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The Building Stock Lab (BSL) at UCL-Energy has been developing a new kind of 3D model of the UK building stock. The model’s purpose is to assess energy use in buildings, and study the potential for energy and carbon mitigation measures. The techniques have been trialled successfully in London and several other cities. This ‘3DStock’ model is built by bringing together a number of publicly available datasets to produce a full spatial model which contains 3D representations of all domestic and non-domestic buildings with associated floor space, use type and other attributes. 3DStock has been used for statistical analysis of building energy use, assessment of renewable energy potentials and analysis of district energy systems. A version of 3DStock is being developed to create the London Building Stock Model (LBSM) for the Greater London Authority to be used in climate change mitigation planning in Greater London.
Data from 3DStock is passed to the SimStock modelling platform which automatically generates dynamic simulation models to predict the energy and environmental performance of the building stock and comparisons are made to actual energy meter data.
Further development of key aspects of these models is underway in association with Bentley Systems, the leading global provider of software solutions for the design, construction, and operations of buildings and infrastructure. One studentship, focusing on ‘Simulating and Optimising the Performance of the Building Stock’, has already been allocated. A second studentship is available to work alongside the first and in collaboration with the Building Stock Lab on the topic of:
Digital Characterisation of the Building Stock
Digital models of the building stock require increasing levels of detail to generate more accurate representations and produce performance predictions with higher levels of validity and utility. This PhD will focus on the application of a variety of techniques to process photography, LiDAR and other data sources to derive detailed building and built environment characteristics, materials and components to improve the veracity of current models.
The research will require candidates with advanced computer skills. Experience in one or more of the following would be an advantage: image processing, machine learning, dynamic energy simulation, visualisation and statistics.
Candidates should have a Master’s degree and / or a first or upper-second class Bachelor’s degree.
Applicants must also meet the minimum language requirements of UCL: https://www.ucl.ac.uk/prospective-students/graduate/learning-and-living-ucl/international-students/english-language-requirements
How to apply
Please submit a pre-application by email to the UCL ERBE Centre Manager ([email protected]
) with Subject Reference: 4year PhD in Digital Characterisation of the Building Stock
The application should include the following:
• A covering letter clearly stating which project you wish to apply for, your motivation, and your understanding of eligibility according to these guidelines: https://www.epsrc.ac.uk/skills/students/help/eligibility/
• Names and addresses of two academic referees
• A copy of your degree certificate(s) and transcript(s) of degree(s),
Pre-application deadline: 31 March 2020 23:59 (UK time)
Interview date: TBC
You will be undertaking this project in UCL at the main (Bloomsbury) campus as part of the new EPSRC-SFI Centre for Doctoral Training in Energy Resilience and the Built Environment (ERBE CDT). This is a collaboration between UCL, Loughborough University and Marine and Renewable Energy Ireland (MaREI). For more information please see http://erbecdt.ac.uk