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Title: The development of a novel agent based long term domestic energy stock model
Author: Lee, Timothy
Awarding Body: University of Reading
Current Institution: University of Reading
Date of Award: 2013
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This research has developed a novel long term domestic energy stock model of owner- occupied dwellings in England. Its primary purpose is to aid policy makers in determining appropriate policy measures to achieve CO2 emissions reductions in the housing sector. Current modelling techniques can provide a highly disaggregated technology rich environment, but they do not consider the behaviour required for technological changes to the dwelling stock. Energy efficiency improvements will only occur in the owner-occupied sector of the housing market when owners decide to carry out such improvements. Therefore, a stock model that can simulate this decision making process will be of more use for policy makers in predicting the impact of different measures designed to encourage uptake of suitable technologies. Agent based modelling has been proposed as a solution to allow the inclusion of individual household decision making into a long term domestic stock model. The agents in the model represent households and have a simple additive weighting decision making algorithm based on discrete choice survey data from the Energy Saving Trust and Element Energy. The model has then been calibrated against historic technology diffusion data. Sixteen scenarios have been developed and tested in the model. The initial Business as Usual scenarios indicate that current policies are likely to fall well short of the 2050 80% emissions reduction target, although subsequent scenarios indicate that the target is achievable. The results also indicate that care is required when setting subsidy levels when competing technologies are available, as there is the potential to suppress the diffusion of technologies that offer greater potential savings. The developed model can now be used by policy makers in testing further scenarios, and this novel approach can be applied both regionally and in other countries, subject to the collection of suitable input data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available