论文标题
电池存储系统/电动汽车的电源管理智能计量系统的异常检测
Anomaly Detection of Smart Metering System for Power Management with Battery Storage System/Electric Vehicle
论文作者
论文摘要
为实时家庭电力管理系统提出了一种能够进行异常检测的新型智能计量技术。实时生成的智能电表数据是从900个单个公寓的家庭中获得的。为了检测智能电表数据中的离群值和缺失值，将深度学习模型（由图形卷积网络和双向长期短期内存网络组成的自动编码器都应用于智能计量技术。基于智能计量技术的电源管理是在电池存储系统和电动汽车的存在下通过多目标优化进行的。使用拟议的智能计量技术的电力管理结果表明，与没有异常检测的电力管理结果相比，电网提供的电力成本和电源量的降低。
A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time was obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was performed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.
