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A Scalable Low-Cost Solution to Provide Personalised Home Heating Advice to Households [Best Paper Award BuildSys 2013]

A Scalable Low-Cost Solution to Provide Personalised Home Heating Advice to Households [Best Paper Award BuildSys 2013]
A Scalable Low-Cost Solution to Provide Personalised Home Heating Advice to Households [Best Paper Award BuildSys 2013]
In this paper, we present a deployed prototype of a scalable low-cost solution providing personalised home heating advice to households. Our solution, named MyJoulo (www.myjoulo.com), uses intelligent algorithms to analyse data collected from a specially designed USB temperature logger, placed on top of the thermostat, in order to build a thermal model of the home and to infer the operational settings of the heating system. This model is then used to calculate the impact, in terms of percentage reduction in heating costs, of various interventions (such as reducing the thermostat set-point temperature or adjusting timer settings); providing specific actionable advice to the household. The system was launched in beta form in December 2012 and registered over 750 users in its three months of operation.
Rogers, A.
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Ghosh, S.
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Wilcock, R.
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Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Ghosh, S.
9fd40cd1-34b3-4ffe-adf4-44e39c2dd576
Wilcock, R.
039894e9-f32d-49e0-9ebd-fb13bc489feb
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Rogers, A., Ghosh, S., Wilcock, R. and Jennings, N. R. (2013) A Scalable Low-Cost Solution to Provide Personalised Home Heating Advice to Households [Best Paper Award BuildSys 2013]. 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys). 8 pp . (doi:10.1145/2528282.2528284).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we present a deployed prototype of a scalable low-cost solution providing personalised home heating advice to households. Our solution, named MyJoulo (www.myjoulo.com), uses intelligent algorithms to analyse data collected from a specially designed USB temperature logger, placed on top of the thermostat, in order to build a thermal model of the home and to infer the operational settings of the heating system. This model is then used to calculate the impact, in terms of percentage reduction in heating costs, of various interventions (such as reducing the thermostat set-point temperature or adjusting timer settings); providing specific actionable advice to the household. The system was launched in beta form in December 2012 and registered over 750 users in its three months of operation.

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Published date: 14 November 2013
Venue - Dates: 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys), 2013-11-14
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 358965
URI: http://eprints.soton.ac.uk/id/eprint/358965
PURE UUID: 817f5e6e-f59b-4f20-8524-5d7a513c99c9

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Date deposited: 16 Oct 2013 14:39
Last modified: 14 Mar 2024 15:12

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Contributors

Author: A. Rogers
Author: S. Ghosh
Author: R. Wilcock
Author: N. R. Jennings

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