research-article

Theory and Algorithm of Estimating Energy Consumption Breakdowns using ON/OFF State Sensing

Authors:

Andreas SavvidesAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 11, Issue 1

Article No.: 5, Pages 1 - 36

https://doi.org/10.1145/2630880

Published: 08 September 2014 Publication History

Abstract

This article considers a problem of periodically estimating energy consumption breakdowns for main appliances inside building using a single power meter and the knowledge of the ON/OFF states of individual appliances. In the first part of this article, we formulate the problem as a constrained convex optimization problem with tunable parameters. Then we propose an online algorithm that adaptively determines the optimization parameters to robustly estimate the breakdown information. The proposed solution is evaluated by experiment using a scaled-down proof-of-concept prototype with real measurements. In the second part, we provide detailed analysis to understand the performance of our proposed algorithm. We first develop a stochastic model to describe evolution of appliances’ ON/OFF states using continuous-time Markov chain. Then we derive analytical bounds of estimation error and the probability of a rank-deficient binary matrix. Those analytical bounds are verified by extensive simulations. Finally, we study the effect of collinearity of binary data matrix on estimation performance. Simulation results suggest that our algorithm is robust against the collinearity of binary dataset.

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Cited By

Sovero GKemper NKussul E(2017)Load Signature Generator for Appliance State Sensing and Energy DisaggregationIEEE Sensors Journal10.1109/JSEN.2017.271095617:14(4587-4594)Online publication date: 15-Jul-2017
https://doi.org/10.1109/JSEN.2017.2710956
Jee JDas APathak PMohapatra P(2016)MotionSyncProceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/2993422.2993572(65-74)Online publication date: 16-Nov-2016
https://dl.acm.org/doi/10.1145/2993422.2993572

Index Terms

Theory and Algorithm of Estimating Energy Consumption Breakdowns using ON/OFF State Sensing

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 11, Issue 1

November 2014

631 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/2648771

Editor:
Chenyang Lu
Department of Computer Science and Engineering / School of Engineering and Applied Sciences / Washington University / 1 Brookings Drive / St. Louis, MO 63130

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Copyright © 2014 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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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: 08 September 2014

Accepted: 01 November 2013

Revised: 01 August 2013

Received: 01 November 2012

Published in TOSN Volume 11, Issue 1

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Human Sixth Sense Programme at the Advanced Digital Sciences Center from Singapore's Agency for Science, Technology and Research (A*STAR)

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Cited By

Sovero GKemper NKussul E(2017)Load Signature Generator for Appliance State Sensing and Energy DisaggregationIEEE Sensors Journal10.1109/JSEN.2017.271095617:14(4587-4594)Online publication date: 15-Jul-2017
https://doi.org/10.1109/JSEN.2017.2710956
Jee JDas APathak PMohapatra P(2016)MotionSyncProceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/2993422.2993572(65-74)Online publication date: 16-Nov-2016
https://dl.acm.org/doi/10.1145/2993422.2993572

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