使用智能电表数据检测日常活动中的异常
Álvaro Hernández1, Rubén Nieto2, Laura de Diego-Otón1
1Electronics Department, University of Alcala, 28801 Alcalá de Henares, Spain.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究开发了一个智能电表数据分析系统,以检测日常活动异常. 一个重复的神经网络在识别睡眠,早餐和午餐期间的偏差方面取得了高准确性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 智能电表越来越多地用于能源管理,并提供了对家庭活动的非侵入性监控的潜力.
- 分析能源消耗模式可以提供对乘客行为和幸福感的见解.
研究的目的:
- 使用智能电表能源消耗数据设计一个短期行为警报发生器.
- 评估不同的智能系统来预测每小时的能源消耗和检测活动异常.
主要方法:
- 收集的家庭能源消耗数据分类为六个月的每台电器.
- 训练并比较了四个智能系统 (循环神经网络,卷积神经网络,随机森林,决策树) 用于消费预测.
- 统计分析了对实际消费的预测,以检测睡眠,早餐和午餐活动中的异常.
主要成果:
- 循环神经网络在检测日常活动中的异常方面获得了F1得分0.8.
- 这种方法在预测能源消耗和识别行为偏差方面超过了其他方法.
- 该系统展示了使用智能电表数据进行实时活动监控的可行性.
结论:
- 智能电表数据处理可以开发有效的行为警报系统.
- 提出的方法,特别是使用循环神经网络,对环境辅助生活中的应用有希望.
- 这项技术可以通过非侵入性监测,促进加强健康和社会护理.
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