Analysing the impact of spatial context on the heat consumption of individual households

A. Rafiee*, E. Dias, E. Koomen

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The heating of houses comprises a considerable share of the total energy consumption in many developed countries in temperate and colder climates. While most of the factors affecting space heating depend on individual choices (e.g. occupants’ behaviour, interior building design, heating system efficiency) that are difficult to influence through urban planning, spatial context of individual housing units is within the sphere of influence of planners. Yet the impact of spatial context has hitherto received limited research attention due to the lack of geospatial data and the massive computer processing required to capture the shape and surroundings of individual housing units. Regression analysis was performed in this study to explain the yearly gas consumption of all individual housing units in the city of Amsterdam, the Netherlands. The analysis focused on the impact of spatial context variables at two complementary scales: housing unit and postal code level. State-of-the-art 3D Geographic Information System (GIS) techniques were applied for the efficient processing of massive 3D geospatial data for all buildings in the city. Two- and three-dimensional spatial context of individual housing units were described using spatial data processing routines that characterised building shape and its surroundings. The local housing unit level results highlighted the benefits of compact, dense urban forms: denser neighbourhoods with less open space and buildings with higher numbers of housing units and less exposed perimeters have lower heating demand. Trees were found to limit energy consumption when they are located on the colder northwest side of building units. The more aggregate postal code level results showed the importance of demographic composition: (larger) households with children consume most energy. Size and age of the housing unit are important determinants of energy at both scale levels: older houses have a higher energy consumption, but a rebound effect was found for the newest dwellings.

Original languageEnglish
Pages (from-to)461-470
Number of pages10
JournalRenewable and Sustainable Energy Reviews
Volume112
DOIs
Publication statusPublished - 1 Sep 2019

Keywords

  • 3D geospatial data
  • Geographical information system
  • Household heat consumption
  • Quantitative assessment
  • Spatial context
  • Urban configuration elements

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