Future Energy Decision Making for Cities - Can Complexity Science Rise to the Challenge?

Lead Research Organisation: University of Nottingham
Department Name: School of Computer Science

Abstract

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Description The project established a base case for the current energy system within the City of Leeds and tested it with historical data. We then assessed interventions that could be implemented by the local authority including support for energy efficiency measures for homes and businesses and local generation of electricity and heat.The models used different scenarios of internal and external interventions and unplanned events (such as fuel price fluctuations or changes in national policy).



We found that complexity science in the shape of agent base dimulation wsa a very helpful tool tto understand these scenarios and generate what-if type question sand answers. The work culminated in the formlisation of diffeernt energy user archetypes and how policy makers should respond to them.
Exploitation Route feed into datamining approaches used by metering compnanies to profile customer populations
Sectors Energy

Environment
 
Description we are in discussions with energy companies how to take the work further . the future emphasis will probably be more on a data mining rather than simulation approach.
Sector Digital/Communication/Information Technologies (including Software),Energy