论文标题
随机计算机模型的多级仿真，并应用于大型海上风电场
Multilevel Emulation for Stochastic Computer Models with Application to Large Offshore Wind farms
论文作者
论文摘要
可再生能源项目，例如大型海上风电场，对于实现政府设定的低排放目标至关重要。随机计算机模型使我们能够探索未来的情况，以帮助决策，同时考虑最相关的不确定性。复杂的随机计算机模型可以过慢地缓慢，因此可以构建并部署模拟器以进行有效的计算。我们提出了一种新型的异质性高斯工艺模拟器，该过程利用了廉价的近似近似近海风电场模拟器。我们还进行了概率灵敏度分析，以了解风电场模型中关键参数的影响，这将帮助我们计划将来的概率启发。
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.
