EPSRC DTP PhD studentship: Quantifying energy and carbon savings from using innovative technologies in domestic buildings

University of Exeter - College of Engineering, Mathematics and Physical Sciences

Main supervisor: Dr. Matt Eames (University of Exeter)
Co-supervisor: Dr. Dan Lash (University of Exeter)

The UK Climate Change Act requires the UK to reduce carbon emissions by 80% by 2050 (1990 baseline).  Emissions arising from direct combustion of fossil fuels in homes accounted for 13% of the UK total in 2013, with the majority of these emissions in the form of space heating. Reducing domestic energy consumption is tackled by considering the standards that new dwellings are constructed to, and through traditional efficiency improvement programmes for the existing stock.  There are significant barriers with both of these approaches; for new homes actual energy consumption is higher than would be expected in spite of improving standards, and for existing homes many of the “low hanging fruit” measures (loft and cavity wall insulation) have already been adopted leaving a policy gap regarding Hard To Treat (HTT) homes.  A significant driver of increasing heating consumption has been the universal adoption of full central heating.  Previous research (Kelly et. al. 2013) has shown that efficiency measures are far less significant than behavioural/socio-demographic factors and intransmutable variables (external temperature, geographic location) regarding domestic heat loss.  This has been confirmed elsewhere where the heating set-point and duration were found to influence internal temperature far more than fabric efficiency (Firth et. al. 2012). 

There are likely to be significant opportunities to reduce domestic heating energy demand through the use of emerging initiatives for example smart heating controls, or dynamic energy tariffs.  However, quantifying these savings is not possible using current building models, and this represents a significant barrier to their uptake.  Current building models are deterministic (meaning critical variations, for example in occupant behaviour or quality of construction, cannot be accounted for in the model), over-simplified (for example using the average temperature in a dwelling, whereas a room by room approach could yield significant savings), and quasi-steady state (making it impossible to estimate energy demand profiles or to implement the types of algorithms embedded in smart heating controls).  The proposed research will undertake probabilistic modelling of the UK housing stock, to capture the variance in both dwelling type and efficiency, and the occupant groups and their behaviours.  This will be used to explore a range of novel non-intrusive technologies to establish the potential energy savings that may be possible.  Ultimately the aim is to design new, or modify existing buildings that are resilient and flexible to accommodate divergent occupants through using reliable and accurate building models. 

Share this PhD
     
  Share by Email   Print this job   More sharing options
We value your feedback on the quality of our adverts. If you have a comment to make about the overall quality of this advert, or its categorisation then please send us your feedback
Advert information

Type / Role:

PhD

Location(s):

South West England