Propagating uncertainty in a network of energy models

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