Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales

Malte Schwanebeck, Marcus Krüger and Rainer Duttmann
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Malte Schwanebeck: Competence Center Geo-Energy, Institute of Geosciences, Kiel University, Ludewig-Meyn-Strasse 10, 24118 Kiel, Germany
Marcus Krüger: Division of Physical Geography, Landscape Ecology and Geoinformation Science, Institute of Geography Kiel University, Ludewig-Meyn-Str. 14, 24118 Kiel, Germany
Rainer Duttmann: Division of Physical Geography, Landscape Ecology and Geoinformation Science, Institute of Geography Kiel University, Ludewig-Meyn-Str. 14, 24118 Kiel, Germany

Energies, 2021, vol. 14, issue 4, 1-18

Abstract: Heat demand of buildings and related CO 2 emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipality. All three models overestimate reported heat demand on regional levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid cells data set leads to best prediction accuracy values at municipality section level, showing the benefit of integrating this high detailed spatial data set on building age.

Keywords: GIS; building stock model; residential buildings; census data sets; construction period; building typology; municipality sections; hectare grid cells; heat demand density; district heating potential (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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