每日电动汽车充电数据集用于训练强化学习算法
Nastaran Gholizadeh1, Petr Musilek1,2
1Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada, T6G 2R3.
研究人员使用条件表式生成对抗网络 (CTGAN) 创建了一个合成电动汽车 (EV) 充电数据集. 这种现实的数据集有助于训练电力系统的强化学习算法.
科学领域:
- 电力系统工程 电力系统工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 强化学习 (RL) 算法对于电力系统优化至关重要.
- 获得大型,现实的数据集是训练RL算法的重大挑战.
- 有效的RL部署需要对高保真数据进行广泛的代训练.
研究的目的:
- 创建一个全面和现实的数据集,用于在电力系统中训练离线强化学习算法.
- 为解决数据采集瓶问题,为电力系统开发强大的RL应用程序.
- 为了生成精确反映现实世界使用模式的合成电动汽车充电数据.
主要方法:
- 利用条件表式生成对抗网络 (CTGAN) 来合成最初电动汽车充电数据集的数据.
- 应用后处理技术以确保合成数据符合充电站容量限制.
- 使用内核密度估计 (KDE) 来复制历史EV连接时间的分布特征.
- 在停车场环境中模拟了29600天的电动汽车充电数据.
主要成果:
- 成功生成了电动汽车充电行为的大规模合成数据集.
- 确保合成数据集保持现实的每日需求概况,并遵守容量限制.
- 使用KDE验证了重复关键的分发功能,例如连接时间,使用KDE.
- 开发了一个专门为离线强化学习算法培训量身定制的数据集.
结论:
- 生成的合成数据集有效地克服了有限的现实世界数据在电力系统中的RL培训的挑战.
- 该方法为像电动汽车充电基础设施这样的复杂系统创建现实的数据集提供了可行的方法.
- 该资源有助于在电力部门推进和部署基于RL的解决方案.
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