published: 27 January 2014 Publication History

Abstract

This article presents a framework for deploying a minimal number of smart meters to accurately track the ON/OFF states of a massive number of electrical appliances which exploits the sparseness feature of simultaneous ON/OFF switching events of the massive appliances. A theoretical bound on the least number of required smart meters is studied by an entropy-based approach, which qualifies the impact of meter deployment strategies to the state tracking accuracy. It motivates a meter deployment optimization algorithm (MDOP) to minimize the number of meters while satisfying given requirements to state tracking accuracy. To accurately decode the real-time ON/OFF states of appliances by the readings of meters, a fast state decoding (FSD) algorithm based on the hidden Markov model (HMM) is presented to track the state sequence of each appliance for better accuracy. Although traditional HMM needs O(t2^2N) time complexity to conduct online sequence decoding, FSD improves the complexity to O(tn^U+1), where n < N and U is an upper bound of the simultaneous switching events. Both MDOP and FSD are verified extensively using simulations and real PowerNet data. The results show that the meter deployment cost can be saved by more than 80% while still getting over 90% state tracking accuracy.

References

[1]

S. Dawson-Haggerty, S. Lanzisera, J. Taneja, R. Brown, and D. Culler. 2012. &commat;scale: Insights from a large, long-lived appliance energy WSN. In Proceedings of the ACM IPSN.

Digital Library

[2]

Energy hub. 2009. Energy hub. http://www.energyhub.net/.

[3]

L. Farinaccio and R. Zmeureanu. 1999. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy Build. 30, 3, 245--259.

[4]

G. D. Forney, Jr. 1973. The Viterbi algorithm. Proc. IEEE 61, 3, 268--278.

[5]

GreenBox. 2009. Green box. http://www.getgreenbox.com/.

[6]

S. Gupta, M. S. Reynolds, and S. N. Patel. 2010. Electrisense: Single-point sensing using EMI for electrical event detection and classification in the home. In Proceedings of Ubicomp'10. ACM, New York, NY, 139--148.

Digital Library

[7]

X. Hao, Y. Wang, C. Wu, L. Song, and Y. Wang. 2012a. Smart meter deployment for efficient appliance state monitoring. In Proceedings of the 3rd IEEE International Conference on Smart Grid Communications. 25--30.

[8]

X. Hao, Y. Wang, C. Wu, A. Y. Wang, and L. Song. 2012b. Proof of approximation ratio and complexity of MDOP algorithm. http://wcy.name/papers/proof2.pdf.

[9]

G. W. Hart. December 1992. Nonintrusive appliance load monitoring. Proc. IEEE 80, 12, 1870--1891.

[10]

X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. 2009a. Design and implementation of a high-fidelity AC metering network. In Proceedings of ACM IPSN'09. IEEE Computer Society, 253--264.

Digital Library

[11]

X. Jiang, M. Van Ly, J. Taneja, P. Dutta, and D. Culler. 2009b. Experiences with a high-fidelity wireless building energy auditing network. In Proceedings of SenSys'09. ACM, New York, NY, 113--126.

Digital Library

[12]

D. Jung and A. Savvides. 2010. Estimating building consumption breakdowns using on/off state sensing and incremental sub-meter deployment. In Proceedings of SenSys'10. ACM, New York, NY, 225--238.

Digital Library

[13]

M. A. Kazandjieva, B. Heller, P. Levis, and C. Kozyrakis. 2009. Energy dumpster diving. In Proceedings of the 2nd Workshop on Power Aware Computing (HotPower'09).

[14]

Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. 2009. ViridiScope: Design and implementation of a fine grained power monitoring system for homes. In Proceedings of Ubicomp'09. ACM, New York, NY, 245--254.

Digital Library

[15]

S. Leeb, S. Shaw, and J. L. Kirtley, Jr. 1995. Transient event detection in spectral envelope estimates for nonintrusive load monitoring. IEEE Trans. Power Delivery 10, 3, 1200--1210.

[16]

J. Lifton, M. Feldmeier, Y. Ono, C. Lewis, and J. A. Paradiso. 2007. A platform for ubiquitous sensor deployment in occupational and domestic environments. In Proceedings of IPSN'07. ACM, New York, NY, 119--127.

Digital Library

[17]

L. K. Norford and S. B. Leeb. 1996. Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy Build. 24, 1, 51--64.

[18]

Oxford St. Hughs College Data. 2010. Energy and water conservation. http://www.st-hughs.ox.ac.uk/welfare-and-facilities/estates/energy-and-water-conservation.

[19]

S. N. Patel, T. Robertson, J. A. Kientz, M. S. Reynolds, and G. D. Abowd. 2007. At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In Proceedings of Ubicomp'07. 271--288.

Digital Library

[20]

A. Rowe, M. Berges, and R. Rajkumar. 2010. Contactless sensing of appliance state transitions through variations in electromagnetic fields. In Proceedings of BuildSys'10. ACM, New York, NY, 19--24.

Digital Library

[21]

Z. C. Taysi, M. A. Guvensan, and T. Melodia. 2010. Tinyears: Spying on house appliances with audio sensor nodes. In Proceedings of BuildSys'10. ACM, New York, NY, 31--36.

Digital Library

[22]

Tendril. 2012. The Tendril residential energy ecosystem. http://www.tendrilinc.com/.

[23]

R. Tibshirani. 1994. Regression shrinkage and selection via the lasso. J. Royal Stat. Soci. Series B 58, 267--288.

[24]

Y. Wang, X. Hao, L. Song, C. Wu, Y. Wang, C. Hu, and L. Yu. 2012. Tracking states of massive electrical appliances by lightweight metering and sequence decoding. In Proceedings of the 6th International Workshop on SensorKDD'12.

Digital Library

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Index Terms

Monitoring massive appliances by a minimal number of smart meters
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 13, Issue 2s

Special Section ESFH'12, ESTIMedia'11 and Regular Papers

January 2014

409 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/2544375

Editors:
Naehyuck Chang
Seoul National University (SNU), Korea
,
Jian-Jia Chen
Karlsruhe Institute of Technology (KIT), Germany

Issue’s Table of Contents

Copyright © 2014 ACM.

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 27 January 2014

Accepted: 01 April 2013

Revised: 01 November 2012

Received: 01 July 2012

Published in TECS Volume 13, Issue 2s

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https://dl.acm.org/doi/10.1109/TC.2024.3500373
Xu JWang XJiang YSingh AGu CHuang LYang MLi S(2022)Secured Data Transmission Over Insecure Networks-on-Chip by Modulating Inter-Packet DelaysIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319753041:11(4313-4324)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1109/TCAD.2022.3197530
Li SHuo SKe W(2021)Intelligent Decision-Making System for Martial Arts Competition Using Deep LearningMobile Information Systems10.1155/2021/99207512021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/9920751
Djenouri DLaidi RDjenouri YBalasingham I(2019)Machine Learning for Smart Building ApplicationsACM Computing Surveys10.1145/331195052:2(1-36)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3311950
Tadayon NAissa S(2018)Radio Resource Allocation and PricingIEEE Transactions on Signal Processing10.1109/TSP.2018.286239866:20(5240-5254)Online publication date: 1-Oct-2018
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Li BWang XSingh AMak T(2017)On Runtime Communication- and Thermal-aware Application Mapping in 3D NoCProceedings of the Eleventh IEEE/ACM International Symposium on Networks-on-Chip10.1145/3130218.3130228(1-8)Online publication date: 19-Oct-2017
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Wang YZhang XChao LWu LPeng X(2017)PowerAnalyzer: An energy-aware power monitor system aiming at energy-saving2017 Eighth International Green and Sustainable Computing Conference (IGSC)10.1109/IGCC.2017.8323568(1-8)Online publication date: Oct-2017
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Tang JTang GWu K(2016)MinNetInternational Journal of Sensor Networks10.1504/IJSNET.2016.07672720:4(252-263)Online publication date: 1-May-2016
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