肖潇, 栾文鹏, 刘博, 王岩, 杨劲男, 刘子帅, 韦尊. 基于电器粗糙归类的无监督NILM结果自主标注[J]. 中国电机工程学报, 2022, 42(7): 2462-2473. DOI: 10.13334/j.0258-8013.pcsee.210242
引用本文: 肖潇, 栾文鹏, 刘博, 王岩, 杨劲男, 刘子帅, 韦尊. 基于电器粗糙归类的无监督NILM结果自主标注[J]. 中国电机工程学报, 2022, 42(7): 2462-2473. DOI: 10.13334/j.0258-8013.pcsee.210242
XIAO Xiao, LUAN Wenpeng, LIU Bo, WANG Yan, YANG Jinnan, LIU Zishuai, WEI Zun. Autonomous Labeling of Unsupervised NILM Results Based on Rough Classification of Appliances[J]. Proceedings of the CSEE, 2022, 42(7): 2462-2473. DOI: 10.13334/j.0258-8013.pcsee.210242
Citation: XIAO Xiao, LUAN Wenpeng, LIU Bo, WANG Yan, YANG Jinnan, LIU Zishuai, WEI Zun. Autonomous Labeling of Unsupervised NILM Results Based on Rough Classification of Appliances[J]. Proceedings of the CSEE, 2022, 42(7): 2462-2473. DOI: 10.13334/j.0258-8013.pcsee.210242

基于电器粗糙归类的无监督NILM结果自主标注

Autonomous Labeling of Unsupervised NILM Results Based on Rough Classification of Appliances

  • 摘要: 无监督非侵入式负荷监测(non-intrusive load monitoring,NILM)方法通常无法自动确定分解结果所对应的电器名称,这影响NILM结果的用户可读性,阻碍了其规模应用。为此,该文提出一种基于电器粗糙归类的无监督NILM结果自主标注方法。从电器运行控制方式和使用时间分布2个方面,总结分析同类电器共同具有的通用运行特性。定义周期运行、密集波动、固定时长运行3种控制规律特性,给出基于聚类分析的电器相应特性自适应判别方法;对于受人类活动影响程度不同的不同电器,提出基于电器使用与人类活动强弱的时间分布之间相关性的电器使用规律特性判别方法,同时给出一种基于负荷成分变化的用户个性化人类活动强弱时段划分方法。在此基础上,基于粗糙集理论,依据上述2类通用运行特性进行电器粗糙归类,进而提出融合通用运行特性的电器名称两层决策方法,实现NILM结果标注。在私有和公开数据集中的实验表明,该方法能在不同场景下实现常见家用电器NILM结果准确标注。所提方法可作为任意无监督NILM方法的后续步骤与之集成,形成完全无监督NILM方案。

     

    Abstract: The unsupervised non-intrusive load monitoring (NILM) method can realize load decomposition by analyzing the unlabeled aggregated load data directly. However, the corresponding appliance name cannot be automatically determined generally, which affects the interpretability of NILM results and hinders its scale application. In this paper, a novel method for autonomous labeling of unsupervised NILM results based on rough classification of appliances was proposed. Both operation characteristics and usage time distribution of appliances were analyzed and summarized. Three categories of operation characteristics as periodic operation, intensive fluctuation, and fixed-duration operation were defined separately. Correspondingly, an adaptive operation characteristic inference method based on cluster analysis of appliance operation data was provided. A method for identifying the appliance usage characteristics based on the correlation between the usage of appliances and the human activities in term of time distribution was proposed. At the same time, an index to mark the periods of strong/weak human activities was given based on the change of the load component. Furthermore, the appliances were roughly classified according to the defined operation and usage characteristics based on the rough set theory, and then a bilevel decision method was proposed to realize the NILM result labeling. Experiments in private and public datasets show that the proposed method could label NILM results of common appliances in different scenarios accurately. The proposed method can be integrated with other unsupervised NILM algorithm to form a fully unsupervised NILM solution.

     

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