  Renewable energy projects, such as large offshore wind farms, are critical to
achieving low-emission targets set by governments. Stochastic computer models
allow us to explore future scenarios to aid decision making whilst considering
the most relevant uncertainties. Complex stochastic computer models can be
prohibitively slow and thus an emulator may be constructed and deployed to
allow for efficient computation. We present a novel heteroscedastic Gaussian
Process emulator which exploits cheap approximations to a stochastic offshore
wind farm simulator. We also conduct a probabilistic sensitivity analysis to
understand the influence of key parameters in the wind farm model which will
help us to plan a probability elicitation in the future.
