Capturing Multivariate Dependencies of EV Charging Events: From Parametric Copulas to Neural Density Estimation
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| Format: | Preprint |
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2026
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| _version_ | 1866908924996747264 |
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| author | Výboh, Martin Grmanová, Gabriela |
| author_facet | Výboh, Martin Grmanová, Gabriela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29554 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Capturing Multivariate Dependencies of EV Charging Events: From Parametric Copulas to Neural Density Estimation Výboh, Martin Grmanová, Gabriela Machine Learning G.3; I.6.3; J.2 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. |
| title | Capturing Multivariate Dependencies of EV Charging Events: From Parametric Copulas to Neural Density Estimation |
| topic | Machine Learning G.3; I.6.3; J.2 |
| url | https://arxiv.org/abs/2603.29554 |