Thermal characterisation of UK dwellings at scale using smart meter data Jonathan Chambers PhD Candidate UCL Energy Institute, Centre of Energy Epidemiology

Energy in homes: climate and health UK homes account for about 25% of energy use and CO2 emissions Key sector to address to meet climate targets: energy efficiency is the ‘first fuel’ of the transition to a low carbon economy Not only about climate – 5million people living in fuel poverty in UK – 9000 excess winter deaths attributed to cold homes in 2014

Measuring thermal performance Track: – Building stock state and evolution – Impact of renovations Regulatory compliance – Certification – Performance based contracting Most common approach: EPC/SAP – Significant divergence between predicted and measured demand: ‘Credibility Gap’ – Expensive to scale, slow, intrusive

How can thermal characterisation of UK dwellings be performed rapidly and non intrusively at scale? Q1.1 What parameters are needed for the thermal characterization? Q1.2 What data sources can be used to perform this assessment? Q1.3 How can the parameters be inferred from data? Q2.1 How precise, accurate and reliable is the method? Q2.2 What is the relation between the characterization parameters determined by the new method and those determined by intrusive monitoring methods? Q2.3 To what extent do as-built thermal properties correspond to EPC labels/SAP assessments? What are the implications?

Data-driven grey-box characterisation Availability of new consumption data enables scalable thermal characterisation of individual homes Smart Meters measure total gas and electricity consumption hourly Physically based grey box model – Response of energy demand to weather – Define energy balance equations based on known building physics and fit to data

Simplified Physical Demand Model (SPDM) Describe building thermal processes using established approximations from literature Conduction Radiative gains Wind-driven losses Baseload gains Link functions – Relate physical processes to energy demand – Make assumptions explicit – Additional data to refine results

DECONSTRUCT algorithm DECONSTRUCT is an approach for estimating SPDM parameters Principal: select subsets from metered data which best represents each physical processes in SPDM As opposed to estimating them all at once – Conceptually less robust – Potentially biases certain parameters – Sensitive to outliers – Many degrees of freedom means you might find local instead of globally optimal solution

Data pipeline Smart meter data from a variety of existing sets Gridded weather data from MetOffice and CFSR Geospatial data for weather and metadata lookup for locations Extensive cleaning, error checking, format conversion

Example site – daily winter demand and low sun sample Results

Bulk estimates Sensible results for heat loss rates General shortage of base data to perform comparison against – Initial comparison using internal temperatures Improvements to algorithm will reduce this Next steps – Extend weather parameters – identify predictors of error Histogram of difference between estimated and measured average internal temperatures for 25 sites

Publications [1]J. Chambers, T. Oreszczyn, and D. Shipworth, “Quantifying Uncertainty In Grey-box Building Models Arising From Smart,” Build. Simul. Conf., vol. 0, no. 1, pp. 2947–2954, [2]J. Chambers, V. Gori, P. Biddulph, I. Hamilton, T. Oreszczyn, and C. Elwell, “How solid is our knowledge of solid walls? - Comparing energy savings through three different methods,” in CISBAT International Conference Future Buildings & Districts Sustainability from Nano to Urban Scale, [3]J. Chambers and A. Stone, “EPC toolkit.” RCUK-CEE, (software) [4]J. D. Chambers and D. Shipworth, “Energy ergonomics as a framework for the analysis and understanding of smart device data : key definitions and experimental approach in dwelling heating demand,” in BEHAVE 2016, (presentation, forthcoming)

Conclusion and Future work Aim to estimate the insulation levels of any home equipped with a smart meter Use grey box modelling to extract physical properties from noisy data Built data pipeline to ingest diverse energy data sources and match with local weather Promising initial results Next steps – measuring errors, cross validation, determining predictors of result quality

Acknowledgements Supervisors – Prof Tadj Oreszczyn – Dr David Shipworth EDF Energy R&D Funding from EDF R&D industrial case studentship and the EPSRC