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首页> 外文学位 >Non-Intrusive Load Monitoring: Disaggregation of Energy by Unsupervised Power Consumption Clustering.
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Non-Intrusive Load Monitoring: Disaggregation of Energy by Unsupervised Power Consumption Clustering.

机译：非侵入式负载监控：通过无监督功耗聚类进行能量分解。

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摘要

There is a growing trend in monitoring residential infrastructures to provide inhabitants with more information about their energy consumption and help them to reduce usage and cost. Device-level power consumption information, while a functionality in newer smart appliances, is not generally available to consumers.;In electricity consumption disaggregation, Non-Intrusive Load Monitoring (NILM) refers to methods that provide consumers estimates of device-level energy consumption based on aggregate measurements usually taken at the main circuit panel or electric meter. The traditional NILM approach characterizes changes in the power signal when devices turn on or off, and it infers the consumption of different devices present in the home based on these changes. Generally, these NILM methods require training and models of the devices present in the home in order to function properly. Because of these challenges, much of the NILM literature does not address the actual energy disaggregation problem but focuses on detecting events and classifying changes in power.;In this dissertation, we propose a relaxation to the traditional NILM problem and provide an unsupervised, data-driven algorithm to solve it. Specifically, we propose Power Consumption Clustered Non-Intrusive Load Monitoring (PCC-NILM), a relaxation that reports on the energy usage of devices grouped together by power consumption levels. In order to solve the PCC-NILM problem, we provide the Approximate Power Trace Decomposition Algorithm (APTDA). Unlike other methods, APTDA does not require training and it provides estimated energy consumption for different classes of devices.
机译：监视住宅基础设施以向居民提供有关其能源消耗的更多信息并帮助他们减少使用量和成本的趋势正在日益增长。设备级别的功耗信息虽然是较新的智能设备中的功能，但通常不向消费者提供。在功耗分类中，非侵入式负载监控（NILM）指的是为消费者提供基于设备级别能耗估算的方法。通常在主电路板或电表上进行的总测量。传统的NILM方法可表征设备打开或关闭时电源信号的变化，并根据这些变化推断家庭中存在的不同设备的功耗。通常，这些NILM方法需要对家庭中存在的设备进行培训和建模，以使其正常运行。由于这些挑战，许多NILM文献都没有解决实际的能量分配问题，而是着重于检测事件和对功率变化进行分类。在本论文中，我们提出了对传统NILM问题的放松，并提供了无监督的数据-驱动算法来解决。具体而言，我们提出了功耗集群式非侵入式负载监控（PCC-NILM），这是一种放松报告，其中报告了按功耗级别分组在一起的设备的能耗。为了解决PCC-NILM问题，我们提供了近似功率迹线分解算法（APTDA）。与其他方法不同，APTDA不需要培训，并且可以提供不同类别设备的估计能耗。

著录项

  • 作者

    Anderson, Kyle D.;

  • 作者单位

    Carnegie Mellon University.;

  • 授予单位 Carnegie Mellon University.;
  • 学科 Electrical engineering.;Environmental engineering.;Civil engineering.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 145 p.
  • 总页数 145
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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