  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.
