论文标题
风电场布局优化，使用基于集的多目标贝叶斯优化
Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation
论文作者
论文摘要
风能是最干净的可再生电源之一，可以帮助应对气候变化的挑战。风产生的能源的缺点之一是安装风电场所需的巨大空间。这是由于以下事实，即将风力涡轮机放置在有限的区域会阻碍其生产率，因此在经济上不方便。这自然会导致一个优化问题，该问题面临三个特定的挑战：（1）多个相互冲突的目标（2）计算昂贵的仿真模型以及（3）对设计集而不是设计向量的优化。可以通过使用替代辅助的例如\贝叶斯多目标优化来解决第一和第二挑战。但是，传统的贝叶斯优化不能应用于问题中的优化函数，依赖于设计集而不是设计向量。本文将贝叶斯多目标优化的适用性扩展到解决风电场布局问题的基于集合的优化。我们在高斯工艺中使用基于集合的内核来量化风电场之间的相关性（不同数量的涡轮机）。在给定的风能和方向的数据集上的结果清楚地表明了使用基于集合的贝叶斯多目标优化的潜力。
Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind farm; this arises from the fact that placing wind turbines in a limited area would hinder their productivity and therefore not be economically convenient. This naturally leads to an optimisation problem, which has three specific challenges: (1) multiple conflicting objectives (2) computationally expensive simulation models and (3) optimisation over design sets instead of design vectors. The first and second challenges can be addressed by using surrogate-assisted e.g.\ Bayesian multi-objective optimisation. However, the traditional Bayesian optimisation cannot be applied as the optimisation function in the problem relies on design sets instead of design vectors. This paper extends the applicability of Bayesian multi-objective optimisation to set based optimisation for solving the wind farm layout problem. We use a set-based kernel in Gaussian process to quantify the correlation between wind farms (with a different number of turbines). The results on the given data set of wind energy and direction clearly show the potential of using set-based Bayesian multi-objective optimisation.
