Energy Data & Learning

Energy systems generate diverse datasets, including for example: technical performance data; physical resources; financial market data; infrastructure; energy demand; and human behaviour in the use and perception of energy. 

Energy systems also require careful data governance, as energy devices in everyday life (electric vehicles, home batteries) proliferate and require ethical data management. Data is analysed to improve  the performance across the technical, social and economic spectrum. As well as analysis, we have expertise in the measurement and collection of data. In order to collect, use and interpret data to deliver the energy transition, researchers at Energy@Ed collaborate across all disciplines. The Space and Satellite Technology Group in GeoSciences and the Coastal and Environmental Remote Sensing Group in Engineering monitor environmental and coastal impacts, extreme weather and climate change, providing data to inform local communities, technology developers, and policy makers at national and international level. GIS tools are used to map energy resources, environment and infrastructure, but also to examine (shifting) spatio-temporal patterns of energy consumption. Climate models are used to predict the impact of climate change on the operation of energy networks and the impact on available renewable resource in the design and location of renewable energy farms. Data on human behaviour in the consumption of energy is collected through the use of sensors, surveys and engagement with citizens and stakeholders, thus improving energy literacy and co-developing effective interventions to provide effective energy services and reduce energy waste. Using machine learning techniques researchers in the School of Informatics collaborate with colleagues in the Schools of Architecture, Social and Political Science, and Engineering to propose modified behaviours to reduce energy use. The Energy Data theme is cross cutting, and feeds into the other research themes. 

Main Contacts:

  • Nigel Goddard
  • Lynda Webb
  • Kate Carter
  • Janette Webb
  • Jamie Cross
  • Dan van der Horst
  • Daniel Fosas
  • Laura Watts
big data graphs of monitoring data in a computer monitor
Energy Data & Learning