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

•A method is presented for non-intrusively inferring a building Heat Loss Coefficient using smart meter data.•For the sample data from the UK Energy Demand Research Project, a median Heat Loss Coefficient of 0.14 kW/°C and uncertainty of 15% was estimated.•The method was demonstrated to be reliably scalable to large numbers of buildings, enabling rapid thermal performance evaluation at a regional or national scale.•The method was demonstrated to produce stable results across several years, indicating its robustness to climate variation and occupant effects. 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.