HOT|COOL NO. 2/2020 - "Decarbonizing"

Hotmaps dataset is good, but using local data is always better

A toolbox and a large data set for Europe is now available for free for city planners The Hotmaps project addresses this challenge. Leading research institutions in Europe 2) developed a GIS-based website that allows you to have in just 5 minutes an estimate of H&C demand in your region and the potential of local renewable energy to cover this demand. By performing more detailed analyses, the tool supports the development of fully- fledged heating and cooling strategies. The Hotmaps software is • Fast: it provides a quick indication about which direction to go, to kick-start detailed technical planning. • Free and open-source: it is available online, with no fees. You don’t need to install additional tools. • Easy to use: no need to be a GIS expert, the software combines web-based visualization of GIS data with a flexible selection tool. Data are visualized directly on the website. • Adaptable: You can retrieve indicators at various geographical and administrative levels. Moreover, you can upload your data to your account and use it to elaborate comprehensive heating and cooling strategies for your area of interest.

Hotmaps especially provides open GIS data on the distribution of heat demand (HD) in buildings, based on gross floor area (GFA) data. Researchers broke down energy demand data from the national level to the local level using several other (open) data sets. The data collected with a top-down approach was compared with other sources for 20 selected areas across Europe. The average difference of all compared values was 12% (median 8%), with a standard deviation of 10%. A comparison of the developed maps with maps based on municipal building stock datasets (bottom-up approach) for three cities shows that, for these locations, the overall tendency of the distribution of gross floor areas and heat density is similar in both approaches. In figure 2, you can see the difference between bottom-up and top-down dataset. The blue dots indicate that the Hotmaps’ top-down data assign a lower share of energy or gross floor area to a specific hectare cell compared to the bottom-up data (and vice versa for the red dots). Therefore, the developed datasets seem to systematically overestimate the GFA and HD in low-density areas and underestimate the GFA and HD in high-density areas.

Figure 1 a screenshot from the website.

Figure 2 : Difference between the top-down and bottom-up values for each hectare element in three cities: (a) gross floor area (GFA) of all buildings (including industrial and non-energy relevant buildings) in the bottom-up data vs. heated area (HA) in the top-down data (left column), (b) HA in the bottom-up data vs. HA in the top-down data (middle column), and (c) heat demand in the bottom-up and the top-down data (right column). (Müller et al., 2019) Therefore, we believe that the Hotmaps dataset allows performing the first analysis for strategic heat planning, including the identification of areas that might be suitable for district heating. For the detailed planning of supply infrastructure, however, users can upload their own data in the toolbox to get results

Hotmaps provides a large array of data sets with detailed resolution: from NUTS0 data down to LAU2 and even Hectare- level. Default data is available for the entire EU27 area, UK, and Switzerland, intending to support local, regional, and national H&C planning. 3) Hotmaps open-source data sets provide information on: • Building stock; • Space heating, cooling, and domestic hot water demand; • Climate context; • Industrial processes; • Heating and cooling supply; • Renewable energy sources data collection and potential review; • Hourly load profiles.

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