论文标题
数据驱动的不确定性建模和减少能量优化问题的方法
A data-driven uncertainty modelling and reduction approach for energy optimisation problems
论文作者
论文摘要
在做出战略决策时，考虑不确定性至关重要。为了防止不利情景的风险，传统优化技术在其分布的先验知识的基础上结合了不确定性。在本文中，我们展示了如何基于有限的历史数据，我们可以从低维空间中生成不确定性的基本结构，然后可以在此类优化框架中使用。为此，我们首先通过主成分分析来利用不确定性来源之间的相关性，以降低维度。接下来，我们执行聚类以揭示典型的不确定性模式，最后我们基于边缘概率函数的内核密度估计（KDE）生成多面体不确定性集。
Taking uncertainty into account is crucial when making strategic decisions. To guard against the risk of adverse scenarios, traditional optimisation techniques incorporate uncertainty on the basis of prior knowledge on its distribution. In this paper, we show how, based on a limited amount of historical data, we can generate from a low-dimensional space the underlying structure of uncertainty that could then be used in such optimisation frameworks. To this end, we first exploit the correlation between the sources of uncertainty through a principal component analysis to reduce dimensionality. Next, we perform clustering to reveal the typical uncertainty patterns, and finally we generate polyhedral uncertainty sets based on a kernel density estimation (KDE) of marginal probability functions.
