Hotmaps - Open source mapping and planning tool

Hotmaps is an open source project for local, regional and national administrations on heating and cooling planning (heating and cooling in the residential and industrial sectors account for half of the EU's energy consumption). The project started in October 2016 and was funded by the European Union's Horizon 2020 research and innovation program.

The Hotmaps project is designed to support public authorities and urban planners, and to develop strategic local heating, cooling and cooling plans. The project is aligned with renewable energy sources (RES) and CO2 emissions targets and is built on three main pillars.

- First, Hotmaps is user driven. It was developed in close collaboration with 7 European pilot areas in Ireland, Great Britain, Spain, Germany, Denmark, Switzerland and Romania. More than 20 areas of followers have joined the project across Europe as Hotmaps cities.

- Secondly, Hotmaps was designed based on the principles of free and open source software, all related modules being available for free. In addition, there is an open data set available to support users. Finally, the project is compatible and implicitly adaptable to all European countries thanks to the EU's Horizon 2020 research and innovation program. Hotmaps will be available for cities in all European Member States in the future.

Built primarily using Python, Hotmaps is a geographic information system (GIS) that helps European member states plan their strategic heating and cooling.

- identifies the distribution of demand and supply with heating and cooling;
- emphasizes the potential sources of renewable energy to provide heating and cooling in a certain area;
- identifies potential sources of heat waste from industrial installations;
- estimates the potential of efficient district heating options in a selected area;
- develops potential scenarios for decarbonization of heating and cooling.

So far, Hotmaps has been released as a beta version with basic features. An update is scheduled for next year. The data sets used in the tool can be found on GitLab, and the source code is available on GitHub under a CC-BY-4.0 license.



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