The urban waste heat (WH) potential within a defined area is initially assessed based on readily available building characteristics. This approach utilizes coefficients that correlate waste heat generation with building size and type, typically defining β€œspecific” WH amounts π‘’π‘Šπ» expressed in kWh per square meter per year (kWh/(mΒ²y)). To determine the total waste heat potential for a given building, these coefficients are multiplied by the building total floor area, Aπ‘“π‘™π‘œπ‘œπ‘Ÿ which is obtained by multiplying the building's footprint area Aπ‘“π‘œπ‘œπ‘‘ by the number of floors π‘›π‘“π‘™π‘œπ‘œπ‘Ÿ, i.e., Aπ‘“π‘™π‘œπ‘œπ‘Ÿ=Aπ‘“π‘œπ‘œπ‘‘ π‘›π‘“π‘™π‘œπ‘œπ‘Ÿ.


Building footprint and floor count data can be retrieved from sources such as Overpass Turbo, OpenStreetMap (possibly combined with DLR open digital terrain model to get the building height and hence an estimate of π‘›π‘“π‘™π‘œπ‘œπ‘Ÿ) or through manual acquisition. The actual WH amount for a given building is hence Eπ‘Šπ»=Aπ‘“π‘™π‘œπ‘œπ‘Ÿ π‘’π‘Šπ». 


While this methodology provides a valuable initial, high-level estimation for urban areas, enabling a broad spatial analysis and the identification of zones with significant heat potential, it is acknowledged that this simplified approach represents a generalized approximation and does not capture the full complexity required for detailed feasibility studies. A comprehensive assessment necessitates more granular information on the specific nature and operational characteristics of urban waste heat sources.


Urban environments harbour diverse low-temperature waste heat sources beyond general building heat rejection, each with unique generation profiles and temperature characteristics. These include, but are not limited to:

  • Commercial Buildings: Particularly supermarkets (from refrigeration systems, often providing heat at 25-40Β°C), shopping malls, and data centers (from cooling IT equipment, typically 25-60Β°C), which offer relatively constant heat availability. These sources are potentially highly accessible due to centralized systems and cooperative owners.
  • Service Sector Buildings: Such as offices, hospitals, ice-rinks (from central cooling devices, 25-40Β°C). These sources face retrofit challenges (e.g., HVAC integration) and also require specific stakeholder agreements.
  • Urban Infrastructure: Including metro stations (from platform ventilation exhaust, 5-35Β°C), and wastewater (sewage networks, 8-15Β°C, as detailed in the following sections, as wastewater is typically classified as renewable). The accessibility for these sources depends on the public hand. With correct communication the potential is high.


Recovery technologies for urban waste heat include heat pumps (COP 3.5–4.5, €500–800/kW) for low-temperature sources (25–60Β°C), suitable for supermarkets and data centers. Heat exchangers (€200–400/kW) are viable for HVAC and ventilation exhaust (e.g., offices). These technologies, aligned with ReUseHeat benchmarks, enable integration with DHC networks. The heat sources are prioritized for integration with DHC networks based on proximity and potential. The specific plans, based on the data collected in WP3, will be optimized in WP4’s scenario analyses.


In HeatMineDH, the data collection for urban waste heat sources was mostly based on a QGIS plugin developed by EURAC within the LIFE4HeatRecovery project. Currently, the plugin targets: supermarkets, shopping malls, hospitals, and ice rinks. Other sources, like laundries and swimming pools, were explored but excluded from the mapping as considered not promising enough. The EURAC plugin retrieves building data from a combination of OpenStreetMap and Google Services: building tags are used for classification and a set of rules is used to avoid duplicates and properly assess the building area when needed. The number of floors, if not available directly, is calculated using first the DLR database for the digital terrain model to estimate the building height and then dividing by a typical floor height1 of 3 m. Concerning data centres, separate information was instead typically used: either data provided by Associated Partners or data (e.g., the DataCenterMap website).


Differently from the case of industrial waste heat, the availability profile of urban WH is more dependent on time. Specific profiles have been developed by EURAC in previous projects (FLEXYNETS, LIFE4HeatRecovery). For example, supermarkets profiles take into account a day/night difference as well as a seasonal difference. The profiles of other activities, like ice-rinks, are instead estimated only at monthly level. Similarly to the case of industrial WH, time profiles are not fully included in the HeatMineDH WH Explorer, but are used for the hourly models used in WP4.


From the point of view of overall urban WH assessment, here below the most relevant cases are summarized, exploiting results developed in LIFE4HeatRecovery (β€œReport on the GIS tool for waste heat recovery opportunities individuation” – Action C.6.4):


Malls and supermarkets2: 

π‘’π‘Šπ»=𝑒𝑒𝑙,π‘“π‘œπ‘œπ‘‘Γ—π‘“π‘“π‘œπ‘œπ‘‘Γ—π‘“π‘Ÿπ‘’π‘“,π‘“π‘œπ‘œπ‘‘Γ—COPπ‘Ÿπ‘’π‘“,


where 

𝑒𝑒𝑙,π‘“π‘œπ‘œπ‘‘ = 500 kWh/(m2β‹…y) is the yearly specific electricity consumption for food shops, 


π‘“π‘“π‘œπ‘œπ‘‘ = 0.2 for malls and 1 for supermarkets (corresponding to an estimate of the shop area devoted to food), 


π‘“π‘Ÿπ‘’π‘“,π‘“π‘œπ‘œπ‘‘ = 0.5 is the electricity share devoted to refrigeration in food shop, and 


COPπ‘Ÿπ‘’π‘“ = 3 is the typical chiller performance for refrigeration. 



Hospitals3: 


π‘’π‘Šπ»=𝑒𝑒𝑙,β„Žπ‘œπ‘ π‘Γ—π‘“π‘Ÿπ‘’π‘“Γ—COPπ‘Ÿπ‘’π‘“, 


where 

𝑒𝑒𝑙,β„Žπ‘œπ‘ π‘ = 600 kWh/(m2β‹…y),         


π‘“π‘Ÿπ‘’π‘“,β„Žπ‘œπ‘ π‘ = 3 %, and 


COPπ‘Ÿπ‘’π‘“ = 3. 


One has π‘’π‘Šπ» = 54 kWh/(m2β‹…y). 



Ice rinks4: 


Eπ‘Šπ»=840 MWh/y. This value applies to standard skating surfaces. Typically, there is a single rink for each ice skating facility, hence in this case the building floor area approach is substituted by a single overall value. 



Footnotes

[1] While this is expected to be reasonably accurate for residential buildings, it can introduce larger uncertainties for commercial buildings. For buildings where a significant WH potential is identified with this preliminary approach (e.g., large shopping malls), a manual check (e.g., using Google Street View) is recommended. 


[2] R. Bointner et al., β€œShopping malls features in EU-28 and Norway” (report elaborated under the FP7 project CommONEnergy), 2014. M. Cozzini and F. Bava, β€œD3.2 – Integration of substations into DHC networks” (deliverable of the FLEXYNETS project), 2018. 


[3] C. Shen, K. Zhao, J. Ge, and Q. Zhou, β€œAnalysis of Building Energy Consumption in a Hospital in the Hot Summer and Cold Winter Area,” Energy Procedia, vol. 158, pp. 3735–3740, 2019. The paper reports an electricity consumption intensity of 600 kWh/mΒ², with 3% of this energy being attributed to refrigeration purposes. 


[4] International Ice Hockey Federation, β€œIIHF Ice Rink Guide”, 2016. The value reported in the text is for a reference case located in Munich and it already accounts for internal WH recovery for space heating.