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Matchstick: a room-to-room thermal model for predicting indoor temperature from wireless sensor data

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Matchstick : a room-to-room thermal model for predicting indoor temperature from wireless sensor data. / Ellis, Carl; Hazas, Michael; Scott, James.

Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN '13). New York : ACM, 2013. p. 31-42.

Research output: Contribution in Book/Report/ProceedingsPaper

Harvard

Ellis, C, Hazas, M & Scott, J 2013, Matchstick: a room-to-room thermal model for predicting indoor temperature from wireless sensor data. in Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN '13). ACM, New York, pp. 31-42. DOI: 10.1145/2461381.2461389

APA

Ellis, C., Hazas, M., & Scott, J. (2013). Matchstick: a room-to-room thermal model for predicting indoor temperature from wireless sensor data. In Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN '13) (pp. 31-42). New York: ACM. DOI: 10.1145/2461381.2461389

Vancouver

Ellis C, Hazas M, Scott J. Matchstick: a room-to-room thermal model for predicting indoor temperature from wireless sensor data. In Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN '13). New York: ACM. 2013. p. 31-42. Available from, DOI: 10.1145/2461381.2461389

Author

Ellis, Carl ; Hazas, Michael ; Scott, James. / Matchstick : a room-to-room thermal model for predicting indoor temperature from wireless sensor data. Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN '13). New York : ACM, 2013. pp. 31-42

Bibtex

@inbook{4897430143fa404a8effadedabf18f90,
title = "Matchstick: a room-to-room thermal model for predicting indoor temperature from wireless sensor data",
abstract = "In this paper we present a room-to-room thermal model used to accurately predict temperatures in residential buildings. We evaluate the accuracy of this model with ground truth data from four occupied family homes (two in the UK and two in the US). The homes have differing construction and a range of heating infrastructure (wall-mounted radiators, underfloor heating, and furnace-driven forced-air). Data was gathered using a network of simple and sparse (one per room) temperature sensors, a gas meter sensor, and an outdoor temperature sensor. We show that our model can predict future indoor temperature trends with a 90th percentile aggregate error between 0.61--1.50°, when given boiler or furnace actuation times and outdoor temperature forecasts. Two existing models were also implemented and then evaluated on our dataset alongside Matchstick. As a proof of concept, we used data from a previous control study to show that when Matchstick is used to predict temperatures (rather than assuming a preset linear heating rate) the possible gas savings increase by up to 3{\%}.",
author = "Carl Ellis and Michael Hazas and James Scott",
year = "2013",
month = "4",
day = "9",
doi = "10.1145/2461381.2461389",
language = "English",
isbn = "9781450319591",
pages = "31--42",
booktitle = "Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN '13)",
publisher = "ACM",

}

RIS

TY - CHAP

T1 - Matchstick

T2 - a room-to-room thermal model for predicting indoor temperature from wireless sensor data

AU - Ellis,Carl

AU - Hazas,Michael

AU - Scott,James

PY - 2013/4/9

Y1 - 2013/4/9

N2 - In this paper we present a room-to-room thermal model used to accurately predict temperatures in residential buildings. We evaluate the accuracy of this model with ground truth data from four occupied family homes (two in the UK and two in the US). The homes have differing construction and a range of heating infrastructure (wall-mounted radiators, underfloor heating, and furnace-driven forced-air). Data was gathered using a network of simple and sparse (one per room) temperature sensors, a gas meter sensor, and an outdoor temperature sensor. We show that our model can predict future indoor temperature trends with a 90th percentile aggregate error between 0.61--1.50°, when given boiler or furnace actuation times and outdoor temperature forecasts. Two existing models were also implemented and then evaluated on our dataset alongside Matchstick. As a proof of concept, we used data from a previous control study to show that when Matchstick is used to predict temperatures (rather than assuming a preset linear heating rate) the possible gas savings increase by up to 3%.

AB - In this paper we present a room-to-room thermal model used to accurately predict temperatures in residential buildings. We evaluate the accuracy of this model with ground truth data from four occupied family homes (two in the UK and two in the US). The homes have differing construction and a range of heating infrastructure (wall-mounted radiators, underfloor heating, and furnace-driven forced-air). Data was gathered using a network of simple and sparse (one per room) temperature sensors, a gas meter sensor, and an outdoor temperature sensor. We show that our model can predict future indoor temperature trends with a 90th percentile aggregate error between 0.61--1.50°, when given boiler or furnace actuation times and outdoor temperature forecasts. Two existing models were also implemented and then evaluated on our dataset alongside Matchstick. As a proof of concept, we used data from a previous control study to show that when Matchstick is used to predict temperatures (rather than assuming a preset linear heating rate) the possible gas savings increase by up to 3%.

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U2 - 10.1145/2461381.2461389

DO - 10.1145/2461381.2461389

M3 - Paper

SN - 9781450319591

SP - 31

EP - 42

BT - Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN '13)

PB - ACM

CY - New York

ER -