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URN: urn:nbn:de:swb:90-463776
DOI: 10.1186/s13705-014-0021-9

Applying Bayesian model averaging for uncertainty estimation of input data in energy modelling

Culka, M.


Energy scenarios that are used for policy advice have ecological and social impact on society. Policy measures that are based on modelling exercises may lead to far reaching financial and ecological consequences. The purpose of this study is to raise awareness that energy modelling results are accompanied with uncertainties that should be addressed explicitly.


With view to existing approaches of uncertainty assessment in energy economics and climate science, relevant requirements for an uncertainty assessment are defined. An uncertainty assessment should be explicit, independent of the assessor’s expertise, applicable to different models, including subjective quantitative and statistical quantitative aspects, intuitively understandable and be reproducible. Bayesian model averaging for input variables of energy models is discussed as method that satisfies these requirements. A definition of uncertainty based on posterior model probabilities of input variables to energy models is presented.


The main findings are that (1) expert elicitation as predominant assessment method does not sa ... mehr

Zugehörige Institution(en) am KIT Institut für Philosophie (PHIL)
Publikationstyp Zeitschriftenaufsatz
Jahr 2014
Sprache Englisch
Identifikator ISSN: 2192-0567
KITopen ID: 1000046377
Erschienen in Energy, Sustainability and Society
Band 4
Heft 1
Seiten 1-17
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagworte Uncertainty; Energy modelling; Assessment methods; Bayesian model averaging
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