Propagating uncertainty in a network of energy models

Victoria Volodina, Nikki Sonenberg, Jim Q. Smith, Peter G. Challenor, Chris J. Dent, Henry P. Wynn

Research output: Contribution to conferencePaperpeer-review

Abstract

Computational models are widely used in
decision support for energy system 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; this type of
emulator both provides the predictions and quantifies
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, that is interested in considering low carbon
technology options to transform its infrastructure. The
system model employed for this case study is simple,
however, the framework can be applied to larger
networks of more complex models.
Index Terms—energy systems, decision support, surrogate,
Gaussian processes, uncertainty propagation
Original languageEnglish
Number of pages6
Publication statusAccepted/In press - 24 Mar 2022
Event17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022 - Manchester, United Kingdom
Duration: 12 Jun 202215 Jun 2022
https://www.pmaps2022.org/

Conference

Conference17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022
Country/TerritoryUnited Kingdom
CityManchester
Period12/06/2215/06/22
Internet address

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