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Propagating uncertainty in a network of energy models

Volodina, Victoria, Sonenberg, Nikki, Smith, Jim Q., Challenor, Peter G., Dent, Chris J. and Wynn, Henry P. ORCID: 0000-0002-6448-1080 (2022) Propagating uncertainty in a network of energy models. In: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022. 2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022. Institute of Electrical and Electronics Engineers Inc.. ISBN 9781665412117

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Identification Number: 10.1109/PMAPS53380.2022.9810635

Abstract

Computer models are widely used in decision support for energy systems operation, planning and policy. A system of models is often employed, where model inputs themselves arise from other computer models, with each model being developed by different teams of experts. Gaussian Process emulators can be used to approximate the behaviour of complex, computationally intensive models and used to generate predictions together with a measure of uncertainty about the predicted model output. This paper presents a computationally efficient framework for propagating uncertainty within a network of models with high-dimensional outputs used for energy planning. We present a case study from a UK county council considering low carbon technologies to transform its infrastructure to reach a net-zero carbon target. The system model considered for this case study is simple, however the framework can be applied to larger networks of more complex models.

Item Type: Book Section
Official URL: https://ieeexplore.ieee.org/xpl/conhome/9810556/pr...
Additional Information: © 2022 IEEE.
Divisions: Statistics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 16 Aug 2022 10:51
Last Modified: 17 Oct 2022 08:33
URI: http://eprints.lse.ac.uk/id/eprint/116001

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