|4th December 2023
|4th January 2024
A decarbonised built environment is key to climate change mitigation and adaptation, yet buildings still consume 29% of the global primary energy, mainly due to space conditioning demand. This is particularly worrying given the imminent expansion of the building stock associated with the population growth to 9.7 billion by 2050, the service life of buildings and their costly retrofit. As the climate changes, there is a need to map closely and rigorously the boundary conditions of buildings through the so-called weather files, to facilitate learning from regular and atypical weather events and promoting robust planning. However, and due to historical limitations, current practice relies on: (1) the existence of weather stations with suitable records nearby the location of interest; (2) carefully crafted single-year weather files that prompts the typical response of buildings according to a single key performance indicator like space heating demand or indoor overheating.
Thanks to satellite imaging and novel climate reanalyses datasets, a new generation of weather files is now possible to deliver unprecedented spatiotemporal resolution (the whole world across decades) to create a rigorous, global testbed for the built environment. Here, historical data is essential to assimilate past performance as it provides every relatable event while helping establish the methods to prepare and consume datasets for future weather (near future) and climate change, improving predictive control and resilience. This project will leverage new approaches to weather file creation that allow assimilating such data for the built environment with emphasis on robust decision-making, usability, and interpretability.
The standard stipend in Engineering is £17,664 per year for the 2021/22 academic year, with annual increments. Any stipend will be at this rate, unless stated below.
To qualify as a Home student, you must fulfil one of the following criteria:
Candidates should have at least a 2:1 undergraduate degree in Engineering, Environmental Sciences, Physics or in another relevant programme. Experience or willingness to learn programming (Python, R, Julia, or similar) to manipulate large-scale datasets, manage simulations and analyse results will be essential. A background in building physics or meteorology would be an advantage but it is not essential.
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