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A simple, scalable and low-cost method to generate thermal diagnostics of a domestic building

A simple, scalable and low-cost method to generate thermal diagnostics of a domestic building
A simple, scalable and low-cost method to generate thermal diagnostics of a domestic building
Traditional approaches to understand the problem of the energy performance in the domestic sector include on-site surveys by energy assessors and the installation of complex home energy monitoring systems. The time and money that needs to be invested by the occupants and the form of feedback generated by these approaches often makes them unattractive to householders. This paper demonstrates a simple, low cost method that generates thermal diagnostics for dwellings, measuring only one field dataset; internal temperature over a period of one week. A thermal model, which is essentially a learning algorithm, generates a set of thermal diagnostics about the primary heating system, the occupants’ preferences and the impact of certain interventions, such as lowering the thermostat set-point. A simple clustering approach is also proposed to categorise homes according to their building fabric thermal performance and occupants’ energy efficiency with respect to ventilation. The advantage of this clustering approach is that the occupants receive tailored advice on certain actions that if taken will improve the overall thermal performance of a dwelling. Due to the method’s low cost and simplicity it could facilitate government initiatives, such as the ‘Green Deal’ in the UK.
domestic heating, thermal modelling, heating and ventilation, occupant preferences, energy feedback, building energy performance
0306-2619
519-530
Papafragkou, A.
2744628b-f747-4a8b-ad63-d3720f1c8d1b
Ghosh, S.
9fd40cd1-34b3-4ffe-adf4-44e39c2dd576
James, P.A.B.
da0be14a-aa63-46a7-8646-a37f9a02a71b
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Bahaj, A.S.
a64074cc-2b6e-43df-adac-a8437e7f1b37
Papafragkou, A.
2744628b-f747-4a8b-ad63-d3720f1c8d1b
Ghosh, S.
9fd40cd1-34b3-4ffe-adf4-44e39c2dd576
James, P.A.B.
da0be14a-aa63-46a7-8646-a37f9a02a71b
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Bahaj, A.S.
a64074cc-2b6e-43df-adac-a8437e7f1b37

Papafragkou, A., Ghosh, S., James, P.A.B., Rogers, A. and Bahaj, A.S. (2014) A simple, scalable and low-cost method to generate thermal diagnostics of a domestic building. Applied Energy - Elsevier, 134, 519-530. (doi:10.1016/j.apenergy.2014.08.045).

Record type: Article

Abstract

Traditional approaches to understand the problem of the energy performance in the domestic sector include on-site surveys by energy assessors and the installation of complex home energy monitoring systems. The time and money that needs to be invested by the occupants and the form of feedback generated by these approaches often makes them unattractive to householders. This paper demonstrates a simple, low cost method that generates thermal diagnostics for dwellings, measuring only one field dataset; internal temperature over a period of one week. A thermal model, which is essentially a learning algorithm, generates a set of thermal diagnostics about the primary heating system, the occupants’ preferences and the impact of certain interventions, such as lowering the thermostat set-point. A simple clustering approach is also proposed to categorise homes according to their building fabric thermal performance and occupants’ energy efficiency with respect to ventilation. The advantage of this clustering approach is that the occupants receive tailored advice on certain actions that if taken will improve the overall thermal performance of a dwelling. Due to the method’s low cost and simplicity it could facilitate government initiatives, such as the ‘Green Deal’ in the UK.

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More information

e-pub ahead of print date: 6 September 2014
Published date: 1 December 2014
Keywords: domestic heating, thermal modelling, heating and ventilation, occupant preferences, energy feedback, building energy performance
Organisations: Electronics & Computer Science, Energy & Climate Change Group

Identifiers

Local EPrints ID: 368896
URI: https://eprints.soton.ac.uk/id/eprint/368896
ISSN: 0306-2619
PURE UUID: cf6d1843-5a1e-4c11-b26a-c3b0f6d611e3
ORCID for A.S. Bahaj: ORCID iD orcid.org/0000-0002-0043-6045

Catalogue record

Date deposited: 17 Sep 2014 11:06
Last modified: 17 Jul 2019 01:24

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