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Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Culka, Monika

Abstract: Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy scenarios.


Zugehörige Institution(en) am KIT Institut für Philosophie (PHIL)
Publikationstyp Zeitschriftenaufsatz
Jahr 2016
Sprache Englisch
Identifikator DOI: 10.1186/s13705-016-0073-0
ISSN: 2192-0567
URN: urn:nbn:de:swb:90-679081
KITopen ID: 1000067908
Erschienen in Energy, Sustainability and Society
Band 6
Heft 1
Seiten 7
Lizenz CC BY 4.0: Creative Commons Namensnennung 4.0 International
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagworte Uncertainty assessment; Bayesian model averaging; Energy model; Probability
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