New Preprint: ETHOS.GeoKit – Streamlining Geospatial Analysis for Energy Systems Modelling

We have published a new preprint introducing ETHOS.GeoKit, a Python toolkit designed to make geospatial data processing in energy systems modelling more structured, reproducible and scalable.

ETHOS.GeoKit forms the methodological backbone of our ETHOS model suite. It removes repetitive preprocessing steps, standardises spatial workflows, and reduces errors related to coordinate and grid harmonisation. This allows researchers to focus on analytical questions rather than data wrangling.

A key feature is the seamless integration of vector and raster data within a coherent data structure. The toolkit is fully compatible with NumPy and Pandas and designed for scalable deployment on HPC clusters.

ETHOS.GeoKit is already being applied within the International Energy Agency Global Energy and Climate Model for renewable energy and Power-to-X (PtX) potential assessments, demonstrating its robustness in an international modelling context.

With ETHOS.GeoKit, we aim to contribute to more transparent, efficient and reproducible energy systems research.

📄 Preprint: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6299208
💻 GitHub: https://github.com/FZJ-IEK3-VSA/geokit
📘 Documentation: https://geokit.readthedocs.io/latest/

Authors: Shitab Ishmam, Julian Belina, Christoph Winkler, Jann Weinand, Noah Pflugradt, Heidi Heinrichs, Jochen Linßen