Open energy system databases

For a list of models that utilize open energy system data, see Open energy system models.

Open energy system database projects employ open data methods to collect, clean, and republish energy-related datasets for open use. The resulting information is then available, given a suitable open license, for statistical analysis and for building numerical energy system models, including open energy system models. Permissive licenses like Creative Commons CC0 and CC BY are preferred, but some projects will house data made public under market transparency regulations and carrying unqualified copyright.

The databases themselves may furnish information on national power plant fleets, renewable generation assets, transmission networks, time series for electricity loads, dispatch, spot prices, and cross-border trades, weather information, and similar. They may also offer other energy statistics including fossil fuel imports and exports, gas, oil, and coal prices, emissions certificate prices, and information on energy efficiency costs and benefits.

Much of the data is sourced from official or semi-official agencies, including national statistics offices, transmission system operators, and electricity market operators. Data is also crowdsourced using public wikis and public upload facilities.[1] Projects usually also maintain a strict record of the provenance and version histories of the datasets they hold. Some projects, as part of their mandate, also try to persuade primary data providers to release their data under more liberal licensing conditions.[a]

Two drivers favor the establishment of such databases. The first is a wish to reduce the duplication of effort that accompanies each new analytical project as it assembles and processes the data that it needs from primary sources. And the second is an increasing desire to make public policy energy models more transparent to improve their acceptance by policymakers and the public.[3] Better transparency dictates the use of open information, able to be accessed and scrutinized by third-parties, in addition to releasing the source code for the models in question.[4]


  1. ^ Arderne, C; Zorn, C; Nicolas, C; Koks, EE (15 January 2020). "Predictive mapping of the global power system using open data" (PDF). Scientific Data. 7 (1): 19. Bibcode:2020NatSD...7...19A. doi:10.1038/s41597-019-0347-4. ISSN 2052-4463. PMC 6962213. PMID 31941897. Retrieved 4 March 2021.
  2. ^ Cite error: The named reference osi-2016 was invoked but never defined (see the help page).
  3. ^ acatech; Lepoldina; Akademienunion, eds. (2016). Consulting with energy scenarios: requirements for scientific policy advice (PDF). Berlin, Germany: acatech — National Academy of Science and Engineering. ISBN 978-3-8047-3550-7. Archived from the original (PDF) on 21 December 2016. Retrieved 19 December 2016.
  4. ^ Cite error: The named reference decarolis-etal-2012 was invoked but never defined (see the help page).


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