定义用于实际电动车辆充电会话的合成数据生成器
Defining a synthetic data generator for realistic electric vehicle charging sessions
摘要：
电动汽车（EV）充电站在过去几年中已经在电网中变得突出。EV充电会话的分析对于灵活性分析、负载均衡、向客户提供激励等是有用的。然而，这种EV会话数据的有限可用性阻碍了这些领域的进一步发展。为了满足对公开可用和现实数据的需求，我们开发了一个用于EV充电会话的合成数据生成器（SDG）。我们的SDG假设EV到达间隔时间遵循指数分布。出发时间通过定义连接时间的条件概率密度函数（pdf）来建模。连接时间和所需能量的pdf由高斯混合模型拟合。由于我们使用大型真实世界数据集训练SDG，因此其输出是现实的。
英文摘要：
Electric vehicle (EV) charging stations have become prominent in electricity grids in the past years. Analysis of EV charging sessions is useful for flexibility analysis, load balancing, offering incentives to customers, etc. Yet, the limited availability of such EV sessions data hinders further development in these fields. Addressing this need for publicly available and realistic data, we develop a synthetic data generator (SDG) for EV charging sessions. Our SDG assumes the EV inter-arrival time to follow an exponential distribution. Departure times are modeled by defining a conditional probability density function (pdf) for connection times. This pdf for connection time and required energy is fitted by Gaussian mixture models. Since we train our SDG using a large real-world dataset, its output is realistic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
