Combining GIS-based statistical and engineering urban heat consumption models: Towards a new framework for multi-scale policy support

Abstract Diagnosing and modelling precisely the actual energy consumption at the urban scale is the indispensable starting point of any low-carbon urban energy policy. This paper compares two building heat consumption models for urban scale applications: a statistical model based on 2D-GIS and multiple linear regression, and an engineering model making use of 3D city models and monthly energy balance of standard EN ISO 13790. Both methods are combined in a new multi-scale framework for improved prediction of heat demand and energy savings potential of building stock at the several scales within the city. This multi-scale framework was tested for the case study of Bospolder – Rotterdam (Netherlands) including around 1000 buildings. Firstly, the statistical model predicts the energy consumption of buildings at the city scale, then relevant neighbourhoods for retrofitting plans are selected and precisely modelled using the engineering model, finally individualized energy savings potentials are predicted building by building. The prediction provided by the two models demonstrated a good agreement with measured gas consumption data at the neighbourhood level (5–25% deviation), while errors become higher at disaggregated level. Major differences result from the ability of each model to cope with the lack of information concerning subsequently refurbished buildings, occupants’ profile and behaviour, and unoccupied buildings. The study showed the ability and effectiveness of the multi-scale framework to support decision about retrofitting plans at different levels and scales.

[1]  V. Ismet Ugursal,et al.  Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .

[2]  Volker Coors,et al.  INTEGRATING QUALITY MANAGEMENT INTO A 3D GEOSPATIAL SERVER , 2011 .

[3]  Gian Vincenzo Fracastoro,et al.  A methodology for assessing the energy performance of large scale building stocks and possible appli , 2011 .

[4]  Keith Baker,et al.  Improving the prediction of UK domestic energy-demand using annual consumption-data , 2008 .

[5]  Volker Coors,et al.  Citygml-based 3d City Model For Energy Diagnostics And Urban Energy Policy Support , 2013, Building Simulation Conference Proceedings.

[6]  Bodil Merethe Larsen,et al.  Household electricity end-use consumption: results from econometric and engineering models , 2004 .

[7]  F. Stazi,et al.  Estimating energy savings for the residential building stock of an entire city: A GIS-based statistical downscaling approach applied to Rotterdam , 2014 .

[8]  Vijay Modi,et al.  Spatial distribution of urban building energy consumption by end use , 2012 .

[9]  Henk Visscher,et al.  The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock , 2009 .

[10]  Ronald Christensen.  Plane Answers to Complex Questions , 1987 .

[11]  Filip Johnsson,et al.  A modelling strategy for energy, carbon, and cost assessments of building stocks , 2013 .

[12]  Ulrich Leopold,et al.  iGUESS - A web based system integrating Urban Energy Planning and Assessment Modelling for multi-scale spatial decision making , 2012 .

[13]  Dejan Mumovic,et al.  A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .

[14]  Diane Perez,et al.  A framework to model and simulate the disaggregated energy flows supplying buildings in urban areas , 2014 .