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• Dr Ruchi Choudhary
  • Reader, Department of Engineering, University of Cambridge, UK.
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• Dr Brian Matthews
  • Group Leader, DAFNI and Data Science and Technology Group, Scientific Computing Department of the Science and Technology Facilities Council.
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• Dr Nicolas Malleson
  • Professor of Spatial Science, University of Leeds, UK.
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• Dr André Paul Neto-Bradley
  • PDRA, Department of Engineering, University of Cambridge, Cambridge, UK
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• Dr Patricia Ternes
  • Research Fellow, School of Geography, University of Leeds, UK
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Energy flexibility is key to delivering a reliable, sustainable energy system. Unexpected peaks in demand put considerable pressure on energy production systems and are often met through the use of fossil fuels. Although some previous work has analysed energy use of buildings in order to better understand variations of demand, predictions of short-term future energy use at the urban scale are extremely difficult in the absence of information about peoples' activities as these ultimately determine when individuals will use energy for particular end-uses. Understanding the time-variations of energy use will become even more important in the near future, as vehicle fleets are electrified, placing considerable additional load on the grid.

This project will develop a new agent-based simulation that models the daily activities of people in urban areas to estimate when they are likely to be using energy. This is extremely challenging, but the project will mitigate this difficulty by building on two existing, simpler, models. With an emphasis on usability through live cases, we will produce a model that is able to derive times and places of energy demand in cities as a function of the main activities of people. This will enable policy makers and local councils to react to forthcoming demands and test demand management strategies more proactively. Importantly, it will also lay the groundwork for a more comprehensive agent-based model that will include transport networks explicitly and will allow new transport policies related to (e.g.) electric vehicle use to be modelled.

• Energy flexibility
• Energy demand
• Urban analytics
• Sustainability

1. Dynamic Activity Model for Energy
2. Energy Model