Zoe’s research focuses on energy demand flexibility in the built environment and how flexible demand can support the transition towards smarter, low-carbon energy systems. Her work combines detailed building energy modelling with urban-scale analysis to understand the magnitude, timing and spatial distribution of energy demand and its potential flexibility.

Her earlier PhD research investigated the factors influencing building energy demand flexibility through large-scale parametric EnergyPlus simulations and sensitivity analysis. Building on the key factors identified, she developed building archetypes and modelling approaches for predicting energy flexibility across large groups of buildings, providing a systematic method for translating detailed building-level analysis to wider building stocks.

More recently, Zoe has been exploring the integration of building energy demand with electric vehicle (EV) charging demand to understand their combined impacts on electricity networks, including peak magnitude, peak duration and spatial demand patterns, with the aim of informing future grid planning and reinforcement. Building on this work, her latest research focuses on the application of large language models (LLMs) to support city-scale energy modelling, particularly for integrating heterogeneous building, transport and energy datasets.

Her wider research interests include building energy flexibility, urban building energy modelling, electrification, electricity-network impacts and the use of artificial intelligence to support automated urban energy modelling.