使用条件变分自编码器生成合成电动汽车充电会话
Synthetic Electric Vehicle Charging Session Generation Using a Conditional Variational Autoencoder
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中文总结 AI 辅助
本文提出一种条件变分自编码器，从真实交易数据生成合成电动汽车充电会话，保留关键统计特性并支持预测建模，以解决真实数据稀缺问题。
中文摘要 AI 辅助
电动汽车（EV）的日益普及预计将对住宅配电网络带来显著的额外需求，从而需要用于规划和仿真研究的真实充电数据集。然而，由于隐私限制、记录不完整和可用性受限，获取真实世界的电动汽车充电数据往往受到限制。本文提出了一种条件变分自编码器（CVAE），用于从真实的交易级充电数据中生成合成电动汽车充电会话。该模型在工程化的会话特征上进行训练，这些特征描述了插电持续时间、充电持续时间、输送能量、充电延迟和一周内的周期性时间，同时以星期几和受控充电状态为条件。采用高斯负对数似然（NLL）重建损失来建模特征级异方差不确定性，并使用Kullback-Leibler（KL）散度项对潜在空间进行正则化。生成数据的统计保真度通过分布度量和下游任务性能（通过训练于合成测试于真实（TSTR）协议）进行评估。结果表明，所提出的方法生成的合成电动汽车充电会话保留了原始数据集的关键统计特性，同时支持预测建模任务。
英文摘要
The increasing adoption of electric vehicles (EVs) is expected to place significant additional demand on residential distribution networks, creating a need for realistic charging datasets for planning and simulation studies. However, access to real-world EV charging data is often limited due to privacy constraints, incomplete records, and restricted availability. This paper proposes a conditional variational autoencoder (CVAE) for the generation of synthetic EV charging sessions from real transaction-level charging data. The model is trained on engineered session features describing plug-in duration, charging duration, delivered energy, charging delay, and cyclical time-of-week, while conditioning on day of week and managed charging status. A Gaussian negative log-likelihood (NLL) reconstruction loss is employed to model feature-wise heteroscedastic uncertainty, and the latent space is regularised using a Kullback-Leibler (KL) divergence term. The statistical fidelity of the generated data is evaluated using distributional metrics and downstream task performance through the Train-on-Synthetic-Test-on-Real (TSTR) protocol. Results demonstrate that the proposed approach produces synthetic EV charging sessions that preserve key statistical properties of the original dataset while supporting predictive modelling tasks.
发表机构
- University College Cork（科克大学）
- MaREI Centre（MaREI中心）
- Sustainability Institute（可持续发展研究所）
机构由 AI 辅助整理，请以论文原文为准。
