The perfect match of data and heat planning

Cities need data to make sustainable and cost-efficient decisions


The role of heating in achieving a reduction target of 55% of greenhouse gas emissions by 2030 is increasing. The recast Energy Efficiency Directive, as part of the legislative set presented by the European Commission on 14 July, foresees the requirement of a heat planning for cities over 50,000 inhabitants. It will be key for cities to have an easy access to data to draft relevant heat plans.

Cities are often a step ahead: some did not wait to take measures to harness heat data. Information on available resources and heat demand in a defined area is a strong support to the design of locally best-fitted solutions (individual heating systems, district heating…). In the Netherlands, the open data approach has been favoured for long. Energy utility companies must publish the effective consumption data of small clusters of buildings, which proves very helpful for cities to do urban planning at district level. In addition, oil and gas companies are required to share data from their exploratory studies, which gives cities access to data to estimate the geothermal energy potential of an area. More recently, in Poland, an obligation for households to declare the heating system they use entered into force on 1st of January 2021.

Why are data so important?

Data provide a better understanding of the reality and are a true support to make sustainable and cost-efficient decisions. For instance, the Metropolitan City of Milano wanted to investigate the possibilities to refurbish the building stock envelop and to promote renewable heating and cooling through district heating[1]. Politecnico di Milano, who carried out the research study on the topic, focused on the following dataset:

  • Heat: Heat demand, heating sources (existing and potential)
  • Residential sector: Residential sector heating demand density, density of residential building, residential building total energy demand
  • Heating systems: Heat potential for district heating, potential district heating sources, best technologies for heating in areas of non-district heating suitability
  • Environment: Environmental impact indicators.

The secret of good projections

The data collection showed two main findings[2]:

  • More energy efficient buildings will drive the network costs higher, reducing district heating potential. But it also showed that low-temperature district heating could compensate for the drop in its cost-effectiveness.
  • Building renovation should be performed with criteria, and not evenly.

Collecting these data helped Milan identify the most interesting areas, from both a heat density and an economic point of view, and then investigate the potential synergies with available heat sources. Of course, additional parameters can help finetune the modelling, such as building ages, materials, surface and height, or heating systems. The optimum solution to a good projection often lies between direct collection and data modelling.

Find out more on the website of our project DecarbCityPipes 2050 website!  

Data collection for renewable heating and cooling uptake

 Data are thus an essential component of heat planning. In the United Kingdom, local governments can receive grants from the Heat Network Delivery Unit to perform feasibility studies and the related early stages of infrastructure development. This includes heat mapping, energy masterplans, techno-economic feasibility, and detailed project development[3]. All this information also contributes to reassuring investors, providing them with valuable information on viability of projects and long-term perspectives. As highlighted in a report by IRENA[4], one of the main barriers to renewable heating and cooling uptake is the inadequate data and statistics on types and amounts of energy required to meet heating and cooling needs. Collecting data is also a way to favour the penetration of renewable energies in heating systems. Given their long lifetime, they require long-term planning for urban development: the more accurate the data, the easier to plan the decarbonisation.
Human resources and training are key

So, what does it mean concretely? Data collection is a crucial support for cities to plan energy district networks and to do heat mapping and zoning. It helps develop possible decarbonisation scenarios and discuss them with stakeholders and citizens. However, it requires human resources and training for data and heat planning to be the perfect match. All EU Member States should take inspiration from the Netherlands, Poland, and the United-Kingdom to remove barriers to access data for cites and to support them technically and financially.


[1] Deep Demonstration: Milan. Energy Workshop of 4th February 2021.

[2] Spirito, G.; Dénarié, A.; Fattori, F.; Motta, M.; Macchi, S.; Persson, U. Potential Diffusion of Renewables-Based DH Assessment through Clustering and Mapping: A Case Study in Milano. Energies 2021, 14, 2627.

[3] IRENA, IEA and REN21 (2020), ‘Renewable Energy Policies in a Time of Transition: Heating and Cooling’. IRENA, OECD/IEA and REN21

[4] IRENA, IEA and REN21 (2020), ‘Renewable Energy Policies in a Time of Transition: Heating and Cooling’. IRENA, OECD/IEA and REN21

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Publication date

September 10, 2021