ERBE CDT PhD Studentship: Simulating and Optimising the Performance of the Building Stock

Lead Research Organisation: University College London
Department Name: Bartlett Sch of Env, Energy & Resources


The Building Stock Lab at the Energy Institute in UCL has been developing a new kind of 3D model of the UK building stock since 2012. 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.
This project proposes a further development of 3DStock and SimStock in association with Bentley Systems, a leading global provider of software solutions for the design, construction, and operations of buildings and infrastructure. The aim is to develop automatic processes that allow the future evolution of the UK building stock to be forecast, emulated and the resulting energy and environmental performance to be predicted. These processes will allow future scenarios in which new buildings are constructed to every increasing standards of performance and existing buildings are retrofitted to reduce carbon emissions and improve indoor environmental quality. Modules will be developed to evaluate the costs and benefits of these scenarios, considering direct costs and other social costs and benefits. Emerging techniques such as genetic algorithms will be employed to identify optimal approaches to reducing carbon emissions whilst maintaining, in many cases improving, indoor environmental standards at least overall cost. The resulting models will be used to explore policies and regulations options aimed at achieving net zero carbon emissions by 2050.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S021671/1 01/10/2019 31/03/2028
2241033 Studentship EP/S021671/1 23/09/2019 15/09/2023 Shyam Amrith