research-article

Estimating building consumption breakdowns using ON/OFF state sensing and incremental sub-meter deployment

Authors:

Andreas SavvidesAuthors Info & Claims

SenSys '10: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems

Pages 225 - 238

https://doi.org/10.1145/1869983.1870006

Published: 03 November 2010 Publication History

Abstract

This paper considers the problem of estimating the power breakdowns for the main appliances inside a building using a small number of power meters and the knowledge of the ON/OFF states of individual appliances. First we solve the breakdown estimation problem within a tree configuration using a single power meter and the knowledge of ON/OFF states and use the solution to derive an estimation quality metric. Using this metric, we then propose an algorithm for optimally placing additional power meters to increase the estimation certainty for individual appliances to the required level. The proposed solution is evaluated using real measurements, numerical simulations and by constructing a scaled down proof-of-concept prototype using binary sensors.

References

[1]

Arch rock. http://http://www.archrock.com/.

[2]

The energy detective (ted). http://www.theenergydetective.com/.

[3]

Millennial net. http://www.millennial.net/.

[4]

Sensicast. http://www.sensicast.com/.

[5]

The tendril residential energy ecosystem (tree). http://www.tendrilinc.com/.

[6]

Watts up.net. http://www.wattsupmeters.com/.

[7]

R. Chellappa and A. K. Jain. Markov random Fields, theory and applications. Academic Press, Boston, 1993.

[8]

G. W. Hart. Nonintrusive appliance load monitoring. In roceedings of the IEEE, December 1992.

[9]

X. Jiang, M. Van Ly, J. Taneja, P. Dutta, and D. Culler. Experiences with a high-fidelity wireless building energy auditing network. In SenSys '09: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, 2009.

Digital Library

[10]

Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. Viridiscope: design and implementation of a fine grained power monitoring system for homes. In Ubicomp '09, 2009.

Digital Library

[11]

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, Number 4598, 13 May 1983, 220, 4598:671--680, 1983.

[12]

S. B. Leeb, S. R. Shaw, and Jr. Kirtley, J. L. Transient event detection in spectral envelope estimates for nonintrusive load monitoring. Power Delivery, IEEE Transactions on, 10(3), Jul 1995.

[13]

J. Lifton, M. Feldmeier, Y. Ono, C. Lewis, and J. A. Paradiso. A platform for ubiquitous sensor deployment in occupational and domestic environments. In IPSN '07, 2007.

Digital Library

[14]

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

[15]

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

Digital Library

[16]

S. Weisberg. Applied Linear Regression. Wiley, thrid edition, 2005.

Cited By

Milis GPanayiotou CPolycarpou M(2019)SEMIoTICS: Semantically Enhanced IoT-Enabled Intelligent Control SystemsIEEE Internet of Things Journal10.1109/JIOT.2017.27732006:1(1257-1266)Online publication date: Feb-2019
https://doi.org/10.1109/JIOT.2017.2773200
T. BL.T. M(2019)An Proposal to Energy Consumption Estimation of Residential Loads based on State Sensors Devices2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)10.1109/ISGT-LA.2019.8895448(1-6)Online publication date: Sep-2019
https://doi.org/10.1109/ISGT-LA.2019.8895448
Benedá TManera L(2019)Non-intrusive and Intrusive Energy Monitoring Methods Overview and Their Relation with Household Appliances State Sensors DevicesProceedings of the 4th Brazilian Technology Symposium (BTSym'18)10.1007/978-3-030-16053-1_39(407-415)Online publication date: 29-May-2019
https://doi.org/10.1007/978-3-030-16053-1_39
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Index Terms

Estimating building consumption breakdowns using ON/OFF state sensing and incremental sub-meter deployment
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

SenSys '10: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems

November 2010

461 pages

ISBN:9781450303446

DOI:10.1145/1869983

General Chair:
Jan Beutel
ETH Zurich, Switzerland
,
Program Chairs:
Deepak Ganesan
University of Massachusetts, Amherst
,
Jack Stankovic
University of Virginia

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 November 2010

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SenSys10

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SenSys10: The 8th ACM Conference on Embedded Network Sensor Systems

November 3 - 5, 2010

Zürich, Switzerland

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Overall Acceptance Rate 198 of 990 submissions, 20%

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Cited By

Milis GPanayiotou CPolycarpou M(2019)SEMIoTICS: Semantically Enhanced IoT-Enabled Intelligent Control SystemsIEEE Internet of Things Journal10.1109/JIOT.2017.27732006:1(1257-1266)Online publication date: Feb-2019
https://doi.org/10.1109/JIOT.2017.2773200
T. BL.T. M(2019)An Proposal to Energy Consumption Estimation of Residential Loads based on State Sensors Devices2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)10.1109/ISGT-LA.2019.8895448(1-6)Online publication date: Sep-2019
https://doi.org/10.1109/ISGT-LA.2019.8895448
Benedá TManera L(2019)Non-intrusive and Intrusive Energy Monitoring Methods Overview and Their Relation with Household Appliances State Sensors DevicesProceedings of the 4th Brazilian Technology Symposium (BTSym'18)10.1007/978-3-030-16053-1_39(407-415)Online publication date: 29-May-2019
https://doi.org/10.1007/978-3-030-16053-1_39
Milis GPanayiotou CPolycarpou M(2018)Semantically Enhanced Online Configuration of Feedback Control SchemesIEEE Transactions on Cybernetics10.1109/TCYB.2017.268074048:3(1081-1094)Online publication date: Mar-2018
https://doi.org/10.1109/TCYB.2017.2680740
Reinhardt A(2017)Adaptive load signature coding for electrical appliance monitoring over low-bandwidth communication channels2017 Sustainable Internet and ICT for Sustainability (SustainIT)10.23919/SustainIT.2017.8379797(1-8)Online publication date: Dec-2017
https://doi.org/10.23919/SustainIT.2017.8379797
Tan RPhillips DMoazzami MXing GChen J(2017)Unsupervised Residential Power Usage Monitoring Using a Wireless Sensor NetworkACM Transactions on Sensor Networks10.1145/307824013:3(1-28)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1145/3078240
Soto-Mendoza VGarca-Macas JChvez EGomez-Montalvo JQuintana E(2017)Detecting abnormal behaviours of institutionalized older adults through a hybrid-inference approachPervasive and Mobile Computing10.1016/j.pmcj.2017.06.01940:C(708-723)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.pmcj.2017.06.019
Englert FLieser PRettberg-Paplow MRömer KLangendoen KVoigt T(2016)LightMon: Apportioning the Effect of Light Switching Events on the Electricity Consumption of BuildingsProceedings of the 2016 International Conference on Embedded Wireless Systems and Networks10.5555/2893711.2893724(77-88)Online publication date: 15-Feb-2016
https://dl.acm.org/doi/10.5555/2893711.2893724
Campbell BClark MDeBruin SGhena BJackson NKuo YDutta P(2016)Perpetual Sensing for the Built EnvironmentIEEE Pervasive Computing10.1109/MPRV.2016.6615:4(45-55)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1109/MPRV.2016.66
Almeida GVieira JSá Marques ACardoso ALudwig O(2016)Identifying Household Water Use through Transient Signal ClassificationJournal of Computing in Civil Engineering10.1061/(ASCE)CP.1943-5487.000047630:2Online publication date: Mar-2016
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000476
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