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.

A Microsimulation Approach

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.

Spatial Inequality in Energy Efficiency

Energy Performance Certificates are a useful source of data but they only exist for homes bought and sold since 2008 and there can be discrepancies. To overcome this and produce an unbiased estimate of energy intensity at a local scale for differnt types and ages of homes, we have used a Bayesian model to estimate energy intensity combining NEED data on household energy consumption nationally with local EPCs. The map below shows energy intensity estimates for post-2000 homes and the proportion of top-rated wall insulation in those homes by loacl authority.


  • Ruchi Choudhary
  • André Neto-Bradley
  • Nicolas Malleson (University of Leeds)
  • Patricia Ternes (University of Leeds)
  • Brian Matthews (DAFNI)