Unveiling the Spatial Distribution of Heat Demand in North-West-Europe Compiled with National Heat Consumption Data

Abstract

Space and water heating for residential and commercial buildings amount to a third of the European Union’s total final energy consumption. Approximately 75% of the primary energy is still produced by burning fossil fuels, leading to high greenhouse gas emissions in the heating sector. Therefore, policymakers increasingly strive to trigger investments in sustainable and low-emission heating systems. This study forms part of the “Roll-out of Deep Geothermal Energy in North-West-Europe”-project and aims at quantifying the spatial heat demand distribution in the Interreg North-West-Europe region. An open-source geographic information system and selected Python packages for advanced geospatial processing, analysis, and visualization are utilized to constrain the maps. These were combined, streamlined, and optimized within the open-source Python package PyHeatDemand. Based on national and regional heat demand input data, three maps are developed to better constrain heat demand at a high spatial resolution of 100 m × 100 m (=1 ha) for the residential and commercial sectors, and for both together (in total). The developed methodology can not only be applied to transnational heat demand mapping but also on various scales ranging from city district level to states and countries. In addition, the workflow is highly flexible working with raster data, vector data, and tabular data. The results reveal a total heat demand of the Interreg North-West-Europe region of around 1700 TWh. The spatial distribution of the heat demand follows specific patterns, where heat demand peaks are usually in metropolitan regions like for the city of Paris (1400 MWh/ha), the city of Brussels (1300 MWh/ha), the London metropolitan area (520 MWh/ha), and the Rhine-Ruhr region (500 MWh/ha). The developed maps are compared with two international projects, Hotmaps and Heat Roadmap Europe’s Pan European Thermal Atlas. The average total heat demand difference from values obtained in this study to Hotmaps and Heat Roadmap Europe is 24 MWh/ha and 84 MWh/ha, respectively. Assuming the implementation of real consumption data, an enhancement in spatial predictability is expected. The heat demand maps are therefore predestined to provide a conceptual first overview for decision-makers and market investors. The developed methods will further allow for anticipated mandatory municipal heat demand analyses.

Keywords

• heat demand map
• geographic information system
• Python
• spatial data analysis
• renewable energy
• sustainable energy