🤖 AI Summary
Existing approaches struggle to accurately capture the complex nonlinear dependencies among arrival time, charging duration, and energy demand in electric vehicle charging events. This work proposes the first application of Vine copulas within the Copula Density Neural Estimation (CODINE) framework to this domain, integrating a conditional Gaussian mixture model network to efficiently model high-dimensional joint dependence structures. The proposed method significantly enhances the ability to capture tail dependencies and intricate correlation patterns. Experimental results on three real-world datasets demonstrate that the approach outperforms conventional parametric copula models, achieves performance comparable to state-of-the-art benchmarks, and generates high-quality synthetic charging events.
📝 Abstract
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.