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


Climate change projections predict substantial impacts on cities and human development in future. The goal of the Paris Agreement (an international treaty approved and adopted by 197 countries in 2015) 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). The 26th United Nations (UN) Climate Change Conference of the Parties (COP26), which concluded in November 2021, recognised that loss of life, livelihoods and ecosystems is already occurring, and the actions towards the goals of Paris Agreement and the UN Framework Convention on Climate Change should be accelerated (COP26, 2021).

In the 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 domestic buildings are required by law when a building is purchased/sold or rented, and remains valid for 10 years. The EPC ratings are subject to some inconsistencies and biases due to the qualitative nature of the information collected by the assessors. Further, these data are not available for all the domestic buildings and there is a big data gap in this regard. Given that the EPC is the only information available publicly to analyse and compare energy efficiency of buildings in the UK, the Office for National Statistics (ONS) data science campus used proxy variables from a number of data sources in an attempt to predict EPC ratings for the buildings using several machine learning models (Williams, 2020). The results, however, were not satisfactory due to a number of underlying problems, one of which was that the project attempted to predict energy efficiency at the most granular level (individual property). The study proposes that a technique considering an aggregated spatial level for this assessment could be enough.

Considering the mammoth decarbonisation required in the domestic sector to attempt meeting the Paris Agreement targets, there is a need to explore methods that could help assess energy efficiency of buildings. Remote sensing data and spatial methods can help assess Urban Heat Islands (UHIs) and classify geographical areas according to Local Climate Zones (LCZs) (see Stewart & Oke, 2012 and Mills et al., 2015), however, a thorough evaluation is required to see if these could be used to predict energy efficiency of individual buildings.

Objectives of the Research:

This project aims to assess whether geospatial techniques could help evaluate the energy efficiency of domestic buildings – 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 of any of these) and ancillary data in order to:

  1. assess the energy efficiency of domestic buildings at an aggregated spatial scale, potentially using UHIs, 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 in different regions over time.

Methods to be used:

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. This would mainly be a desk research involving analyses of spatial and ancillary data through various software to develop a methodology for achieving the study objectives and aim.

Skills required of applicant:

  • A theoretical and practical background in Geosciences or related field.
  • An understanding of optical and thermal remote sensing concepts (in relation to terrestrial remote sensing).
  • Ability to acquire, process and analyse remote sensing and other spatial data.
  • An interest in remote sensing, GIS, climate change, or some combination.
  • Ability to work independently.

Additional desirable criteria:

  • Demonstrated experience in processing and analysing spatial data (including remote sensing datasets) using software packages such as Erdas Imagine, ArcGIS, QGIS, or others.
  • Experience in carrying out independent research demonstrated by, for example, research publication(s) or research project(s) on a subject similar to this research topic.
  • Experience in using statistical software packages such as R, SPSS, or others. 

This application does not require a proposal.

    Essential criteria

    Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

    We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

    In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

    • Experience using research methods or other approaches relevant to the subject domain
    • A comprehensive and articulate personal statement
    • A demonstrable interest in the research area associated with the studentship

    Desirable Criteria

    If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

    • First Class Honours (1st) Degree
    • Practice-based research experience and/or dissemination
    • Experience using research methods or other approaches relevant to the subject domain
    • Work experience relevant to the proposed project
    • Experience of presentation of research findings
    • Use of personal initiative as evidenced by record of work above that normally expected at career stage.

    Funding and eligibility

    Recommended reading

    COP26 (2021) United Nations Climate Change Conference of the Parties (COP26). Available at:

    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:

    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:

    The Doctoral College at Ulster University

    Key dates

    Submission deadline
    Friday 16 June 2023

    Interview Date
    Week Commencing 26th June 2023

    Preferred student start date
    18th September 2023


    Apply Online  

    Contact supervisor

    Dr Saad Bhatti

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