Case Study: EEP model
The energy efficiency variables used in the STEEV project are generated from the Energy and Environment Prediction (EEP) model developed by the Welsh School of Architecture at Cardiff University. The computing-based model provides an auditing tool for quantifying energy use and associated emissions for cities to help plan to reduce carbon dioxide and other emissions. The model was developed from ESPRC funding which commenced in October 1994 and is based on GIS techniques incorporating a number of sub-models to establish current energy use and CO2 emissions produced by buildings, traffic and industrial processes for a city. The model can predict the effects of future planning decisions from a whole city down to a more local level. The user can identify hotspots of energy use and emissions that can be targeted to make environmental improvements.
The EEP model comprises four sub-models that predict energy use and emissions:
- The domestic sub-model uses built form and age to group properties into 100 different types. Each type has an associated CO2 emission, SAP rating and yearly energy cost associated with it. Every property within the region is surveyed and classified as a ‘type’. Predictions can be made of potential CO2 and energy savings that can be made by installing various energy efficiency measures into properties.
- The non-domestic sub-model provides energy use figures for 48 different types of commercial property. Floor area and type of property are used to predict annual energy use and CO2 emissions for every property.
- The industrial sub-model predicts annual energy use and CO2 emissions for sixteen different industrial sectors using output figures from industries.
- Spatial Analysis procedures are used within the traffic sub-model to predict energy use and emissions from traffic flow on every road within a region.
For the purposes of the STEEV project data from the domestic and non-domestic sub-models will be explored and subsequently visualised.
- The Identification and Analysis of Regional Building Stock Characteristics using Map Based Data (D.K. Alexander, S. Lannon, O. Linovski)
- Modeliing the Built Environment at an Urban Scale – Energy and Health Impacts in Relation to Housing (P. Jones, J. Patterson, S. Lannon)