论文标题
使用智能电表数据的活动检测和建模：概念和案例研究
Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies
论文作者
论文摘要
由于广泛部署的高级计量基础设施，住宅消费者消耗的电力数量是全球电力消耗的很大一部分，公用事业公司可以收集高分辨率的负载数据。通过非感染负载监控，对电器负荷分解的研究兴趣越来越大。由于电器的电力消耗与消费者的活动直接相关，因此本文提出了一种新的，更有效的方法，即活动分解。我们介绍了活动分解的概念，并讨论了其优于传统设备负载分解的优势。我们通过利用机器学习来开发一个基于住宅负载数据和功能的活动检测的框架。我们通过数值案例研究来证明活动检测方法的有效性，并通过时间依赖性活动建模来分析消费者行为。最后但并非最不重要的一点是，我们讨论了一些可能受益于活动分类和一些未来研究方向的潜在用例。
Electricity consumed by residential consumers counts for a significant part of global electricity consumption and utility companies can collect high-resolution load data thanks to the widely deployed advanced metering infrastructure. There has been a growing research interest toward appliance load disaggregation via nonintrusive load monitoring. As the electricity consumption of appliances is directly associated with the activities of consumers, this paper proposes a new and more effective approach, i.e., activity disaggregation. We present the concept of activity disaggregation and discuss its advantage over traditional appliance load disaggregation. We develop a framework by leverage machine learning for activity detection based on residential load data and features. We show through numerical case studies to demonstrate the effectiveness of the activity detection method and analyze consumer behaviors by time-dependent activity modeling. Last but not least, we discuss some potential use cases that can benefit from activity disaggregation and some future research directions.
