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2024
Journal Article
Title
PyHeatDemand - Processing Tool for Heat Demand Data
Abstract
PyHeatDemand is an open-source Python package for processing and harmonizing multi-scalemulti-type heat demand input data for constructing local to transnational harmonized heat demand maps (rasters). Knowledge about the heat demand in megawatt hours per area and per year of a respective building, district, city, state, country, or even on a continental scale is crucial for an adequate heat demand analysis or planning for providing power plant capacities.
Mapping of the heat demand may also identify potential areas for new district heating networks or even geothermal power plants for climate-friendly heat production.
The aim of PyHeatDemand is to provide processing tools for heat demand input data of various categories on various scales. This includes heat demand input data provided as rasters or gridded polygons, heat demand input data associated with administrative areas (points or polygons), with building footprints (polygons), with street segments (lines), or with addresses directly provided in kWh or MWh but also as gas usage, district heating usage, or other sources
of heat. It is also possible to calculate the heat demand based on a set of cultural data sets (building footprints, height of the buildings, population density, building type, etc.). The study area is first divided into a coarse mask before heat demands are calculated and harmonized for each cell with the size of the target resolution (e.g., 100 m × 100 m for states). We hereby make use of different spatial operations implemented in the GeoPandas and Shapely packages.
The final heat demand map will be created utilizing the Rasterio package. Next to processing tools for the heat demand input data, workflows for analyzing the final heat demand map through the Rasterstats package are provided.
PyHeatDemand was developed as a result of works carried out within the Interreg NWE project DGE Rollout (Rollout of Deep Geothermal Energy). The development and maintenance of PyHeatDemand will continue in the future beyond the duration of the project. This will include adding bottom-up workflows based on building specifics to calculate the heat demand.
Mapping of the heat demand may also identify potential areas for new district heating networks or even geothermal power plants for climate-friendly heat production.
The aim of PyHeatDemand is to provide processing tools for heat demand input data of various categories on various scales. This includes heat demand input data provided as rasters or gridded polygons, heat demand input data associated with administrative areas (points or polygons), with building footprints (polygons), with street segments (lines), or with addresses directly provided in kWh or MWh but also as gas usage, district heating usage, or other sources
of heat. It is also possible to calculate the heat demand based on a set of cultural data sets (building footprints, height of the buildings, population density, building type, etc.). The study area is first divided into a coarse mask before heat demands are calculated and harmonized for each cell with the size of the target resolution (e.g., 100 m × 100 m for states). We hereby make use of different spatial operations implemented in the GeoPandas and Shapely packages.
The final heat demand map will be created utilizing the Rasterio package. Next to processing tools for the heat demand input data, workflows for analyzing the final heat demand map through the Rasterstats package are provided.
PyHeatDemand was developed as a result of works carried out within the Interreg NWE project DGE Rollout (Rollout of Deep Geothermal Energy). The development and maintenance of PyHeatDemand will continue in the future beyond the duration of the project. This will include adding bottom-up workflows based on building specifics to calculate the heat demand.
