Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales
Heat demand of buildings and related CO<sub>2</sub> 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, annua...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/4/1029 |
| _version_ | 1797396419977412608 |
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| author | Malte Schwanebeck Marcus Krüger Rainer Duttmann |
| author_facet | Malte Schwanebeck Marcus Krüger Rainer Duttmann |
| author_sort | Malte Schwanebeck |
| collection | DOAJ |
| description | Heat demand of buildings and related CO<sub>2</sub> 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. |
| first_indexed | 2024-03-09T00:51:01Z |
| format | Article |
| id | doaj.art-eb024eddc2bc4696ba0ffc606ba46a9b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T00:51:01Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-eb024eddc2bc4696ba0ffc606ba46a9b2023-12-11T17:14:11ZengMDPI AGEnergies1996-10732021-02-01144102910.3390/en14041029Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional ScalesMalte Schwanebeck0Marcus Krüger1Rainer Duttmann2Competence Center Geo-Energy, Institute of Geosciences, Kiel University, Ludewig-Meyn-Strasse 10, 24118 Kiel, GermanyDivision of Physical Geography, Landscape Ecology and Geoinformation Science, Institute of Geography Kiel University, Ludewig-Meyn-Str. 14, 24118 Kiel, GermanyDivision of Physical Geography, Landscape Ecology and Geoinformation Science, Institute of Geography Kiel University, Ludewig-Meyn-Str. 14, 24118 Kiel, GermanyHeat demand of buildings and related CO<sub>2</sub> 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.https://www.mdpi.com/1996-1073/14/4/1029GISbuilding stock modelresidential buildingscensus data setsconstruction periodbuilding typology |
| spellingShingle | Malte Schwanebeck Marcus Krüger Rainer Duttmann Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales Energies GIS building stock model residential buildings census data sets construction period building typology |
| title | Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales |
| title_full | Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales |
| title_fullStr | Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales |
| title_full_unstemmed | Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales |
| title_short | Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales |
| title_sort | improving gis based heat demand modelling and mapping for residential buildings with census data sets at regional and sub regional scales |
| topic | GIS building stock model residential buildings census data sets construction period building typology |
| url | https://www.mdpi.com/1996-1073/14/4/1029 |
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