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Deconstruct: A scalable method of as-built heat power loss coefficient inference for UK dwellings using smart meter data

Chambers, JD; Oreszczyn, T; (2019) Deconstruct: A scalable method of as-built heat power loss coefficient inference for UK dwellings using smart meter data. Energy and Buildings , 183 pp. 443-453. 10.1016/j.enbuild.2018.11.016. Green open access

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Abstract

Dwellings in the UK account for about 25% of global energy demand, of which 60% is space heating making this a key area for efficiency improvement. Dwelling UK Energy Performance Certificates (EPC) are currently based on surveyed data, rather than energy use monitoring. The installation of smart meters provides an opportunity to develop an EPC based on in situ dwelling thermal performance. This paper presents ‘Deconstruct’ – a method of estimating the as-built Heat Power Loss Coefficient (HPLC) of occupied dwellings as a measure of thermal performance, using just smart-meter and meteorological data. Deconstruct is a steady-state grey box building model combined with a data processing pipeline and a model fitting method that limits the effects of confounding factors. Smart meter data from 780 UK dwellings from the UK Energy Demand Research Project (EDRP), was used to calculate a median HPLC of 0.28 kW/°C (±15%). The stability of the estimate across multiple years of data with different weather and energy use was demonstrated. Deconstruct was found to be suitable for large scale inference of dwelling thermal properties using the UK's new smart metering data infrastructure.

Type: Article
Title: Deconstruct: A scalable method of as-built heat power loss coefficient inference for UK dwellings using smart meter data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.enbuild.2018.11.016
Publisher version: https://doi.org/10.1016/j.enbuild.2018.11.016
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Energy demand, Residential sector, Smart meter, Building assessment methods, Building energy models
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10065481
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