research-article

Beyond Control: Enabling Smart Thermostats for Leakage Detection

Authors:

Amarjeet Singh,

Vikas ChandanAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 1

Article No.: 14, Pages 1 - 21

https://doi.org/10.1145/3314401

Published: 29 March 2019 Publication History

Abstract

Smart thermostats, with multiple sensory abilities, are becoming pervasive and ubiquitous, in both residential and commercial buildings. By analyzing occupants' behavior, adjusting set temperature automatically, and adapting to temporal and spatial changes in the atmosphere, smart thermostats can maximize both - energy savings and user comfort. In this paper, we study smart thermostats for refrigerant leakage detection. Retail outlets, such as milk-booths and quick service restaurants set up cold-rooms to store perishable items. In each room, a refrigeration unit (akin to air-conditioners) is used to maintain a suitable temperature for the stored products. Often, refrigerant leaks through the coils (or valves) of the refrigeration unit which slowly diminishes the cooling capacity of the refrigeration unit while allowing it to be functional. Such leaks waste significant energy, risk occupants' health, and impact the quality of stored perishable products. While store managers usually fail to sense the early symptoms of such leaks, current techniques to report refrigerant leakage are often not scalable. We propose Greina - to continuously monitor the readily available ambient information from the thermostat and timely report such leaks. We evaluate our approach on 74 outlets of a retail enterprise and results indicate that Greina can report the leakage a week in advance when compared to manual reporting.

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Cited By

Andrew Ejenakevwe KSong L(2024)Investigation of smart thermostat fault detection and diagnosis potential for air-conditioning systems using a Modelica/EnergyPlus co-simulation approachEnergy and Buildings10.1016/j.enbuild.2024.114053309(114053)Online publication date: Apr-2024
https://doi.org/10.1016/j.enbuild.2024.114053
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
https://dl.acm.org/doi/10.1145/3580865
Ejenakevwe KWang JJiang YSong LKini R(2023)Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurementsScience and Technology for the Built Environment10.1080/23744731.2023.223423129:9(887-904)Online publication date: 2-Aug-2023
https://doi.org/10.1080/23744731.2023.2234231
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Beyond Control: Enabling Smart Thermostats for Leakage Detection

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 1

March 2019

786 pages

EISSN:2474-9567

DOI:10.1145/3323054

Issue’s Table of Contents

Copyright © 2019 ACM.

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 29 March 2019

Accepted: 01 January 2019

Revised: 01 November 2018

Received: 01 May 2018

Published in IMWUT Volume 3, Issue 1

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Cited By

Andrew Ejenakevwe KSong L(2024)Investigation of smart thermostat fault detection and diagnosis potential for air-conditioning systems using a Modelica/EnergyPlus co-simulation approachEnergy and Buildings10.1016/j.enbuild.2024.114053309(114053)Online publication date: Apr-2024
https://doi.org/10.1016/j.enbuild.2024.114053
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
https://dl.acm.org/doi/10.1145/3580865
Ejenakevwe KWang JJiang YSong LKini R(2023)Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurementsScience and Technology for the Built Environment10.1080/23744731.2023.223423129:9(887-904)Online publication date: 2-Aug-2023
https://doi.org/10.1080/23744731.2023.2234231
Guo FRasmussen B(2023)Predictive maintenance for residential air conditioning systems with smart thermostat data using modified Mann-Kendall testsApplied Thermal Engineering10.1016/j.applthermaleng.2022.119955222(119955)Online publication date: Mar-2023
https://doi.org/10.1016/j.applthermaleng.2022.119955
Jain MGhosh SNandanoori SSterpone LBartolini AButko A(2022)Workload characterization of a time-series prediction system for spatio-temporal dataProceedings of the 19th ACM International Conference on Computing Frontiers10.1145/3528416.3530242(159-168)Online publication date: 17-May-2022
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Jain MGupta KSathanur AChandan VHalappanavar M(2021)Transfer-Learnt Models for Predicting Electricity Consumption in Buildings with Limited and Sparse Field Data2021 American Control Conference (ACC)10.23919/ACC50511.2021.9483228(2887-2894)Online publication date: 25-May-2021
https://doi.org/10.23919/ACC50511.2021.9483228
Soller SHolzl GKranz M(2020)Predicting Machine Errors based on Adaptive Sensor Data Drifts in a Real World Industrial Setup2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom45495.2020.9127357(1-9)Online publication date: Mar-2020
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