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Assessing energy efficiency of buildings – role of remote sensing data and geospatial methods


   Faculty of Health and Life Sciences

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  Dr Saad Bhatti, Dr Paul McKenzie  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Climate change projections predict substantial impacts on cities and human development in future. The goal of Paris Agreement is to limit global warming to below 2°C rise, compared to the pre-industrial levels (1850-1900) and making an all-out effort to limit this increase to 1.5°C (UNFCCC, 2015). In United Kingdom (UK), most of the energy consumed in the buildings is used for heating, which is directly linked with their energy efficiency. Energy Performance Certificates (EPC) for buildings are required by law, however, the EPC ratings are subject to some inconsistencies and biases due to the qualitative nature of the information collected by the assessors. Further, this data is not available for all the domestic buildings and there is a big data gap.

Considering the mammoth decarbonisation required in the domestic sector to attempt meeting the Paris Agreement targets, there is a need to determine the current energy efficiency of buildings. This project aims to assess whether geospatial techniques could help in this regard – the initial case study area would be a few regions in the UK. The objectives are to employ remote sensing (satellite, airborne, ground-based, or a combination) and ancillary data in order to:

(1) assess the energy efficiency of domestic buildings at an aggregated spatial scale, potentially using Urban Heat Islands (UHIs), Local Climate Zones (LCZs) and other methods;

(2) evaluate whether geospatial methods could be employed with confidence to estimate building-level energy efficiency; and

(3) automate the developed processes that would help evaluate the impacts of decarbonisation over time.

The successful candidate is expected to acquire, process, and analyse geospatial data, particularly collected through passive and active remote sensing systems. The candidate will also review and analyse the UN’s Sustainable Development Goals and other resolutions relevant to the project, along with the UK EPC data, policies and regulations.


References

COP26 (2021) United Nations Climate Change Conference of the Parties (COP26). Available at: https://ukcop26.org/
Evans, S., Liddiard, R., & Steadman, P. (2017). 3DStock: A new kind of three-dimensional model of the building stock of England and Wales, for use in energy analysis. Environment and Planning B: Urban Analytics and City Science, 44(2), 227-255.
Few, J., McKenna, E., Pullinger, M., Elam, S., Webborn, E., & Oreszczyn, T. (2022). Smart Energy Research Lab: Energy use in GB domestic buildings 2021.
Gupta, R., & Gregg, M. (2018). Targeting and modelling urban energy retrofits using a city-scale energy mapping approach. Journal of cleaner production, 174, 401-412.
McKenna, E., Few, J., Webborn, E., Anderson, B., Elam, S., Shipworth, D., ... & Oreszczyn, T. (2022). Explaining daily energy demand in British housing using linked smart meter and socio-technical data in a bottom-up statistical model. Energy and Buildings, 258, 111845.
Mills, G., Ching, J., See, L., Bechtel, B., and Foley, M. (2015) An introduction to the WUDAPT project. In Proceedings of the 9th International Conference on Urban Climate, Toulouse, France (pp. 20-24).
Stewart, I. D. and Oke, T. R. (2012) Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), pp. 1879-1900.
United Nations Framework Convention on Climate Change (2015) The Paris Agreement. Available at: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement
Webborn, E., Few, J., McKenna, E., Elam, S., Pullinger, M., Anderson, B., ... & Oreszczyn, T. (2021). The SERL Observatory Dataset: Longitudinal smart meter electricity and gas data, survey, EPC and climate data for over 13,000 households in Great Britain. Energies, 14(21), 6934.
Williams, S. (2020) Can machine learning be used to predict energy performance scores?. Available at: https://datasciencecampus.ons.gov.uk/can-machine-learning-be-used-to-predict-energy-performance-scores/

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