捕捉电动汽车充电事件的多变量依赖性：从参数Copula到神经密度估计
Capturing Multivariate Dependencies of EV Charging Events: From Parametric Copulas to Neural Density Estimation
AI总结：
本文提出利用Vine copulas和CODINE框架建模电动汽车充电事件的多变量依赖性，通过实验证明其在捕捉尾部行为和相关结构方面优于传统方法。
摘要：
准确的基于事件的电动汽车充电建模对于电网可靠性和智能充电设计至关重要。尽管传统统计方法能捕捉边缘分布，但往往难以建模充电变量之间的复杂非线性依赖关系，特别是到达时间、持续时间和能源需求。本文通过引入Vine copulas和Copula Density Neural Estimation框架（CODINE）到电动汽车领域，评估了这些高容量依赖模型在三个多样化真实世界数据集上的表现。结果表明，通过显式建模联合依赖结构，Vine copulas和CODINE在参数族中表现更优，并在条件高斯混合模型网络等先进基准中保持高度竞争力。我们展示了这些方法在保持尾部行为和相关结构方面的优越性，为不同基础设施情境下的合成充电事件生成提供了稳健的框架。
英文摘要：
Accurate event-based modeling of electric vehicle (EV) charging is essential for grid reliability and smart-charging design. While traditional statistical methods capture marginal distributions, they often fail to model the complex, non-linear dependencies between charging variables, specifically arrival times, durations, and energy demand. This paper addresses this gap by introducing the first application of Vine copulas and Copula Density Neural Estimation framework (CODINE) to the EV domain. We evaluate these high-capacity dependence models across three diverse real-world datasets. Our results demonstrate that by explicitly focusing on modeling the joint dependence structure, Vine copulas and CODINE outperform established parametric families and remain highly competitive against state-of-the-art benchmarks like conditional Gaussian Mixture Model Networks. We show that these methods offer superior preservation of tail behaviors and correlation structures, providing a robust framework for synthetic charging event generation in varied infrastructure contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
