基于学习的非侵入性电荷监测,用于智能能源管理
Nian He1, Dengfeng Liu2, Zhichen Zhang2
1Zhicheng College, Fuzhou University, Fuzhou 350002, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究介绍了一种用于智能电源管理的新型非侵入性负载监控方法. 计算机视觉和深度学习使大规模物联网网络中的高效能源监控成为可能.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 智能城市需要对大型电力系统进行高效的能源管理.
- 监测和控制电荷对于系统稳定性和成本节约至关重要.
研究的目的:
- 为智能电力管理开发一种经济高效的非侵入性负载监控 (NILM) 方法.
- 为了利用计算机视觉和人工智能进行先进的能源监测.
主要方法:
- 一维电流信号转化为2D彩色特征图像,使用信号转换 (波形,里埃) 和格拉米安角场 (GAF).
- 一个深度神经网络,具有多个尺度的特征提取和注意力机制,用于电荷识别.
- 一种基于云的方法,用于对所有用户的非侵入性监控.
主要成果:
- 拟议的NILM方法在公共和私人数据集上表现出卓越的性能.
- 从转换的功能图像中有效地识别电荷.
- 成功实施基于云的系统,用于大规模监控.
结论:
- 开发的NILM方法支持智能城市的高效能源管理.
- 计算机视觉技术为非侵入性负载监控提供了一种强大的方法.
- 基于云的系统可提高电力系统控制中的能源成本节约.
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