Developing a geographically detailed housing stock model for the North East of England
conference contributionposted on 19.12.2014, 11:35 authored by Timothy Lee, Candy He, Simon Taylor, Steven FirthSteven Firth, Kevin LomasKevin Lomas
Housing stock models predict long term changes in the stock to inform national policy. They operate with a set of reference dwellings representing the national stock, which are changed in response to different scenarios. However, national level models do not consider geographical variations (urban location/rural surroundings, index of multiple deprivation score, etc.), so cannot aid in targeting improvement measures (eg: insulation, microgeneration, etc.) locally. A geographically varying model can identify which measures are most appropriate in a particular location. In this paper a method has been designed and implemented using information at LSOA level (c. 700 dwellings each) to introduce geographical variation for a model of the North East of England. It has been tested against DECC meter data and over 80% of LSOAs are predicted to within ±25% of DECC’s data. The model allows localised policies and interventions to be tested, and is principally of interest to local government and energy efficiency initiatives.
This work is supported by the Engineering and Physical Sciences Research Council (EPSRC) under their Sustainable Urban Environment Programme (grant EP/I002154/1) to SECURE: Self Conserving Urban Environments, https://www. secure-project.org/. SECURE is a consortium of four UK universities: Newcastle University, the University of Sheffield, the University of Exeter and Loughborough University.
- Architecture, Building and Civil Engineering