非侵入式负载监测中学习负载模式的无监督方法

Saman Mostafavi, R. Cox
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引用次数: 6

摘要

提出了一种非侵入式负荷监测的新方法。该技术可用于开发一个强大的框架,用于建筑物,特别是小型商业和住宅部门的低成本电力监测。该方法提出了一个关于负荷模式的先验知识数据库的构建,它提供了一个强大的平台,有能力解决电力监测和能源管理中的一个主要挑战,即开发鲁棒无监督学习算法,消除了昂贵的人工干预。为此,在形成贝叶斯网络的基础上,提出了一种负荷分类问题的解决方案。该方法在处理大型数据集时具有计算兼容性。最后,对从银行大楼获得的一些主要负载进行了案例研究,以演示现实世界中的基本测试用例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An unsupervised approach in learning load patterns for non-intrusive load monitoring
This paper proposes a new novel way for non-intrusive load monitoring. The technique can be applied to develop a powerful framework for low-cost power monitoring in buildings, particularly in the small commercial and residential sector. The method proposes the construction of a data base of prior knowledge about load patterns and it provides a powerful platform which has the capacity to solve one of the major challenges in power monitoring and energy management, which has been the development of robust unsupervised learning algorithms that eliminate the need for costly human involvement. To do so, a proposal is made on the basis of forming Bayesian networks for the load classification problem. The method has shown to be computationally compatible with handling a large data set. Finally, a case is studied for some major loads obtained from a bank building to demonstrate a basic test case in the real world.
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