用新型依赖建模方法精准捕捉电动车充电的多变量关联特性。
Capturing Multivariate Dependencies of EV Charging Events: From Parametric Copulas to Neural Density Estimation
- 引入藤蔓耦合与神经密度估计框架，显式建模充电时间、时长、电量间的复杂依赖。
- 在三个真实数据集上表现优于传统参数模型，接近顶尖基准性能。
- 擅长保留极端情况行为和相关结构，适合电网仿真与智能充电设计。
准确的基于事件的电动汽车（EV）充电建模对电网可靠性和智能充电设计至关重要。传统统计方法虽能捕捉单变量分布，却难以刻画充电变量间复杂的非线性依赖关系，尤其是到达时间、持续时间和能量需求之间的关联。本文首次将藤蔓耦合（Vine copulas）与耦合密度神经估计框架（CODINE）应用于电动车领域。我们在三个不同真实数据集上评估这些高容量依赖模型。结果表明，通过显式建模联合依赖结构，藤蔓耦合与CODINE优于传统参数族模型，在条件高斯混合网络等前沿基准下仍具竞争力。这些方法在保留尾部行为和相关结构方面表现更优，为多样基础设施环境下合成充电事件生成提供了稳健框架。
原文摘要 · Abstract (English)
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.
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