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EarlyOff: using house cooling rates to save energy

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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EarlyOff: using house cooling rates to save energy. / Ellis, Carl; Scott, James; Hazas, Michael et al.
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings. New York: ACM, 2012. p. 39-41.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Ellis, C, Scott, J, Hazas, M & Krumm, J 2012, EarlyOff: using house cooling rates to save energy. in BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings. ACM, New York, pp. 39-41. https://doi.org/10.1145/2422531.2422539

APA

Ellis, C., Scott, J., Hazas, M., & Krumm, J. (2012). EarlyOff: using house cooling rates to save energy. In BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (pp. 39-41). ACM. https://doi.org/10.1145/2422531.2422539

Vancouver

Ellis C, Scott J, Hazas M, Krumm J. EarlyOff: using house cooling rates to save energy. In BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings. New York: ACM. 2012. p. 39-41 doi: 10.1145/2422531.2422539

Author

Ellis, Carl ; Scott, James ; Hazas, Michael et al. / EarlyOff : using house cooling rates to save energy. BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings. New York : ACM, 2012. pp. 39-41

Bibtex

@inproceedings{22bb1a00c1c54fd0be10030aa2e9aff4,
title = "EarlyOff: using house cooling rates to save energy",
abstract = "Home heating systems often have a significant thermal inertia, as homes stay warm after the heating is turned off for significant periods of time. We present the EarlyOff concept, whereby home heating can be predictively turned off in advance of occupants{\textquoteright} departure, using this inertia to keep the house warm while saving energy. We use a previously gathered data set of real-time heating, gas, and occupancy readings from five houses and conduct a data-driven analysis of potential energy savings. Using an “oracle” predicting actual departure events, we show an upper bound savings of 4–12% of the gas used over the whole study period by applying EarlyOff. Using a real predictor which makes use of historical occupancy probabilities, we show savings of 1–8% of gas use.",
author = "Carl Ellis and James Scott and Michael Hazas and John Krumm",
year = "2012",
month = nov,
doi = "10.1145/2422531.2422539",
language = "English",
isbn = "9781450311700",
pages = "39--41",
booktitle = "BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings",
publisher = "ACM",

}

RIS

TY - GEN

T1 - EarlyOff

T2 - using house cooling rates to save energy

AU - Ellis, Carl

AU - Scott, James

AU - Hazas, Michael

AU - Krumm, John

PY - 2012/11

Y1 - 2012/11

N2 - Home heating systems often have a significant thermal inertia, as homes stay warm after the heating is turned off for significant periods of time. We present the EarlyOff concept, whereby home heating can be predictively turned off in advance of occupants’ departure, using this inertia to keep the house warm while saving energy. We use a previously gathered data set of real-time heating, gas, and occupancy readings from five houses and conduct a data-driven analysis of potential energy savings. Using an “oracle” predicting actual departure events, we show an upper bound savings of 4–12% of the gas used over the whole study period by applying EarlyOff. Using a real predictor which makes use of historical occupancy probabilities, we show savings of 1–8% of gas use.

AB - Home heating systems often have a significant thermal inertia, as homes stay warm after the heating is turned off for significant periods of time. We present the EarlyOff concept, whereby home heating can be predictively turned off in advance of occupants’ departure, using this inertia to keep the house warm while saving energy. We use a previously gathered data set of real-time heating, gas, and occupancy readings from five houses and conduct a data-driven analysis of potential energy savings. Using an “oracle” predicting actual departure events, we show an upper bound savings of 4–12% of the gas used over the whole study period by applying EarlyOff. Using a real predictor which makes use of historical occupancy probabilities, we show savings of 1–8% of gas use.

U2 - 10.1145/2422531.2422539

DO - 10.1145/2422531.2422539

M3 - Conference contribution/Paper

SN - 9781450311700

SP - 39

EP - 41

BT - BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings

PB - ACM

CY - New York

ER -