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000859310 1001_ $$0P:(DE-Juel1)166140$$aGotzens, Fabian$$b0$$eCorresponding author
000859310 245__ $$aPerforming energy modelling exercises in a transparent way - the issue of data quality in power plant databases
000859310 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2019
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000859310 520__ $$aIn energy modelling, open data and open source code can help enhance traceability and reproducibility of model exercises which contribute to facilitate controversial debates and improve policy advice. While the availability of open power plant databases increased in recent years, they often differ considerably from each other and their data quality has not been systematically compared to proprietary sources yet. Here, we introduce the python-based ‘powerplantmatching’ (PPM), an open source toolset for cleaning, standardizing and combining multiple power plant databases. We apply it once only with open databases and once with an additional proprietary database in order to discuss and elaborate the issue of data quality, by analysing capacities, countries, fuel types, geographic coordinates and commissioning years for conventional power plants. We find that a derived dataset purely based on open data is not yet on a par with one in which a proprietary database has been added to the matching, even though the statistical values for capacity matched to a large degree with both datasets. When commissioning years are needed for modelling purposes in the final dataset, the proprietary database helps crucially to increase the quality of the derived dataset.
000859310 536__ $$0G:(DE-HGF)POF3-153$$a153 - Assessment of Energy Systems – Addressing Issues of Energy Efficiency and Energy Security (POF3-153)$$cPOF3-153$$fPOF III$$x0
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000859310 7001_ $$0P:(DE-Juel1)145221$$aHeinrichs, Heidi$$b1
000859310 7001_ $$00000-0001-9438-767X$$aHörsch, Jonas$$b2
000859310 7001_ $$aHofmann, Fabian$$b3
000859310 773__ $$0PERI:(DE-600)2652346-2$$a10.1016/j.esr.2018.11.004$$gVol. 23, p. 1 - 12$$p1 - 12$$tEnergy strategy reviews$$v23$$x2211-467X$$y2019
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