.. _about_ref: About ===== **PyHeatDemand** is an open-source Python package for processing and harmonizing multi-scale-multi-type heat demand input data for constructing local to transnational harmonized heat demand maps (rasters). Knowledge about the heat demand (MWh/area/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 MWh but also as gas usage, district heating usage, or 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 x 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 since 2023 as a result of works carried out within the `Interreg NWE project DGE Rollout (Rollout of Deep Geothermal Energy) `_. The original codebase was developed in 2021 as part of two master thesis projects at `RWTH Aachen University, Germany `_, and have been presented at a conference in 2021 (`Herbst et al., 2021 `_). The code base has been optimized and extended for this open-source package. The resulting heat demand for North-West Europe has been published on the websites of the `DGE Rollout Webviewer `_. .. image:: ../images/fig2.png The main steps of the methodology to process the provided HD. Authors ------- The following list (sorted by name) shows the authors with substantial contributions to the conception or design of the software. The authors also provided new code or revised existing code and documentation. * Alexander Jüstel (`@AlexanderJuestel `_) * `Eileen Herbst `_ * `Elias Humm (former Khashfe) `_ * `Frank Strozyk `_ Resources --------- * `PyHeatDemand Documentation `_ * `PyHeatDemand Github Repository `_ * `PyHeatDemand Issue Tracker `_ * `PyHeatDemand Discussion Forum `_ * `PyHeatDemand on PyPi `_ * `PyHeatDemand on conda-forge `_ * `DGE Rollout Webviewer `_ Citing PyHeatDemand ------------------- If you are using **PyHeatDemand** for your scientific research, please remember to cite our work. .. code:: @article{Jüstel2024, doi = {10.21105/joss.xxxxx}, url = {https://doi.org/10.21105/joss.xxxxx}, year = {2024}, publisher = {The Open Journal}, volume = {x}, number = {xx}, pages = {xxxx}, author = {Alexander Jüstel and Frank Strozyk}, title = {PyHeatDemand - Processing Tool for Heat Demand Data}, journal = {Journal of Open Source Software} } * `CITATION.cff `_ * `CITATION.md `_ FAIR Principle -------------- The developers of PyHeatDemand want to make the API, the tutorials and examples meet and adhere to the FAIR data principles (e.g. `FAIR Principles `_). **Findable** With each release, the data stored in the PyHeatDemand repositories are uploaded to Zenodo where a persistent identifier is provided for each release. The data for the latest release of PyHeatDemand can be found at `https://zenodo.org/record/ `_. It is referred to Zenodo as the Github repositories do not strictly fulfill the criteria of having a globally unique and persistent identifier assigned to the (meta)data. However, all code and data can currently be found at `https://github.com/AlexanderJuestel/pyheatdemand `_. **Accessible** The files stored in the respective Zenodo repositories can be downloaded without registration as ZIP file. In addition, the data can be downloaded from the aforementioned Github repositories without registration as ZIP files or via `git `_. The functionality of PyHeatDemand can be easily accessed through installing the software using `conda-forge `_ or `pip `_. Please see also the `Installation Instructions ` provided. **Interoperable** No commercial software is needed to read or alter the data provided in the repositories. Files containing code can be opened with any text editor, vector and raster data can be opened with open-source software such as `QGIS `_ or the respective Python libraries such as GeoPandas or Rasterio. Mesh data can also be opened using text editors or Python packages such as PyVista or open-source software like Blender. We mostly use file formats that are common to the geospatial community (.shp, .tif, ZMAP-Grids, etc.) and that are not proprietary. **Reusable** The provision of tutorials, examples and in fact this documentation makes the data provided in the repositories reusable under the license provided below. License ------- GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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You may place library facilities that are a work based on the Library side by side in a single library together with other library facilities that are not Applications and are not covered by this License, and convey such a combined library under terms of your choice, if you do both of the following: a) Accompany the combined library with a copy of the same work based on the Library, uncombined with any other library facilities, conveyed under the terms of this License. b) Give prominent notice with the combined library that part of it is a work based on the Library, and explaining where to find the accompanying uncombined form of the same work. 6. Revised Versions of the GNU Lesser General Public License. The Free Software Foundation may publish revised and/or new versions of the GNU Lesser General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Library as you received it specifies that a certain numbered version of the GNU Lesser General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that published version or of any later version published by the Free Software Foundation. If the Library as you received it does not specify a version number of the GNU Lesser General Public License, you may choose any version of the GNU Lesser General Public License ever published by the Free Software Foundation. If the Library as you received it specifies that a proxy can decide whether future versions of the GNU Lesser General Public License shall apply, that proxy's public statement of acceptance of any version is permanent authorization for you to choose that version for the Library. References ---------- Jüstel, A., Humm, E., Herbst, E., Strozyk, F., Kukla, P. & Bracke, R., 2024. Unveiling the Spatial Distribution of Heat Demand in North-West-Europe Compiled with National Heat Consumption Data. Energies, 17 (2), 481, https://doi.org/10.3390/en17020481 Herbst, E., Khashfe, E., Jüstel, A., Strozyk, F. & Kukla, P., 2021. A Heat Demand Map of North-West Europe – its impact on supply areas and identification of potential production areas for deep geothermal energy. GeoKarlsruhe 2021, http://dx.doi.org/10.48380/dggv-j2wj-nk88.