Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.746991
Title: Developing a rapid, scalable method of thermal characterisation for UK dwellings using smart meter data
Author: Chambers, Jonathan David
ISNI:       0000 0004 7227 7316
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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Abstract:
Measuring building thermal performance is important for meeting goals in energy efficiency, emissions reductions, and housing quality. Energy Performance Certificate (EPC) assessments performed in the UK are limited in their capacity to assess the in-situ thermal performance of dwellings, while approaches are expensive and impractically invasive for use on a mass-scale. The UK roll out of smart meters will result in widespread collection of energy data. However, to-date no established method exists for inferring thermal performance from such data. This research aims to develop a method of measuring the thermal performance of existing dwellings using smart meter data and weather data derived from location which can be applied to large numbers of dwellings. This research presents the “Deconstruct” method to estimate dwelling thermal characteristics, notably the Heat Loss Coefficient (HLC), from smart meter and weather data. This method uses a grey-box building physics model to describe the relation between dwelling thermal properties and measured energy demand. A 'post-hoc control trail' approach identifies data subsets which enable robust model parameter estimates. This enables dwelling characterisation without the need for a controlled experiment with on-site monitoring. Evaluation of the Deconstruct confirmed that steady-state modelling assumptions made were reasonable for the majority of dwellings from a range of datasets. HLC values were inferred with a mean uncertainty of 15% for 63% of 7529 sites from the Energy Demand Research Project (EDRP) dataset. Future monitoring studies are proposed which address shortcomings in existing datasets. Limitations of the method are discussed. Deconstruct could enable rapid estimation of key thermal performance parameters for millions of UK dwellings, with a range of applications including policy evaluation, retrofit assessments, and advice to dwelling occupants.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.746991  DOI: Not available
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