To better inform the energy use and CO 2 reduction potential of the UK housing stock, computational models can simulate housing energy use and CO 2 emissions to test the effectiveness of decarbonisation scenarios. However, the likelihood that these scenarios will be taken up at scale is highly uncertain, as is the description of the housing stock and the many parameters that influence its energy use.
Currently, computational models lack an ability to evaluate this uncertainty and its corresponding influence on CO 2 emissions in a computationally efficient manner; specifically, what the sources of model and data uncertainty are, how these can be characterised, how they propagate through simulations, how they can be decomposed, and what the factors are that most influence the future energy and carbon performance of the UK housing stock.
This PhD project will develop a novel prototype of an uncertainty and sensitivity analysis workflow to facilitate probabilistic estimates of housing stock energy and carbon performance, to provide reliable and robust evidence for decarbonisation pathways in the residential sector. This will involve:
● Developing a novel Gaussian Process Regression based transfer learning technique for computationally efficient scaling up of uncertainty and sensitivity analysis from individual buildings to the housing stock scale, by emulating similarities and differences between representative housing archetypes. This will expand the scope of our foundational Emulation based Uncertainty and Sensitivity Analysis ( EmUSA ) framework.
● Integrating this global uncertainty and sensitivity analysis toolkit with our open-source dynamic housing stock energy simulation platform, EnHub. This will facilitate identification of the most influential parameters that influence the current and future housing stock performance.
● Supporting UK housing stock decarbonisation pathways with probabilistic estimates of stock performance and how this can best be optimised in terms of the least cost CO 2 emission mitigation pathways, with a particular emphasis on fuel poor households.
● Encouraging the student to publish their research in high impact journals (e.g. Applied Energy, Energy and Buildings, Building and Environment). Desirable Candidate’s Background
● Interests in developing data-driven techniques for advanced building performance assessment
● Interests in decarbonisation topics related to Built Environment
● Basic understanding of Building Physics, energy performance modelling and simulation
● Demonstrable computer programming skills
Funding: 3.5 years (Full time) + Standard RTSG
If you are interested, please contact Prof Alastair Buckley (alastair.buckley@sheffield.ac.uk ) or Dr Parag Wate (p.wate@sheffield.ac.uk)
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