research-article

Strip, bind, and search: a method for identifying abnormal energy consumption in buildings

Authors:

Romain Fontugne,

Nicolas Tremblay,

Pierre Borgnat,

Patrick Flandrin,

Kensuke Fukuda,

Hiroshi EsakiAuthors Info & Claims

IPSN '13: Proceedings of the 12th international conference on Information processing in sensor networks

Pages 129 - 140

https://doi.org/10.1145/2461381.2461399

Published: 08 April 2013 Publication History

Abstract

A typical large building contains thousands of sensors, monitoring the HVAC system, lighting, and other operational sub-systems. With the increased push for operational efficiency, operators are relying more on historical data processing to uncover opportunities for energy-savings. However, they are overwhelmed with the deluge of data and seek more efficient ways to identify potential problems. In this paper, we present a new approach called the Strip, Bind and Search (SBS); a method for uncovering abnormal equipment behavior and in-concert usage patterns. SBS uncovers relationships between devices and constructs a model for their usage pattern relative to other devices. It then flags deviations from the model. We run SBS on a set of building sensor traces; each containing hundred sensors reporting data flows over 18 weeks from two separate buildings with fundamentally different infrastructures. We demonstrate that, in many cases, SBS uncovers misbehavior corresponding to inefficient device usage that leads to energy waste. The average waste uncovered is as high as 2500~kWh per device.

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Cited By

Kaushik KNaik V(2024)Detecting faults in the cooling systems by monitoring temperature and energyEnergy Informatics10.1186/s42162-024-00351-17:1Online publication date: 17-Jun-2024
https://doi.org/10.1186/s42162-024-00351-1
Dissem MAmayri MBouguila N(2024)Neural Architecture Search for Anomaly Detection in Time-Series Data of Smart Buildings: A Reinforcement Learning Approach for Optimal Autoencoder DesignIEEE Internet of Things Journal10.1109/JIOT.2024.336088211:10(18059-18073)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3360882
Golchin BRekabdar B(2024)Anomaly Detection In Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning2024 Conference on AI, Science, Engineering, and Technology (AIxSET)10.1109/AIxSET62544.2024.00007(1-8)Online publication date: 30-Sep-2024
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Index Terms

Strip, bind, and search: a method for identifying abnormal energy consumption in buildings
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

IPSN '13: Proceedings of the 12th international conference on Information processing in sensor networks

April 2013

372 pages

ISBN:9781450319591

DOI:10.1145/2461381

General Chair:
Tarek Abdelzaher
University of Illinois, Urbana-Champaign
,
Program Chairs:
Kay Römer
University of Lübeck & ETH Zurich
,
Raj Rajkumar
Carnegie Mellon University

Copyright © 2013 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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IEEE Signal Processing Society
SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2013

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SIGBED

IPSN 2013: The 12th International Conference on Information Processing in Sensor Networks

April 8 - 11, 2013

Pennsylvania, Philadelphia, USA

Acceptance Rates

IPSN '13 Paper Acceptance Rate 24 of 115 submissions, 21%;

Overall Acceptance Rate 143 of 593 submissions, 24%

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https://doi.org/10.1186/s42162-024-00351-1
Dissem MAmayri MBouguila N(2024)Neural Architecture Search for Anomaly Detection in Time-Series Data of Smart Buildings: A Reinforcement Learning Approach for Optimal Autoencoder DesignIEEE Internet of Things Journal10.1109/JIOT.2024.336088211:10(18059-18073)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3360882
Golchin BRekabdar B(2024)Anomaly Detection In Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning2024 Conference on AI, Science, Engineering, and Technology (AIxSET)10.1109/AIxSET62544.2024.00007(1-8)Online publication date: 30-Sep-2024
https://doi.org/10.1109/AIxSET62544.2024.00007
Farouq SByttner SBouguelia MGadd H(2022)A conformal anomaly detection based industrial fleet monitoring frameworkExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116864201:COnline publication date: 1-Sep-2022
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Safaei MAsadi SDriss MBoulila WAlsaeedi AChizari HAbdullah RSafaei M(2020)A Systematic Literature Review on Outlier Detection in Wireless Sensor NetworksSymmetry10.3390/sym1203032812:3(328)Online publication date: 25-Feb-2020
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