Citing DAVE

A paper describing DAVE and how it is used for generating energy network models has been published in the Nature Journal Scientific Reports. Please acknowledge the usage of DAVE by citing this paper:

You can use the following BibTex entry:

AUTHOR = {Banze, Tobias and Kneiske, Tanja M.},
TITLE = {Open data for energy networks: introducing DAVE---a data fusion tool for automated network generation},
JOURNAL = {Scientific Reports},
VOLUME = {14},
YEAR = {2024},
NUMBER = {1},
PAGES = {1938},
URL = {},
ISSN = {2045-2322},
ABSTRACT = {Developing a sustainable energy system for the future requires new ways of planning and operating energy infrastructure. A large part of this involves suitable network models. Real network data is not available for research without restrictions since energy networks are part of the critical infrastructure. Using open datasets and expert rules to generate non-restricted models is one solution to this. This paper introduces open data for energy networks generated by the open-source software ``DAVE''. The Python-based data fusion tool DAVE can automatically generate customized energy network models quickly and on demand. The software collects data from various databases and uses appropriate methods to fuse them. The current version of the tool can create GIS-based power networks and gas transportation networks, with output that is compatible with common network simulation software. Further developments are planned for creating thermal and gas distribution networks, as these are important for local heat power transition. Implementing a quality description for fused datasets will also be included in future development.},
DOI = {10.1038/s41598-024-52199-w}


About DAVE

The following publications discuss the tool itself and implementation details:

  • T. Banze and T. M. Kneiske, Open data for energy networks: Introducing DAVE - a data fusion tool for automated network generation, Pre-print, 2023, DOI: 10.1109/OSMSES54027.2022.9769084

  • T. Banze, DaVe - Ein Softwaretool zur automatisierten Generierung von regionalspezifischen Stromnetzen, basierend auf Open Data, Masterthesis, 2020, DOI: 10.1109/OSMSES54027.2022.9769084