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Zodiac: Organizing Large Deployment of Sensors to Create Reusable Applications for Buildings

Authors:

Bharathan Balaji,

Balakrishnan Narayanaswamy,

Yuvraj AgarwalAuthors Info & Claims

BuildSys '15: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments

Pages 13 - 22

https://doi.org/10.1145/2821650.2821674

Published: 04 November 2015 Publication History

Abstract

Large scale deployment of sensors is essential to practical applications in cyber physical systems. For instance, instrumenting a commercial building for 'smart energy' management requires deployment and operation of thousands of measurement and metering sensors and actuators that direct operation of the HVAC system. Each of these sensors need to be named consistently and constantly calibrated. Doing this process manually is not only time consuming but also error prone given the scale, heterogeneity and complexity of buildings as well as lack of uniform naming schemas. To address this challenge, we propose Zodiac - a framework for automatically classifying, naming and managing sensors based on active learning from sensor metadata. In contrast to prior work, Zodiac requires minimal user input in terms of labelling examples while being more accurate. To evaluate Zodiac, we deploy it across four real buildings on our campus and label the ground truth metadata for all the sensors in these buildings manually. Using a combination of hierarchical clustering and random forest classifiers we show that Zodiac can successfully classify sensors with an average accuracy of 98% with 28% fewer training examples when compared to a regular expression based method.

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Cited By

Benfer RMüller J(2024)Semantic digital twin creation of building systems through time series based metadata inference – A reviewEnergy and Buildings10.1016/j.enbuild.2024.114637321(114637)Online publication date: Oct-2024
https://doi.org/10.1016/j.enbuild.2024.114637
Weber LLenz R(2023)Machine learning in sensor identification for industrial systemsit - Information Technology10.1515/itit-2023-005165:4-5(177-188)Online publication date: 9-Oct-2023
https://doi.org/10.1515/itit-2023-0051
Cui JYap WProsper CBalaji BChen J(2023)Economizer Optimization with Reinforcement Learning: An Industry PerspectiveProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3625685(366-369)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3625685
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Zodiac: Organizing Large Deployment of Sensors to Create Reusable Applications for Buildings

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Published In

cover image ACM Conferences

BuildSys '15: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments

November 2015

264 pages

ISBN:9781450339810

DOI:10.1145/2821650

General Chair:
David Culler
University of California, Berkeley, USA
,
Program Chairs:
Yuvraj Agarwal
Carnegie Mellon University, USA
,
Rahul Mangharam
University of Pennslyvania, USA

Copyright © 2015 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: 04 November 2015

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University of California, San Diego
Scott Energy Institute

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BuildSys '15

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BuildSys '15: The 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments

November 4 - 5, 2015

Seoul, South Korea

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BuildSys '15 Paper Acceptance Rate 20 of 66 submissions, 30%;

Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

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https://doi.org/10.1016/j.enbuild.2024.114637
Weber LLenz R(2023)Machine learning in sensor identification for industrial systemsit - Information Technology10.1515/itit-2023-005165:4-5(177-188)Online publication date: 9-Oct-2023
https://doi.org/10.1515/itit-2023-0051
Cui JYap WProsper CBalaji BChen J(2023)Economizer Optimization with Reinforcement Learning: An Industry PerspectiveProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3625685(366-369)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3625685
Wu MYao FWang H(2023)An End-to-End Solution for Spatial Inference in Smart BuildingsProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623736(110-119)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623736
Boovaraghavan SChen CMaravi ACzapik MZhang YHarrison CAgarwal Y(2023)MitesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808657:1(1-32)Online publication date: 28-Mar-2023
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Both MKämper BCartus ABeermann JFessler TMüller DDiedrich D(2023)Automated monitoring applications for existing buildings through natural language processing based semantic mapping of operational data and creation of digital twinsEnergy and Buildings10.1016/j.enbuild.2023.113635300(113635)Online publication date: Dec-2023
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Waterworth DSethuvenkatraman SSheng Q(2023)Deploying data driven applications in smart buildings: Overcoming the initial onboarding barrier using machine learningEnergy and Buildings10.1016/j.enbuild.2022.112699279(112699)Online publication date: Jan-2023
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Rana MRahman AAlmashor MMcCulloch JSethuvenkatraman S(2023)Automatic Classification of Sensors in Buildings: Learning from Time Series DataAI 2023: Advances in Artificial Intelligence10.1007/978-981-99-8388-9_30(367-378)Online publication date: 27-Nov-2023
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Ahmed MMyo TAseeri MAl Baroomi BKaiser MSrimal W(2023)Internet of Things-Based Smart Building for Energy EfficiencyFuture Energy10.1007/978-3-031-33906-6_8(87-97)Online publication date: 28-Sep-2023
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Mertens NWilde A(2023)Automated Classification of Datapoint Types in Building Automation Systems Using Time SeriesProduct Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies10.1007/978-3-031-25182-5_48(495-505)Online publication date: 1-Feb-2023
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