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

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.

Material efficiency and its contribution to climate change mitigation in Germany: A deep decarbonization scenario analysis until 2060

“…For the LED scenario, the future residential and nonresidential stock levels are given and we used these values as a lower bound in our scenario suite. The split of the construction of all new nonresidential buildings into the different buildings types is modelled after latest historic data (Müller, Hummel, Kranzl, Fallahnejad, & Büchele, 2019). Product lifetime extension is also considered.…”

“…Due to the lack of detailed archetype simulations, material composition and heating energy demand of multi‐family house were used as proxy for the different nonresidential building archetypes, whereas cooling and hot water requirements were assumed to be constant and 2015 values from the European Union (EU) Hotmaps project were used (Müller et al., 2019). The same data source was used to quantify the 2015 in‐use stock and its energy consumption by age‐cohort, and the split of the total energy consumption in to energy carriers was taken from background data for the Energy Technology Perspectives 2017 (OECD/IEA, 2017).…”

Open Data and Energy Analytics

“…A bottom-up approach is used to collect and analyze market data related to space heating and domestic hot water systems and their performance in Europe. Within the same HOTMAPS project, Müller et al [3] face the challenge of uncertainties coming from different databases and from large differences in available datasets among EU countries. A top-down approach is proposed, and a comparison between country-level and municipal-level building stock data is made for gross floor area and energy demand for space heating and domestic hot water.…”

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

“…Since measured data on heat consumption are usually not publicly available, various mapping approaches have been developed, helping to quantify heat demand of buildings and to localize the potentials for district heating. These approaches are often based on Geographic Information Systems (GIS) and range from the scale of a district [16,17], a municipality or a city [18,19] up to national [15,[20][21][22][23], continental [24,25] and even global levels [26].…”