Open Source Data for Gross Floor Area and Heat Demand Density on the Hectare Level for EU 28

Andreas Müller, Marcus Hummel, Lukas Kranzl, Mostafa Fallahnejad and Richard Büchele
Additional contact information
Andreas Müller: E-Think Energy Research, Zentrum für Energiewirtschaft und Umwelt, Argentinierstrasse 18, 1040 Vienna, Austria
Marcus Hummel: E-Think Energy Research, Zentrum für Energiewirtschaft und Umwelt, Argentinierstrasse 18, 1040 Vienna, Austria
Lukas Kranzl: Institute of Energy Systems and Electrical Drives, Energy Economics Group, Technische Universität Wien, Gusshausstr. 25-27, 1040 Vienna, Austria
Mostafa Fallahnejad: Institute of Energy Systems and Electrical Drives, Energy Economics Group, Technische Universität Wien, Gusshausstr. 25-27, 1040 Vienna, Austria
Richard Büchele: Institute of Energy Systems and Electrical Drives, Energy Economics Group, Technische Universität Wien, Gusshausstr. 25-27, 1040 Vienna, Austria

Energies, 2019, vol. 12, issue 24, 1-25

Abstract: The planning of heating and cooling supply and demand is key to reaching climate and sustainability targets. At the same time, data for planning are scarce for many places in Europe. In this study, we developed an open source dataset of gross floor area and energy demand for space heating and hot water in residential and tertiary buildings at the hectare level for EU28 + Norway, Iceland, and Switzerland. This methodology is based on a top-down approach, starting from a consistent dataset at the country level (NUTS 0), breaking this down to the NUTS 3 level and further to the hectare level by means of a series of regional indicators. We compare this dataset with data from other sources for 20 places in Europe. This process shows that the data for some places fit well, while for others, large differences up to 45% occur. The discussion of these results shows that the other data sources used for this comparison are also subject to considerable uncertainties. A comparison of the developed data with maps based on municipal building stock data for three cities shows that the developed dataset systematically overestimates the gross floor area and heat demand in low density areas and vice versa. We conclude that these data are useful for strategic purposes on aggregated level of larger regions and municipalities. It is especially valuable in locations where no detailed data is available. For detailed planning of heating and cooling infrastructure, local data should be used instead. We believe our work contributes towards a transparent, open source dataset for heating and cooling planning that can be regularly updated and is easily accessible and usable for further research and planning activities.

Keywords: open data; heating; building stock; heat map; spatial analysis; heat density map (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/24/4789/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/24/4789/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:24:p:4789-:d:298413

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().