发表机构
University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建了挪威特隆赫姆2020-2030年寒区高分辨率合成EV充电数据集，整合多类特征与CTGAN等模型生成，为配电网相关分析提供基准。
AI 中文摘要
本数据文章提供了一个针对挪威特隆赫姆的高分辨率长期合成电动汽车（EV）充电数据集，时间跨度为2020年2月至2030年12月。该数据集基于2018年12月至2020年1月共14个月的历史充电日志构建，捕捉了会话级行为模式，包括交付电量、插电时长、连接时段、用户分类（私人与共享）、季节变化、公共假日影响以及与日环境温度的相关性。为模拟未来电气化动态，合成生成流程整合了历史会话记录、挪威气象研究所（MET Norway）的日历与天气特征、来自挪威统计局（SSB）注册轨迹推导的年度EV adoption增长乘数、每日会话数模型、条件表格生成对抗网络（CTGAN）、季节核密度估计（KDE）以及生成后物理充电器功率可行性修正。在标准化7.2 kW交流充电约束下，生成的中等EV adoption场景数据集包含76993条小时级充电活动记录。该小时剖面为基于活动的形式，而非完整连续小时时间序列；未分配EV充电能量的小时不被纳入。记录内容包括总小时充电能量、等效平均充电功率、活跃会话数、私人/共享用户负荷细分、环境温度特征及日历指标。该数据集为寒区配电网影响评估、变压器负载分析、EV充电需求预测、充电器容量规划、能源管理优化及数据驱动智能充电控制策略开发提供了经过验证的基准。
英文摘要
This data article presents a high-resolution, long-term synthetic electric-vehicle (EV) charging dataset for Trondheim, Norway, spanning February 2020 to December 2030. Empirically grounded in 14 months of historical charging logs from December 2018 to January 2020, the dataset captures session-level behavioral patterns, including delivered energy, plug-in duration, connection schedules, user categorization (private vs. shared), seasonal variations, public-holiday effects, and daily ambient temperature dependencies. To model future electrification dynamics, the synthetic generation pipeline integrates historical session records, calendar and weather features from MET Norway, annual EV-adoption growth multipliers derived from Statistics Norway (SSB) registration trajectories, a daily session-count model, a Conditional Tabular Generative Adversarial Network (CTGAN), seasonal Kernel Density Estimation (KDE), and post-generation physical charger-power feasibility correction. Under a standardized 7.2 kW AC charging constraint, the resulting medium EV-adoption scenario dataset contains 76,993 hourly charging-activity records. The hourly profile is activity-based rather than a complete continuous hourly time series; hours with no allocated EV charging energy are not included. The records provide total hourly charging energy, equivalent average charging power, active session counts, private/shared user load breakdowns, ambient temperature features, and calendar indicators. The dataset provides a validated cold-climate benchmark for distribution-grid impact assessment, transformer-loading analysis, EV charging-demand forecasting, charger-capacity planning, energy-management optimization, and the development of data-driven smart-charging control strategies.
Comments10 pages, 4 figures, 2 tables. Dataset available on Zenodo: DOI 10.5281/zenodo.22132618
