随机计算机模型的多级仿真及其在大型海上风电场的应用
Multilevel Emulation for Stochastic Computer Models with Application to Large Offshore Wind farms
摘要：
大型海上风电场等可再生能源项目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景，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能非常慢，因此可以构建和部署仿真器以允许有效的计算。我们提出了一种新的异方差高斯过程仿真器，利用廉价的近似随机海上风电场模拟器。我们还进行了概率敏感性分析，以了解风电场模型中关键参数的影响，这将有助于我们计划未来的概率启发。
英文摘要：
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.
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