基于图表注意力机制的多变量时间序列中的建筑能耗异常检测方法
Zhe Zhang1, Yuhao Chen1, Huixue Wang1
1School of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
PloS one
|June 8, 2023
概括
本研究引入了一种新的方法,用于通过多变量时间序列分析检测智能建筑中的能源消耗异常. 该方法通过考虑影响能源使用的多个因素之间的相关性来提高异常检测的准确性.
科学领域:
- 智能建筑系统 智能建筑系统
- 能源管理 能源管理
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 智能建筑中的能源消耗异常是一个关键问题.
- 基于单变量时间序列数据的传统方法对复杂的异常原因的洞察力有限.
- 现有的方法无法捕捉影响能源消耗的变量间的相关性和时间关系.
研究的目的:
- 开发一种先进的异常检测方法,用于智能建筑的能源消耗.
- 通过结合多变量时间序列数据来解决单变量分析的局限性.
- 提高智能建筑环境中异常检测的准确性和全面性.
主要方法:
- 提出了一种新的异常检测框架,利用图形卷积网络 (GCN) 来提取特征变量之间的相关性.
- 通过图表注意力机制增强了GCN框架,以赋予有影响力的时间序列特征差异权重.
- 通过使用标准数据集进行比较分析,与现有技术对拟议方法进行验证.
主要成果:
- 图表注意力增强的GCN模型在检测能源消耗异常方面表现出卓越的性能.
- 该方法有效地捕捉了多种影响因素之间的复杂的相互依赖和时间关系.
- 实验结果证实,与传统的单变量异常检测方法相比,检测准确度更高.
结论:
- 多变量时间序列异常检测方法为智能建筑提供了更强大,更准确的解决方案.
- 整合图形卷积网络和注意力机制显著改善了能源消耗异常的识别.
- 这种方法提供了对导致能源异常的因素的更全面的理解,使得更好的建筑管理成为可能.
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