A non-intrusive load monitoring system using multi-label classification approach
A non-intrusive load monitoring system using multi-label classification approach
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DOI：
10.1016/j.scs.2018.02.002
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发表时间：
2018-05-01
影响因子：
11.7
通讯作者：
Rakkwamsuk, Pattana
中科院分区：
文献类型：
--
作者：
Buddhahai, Bundit;Wongseree, Waranyu;Rakkwamsuk, Pattana
本文提出了一种应用多标签分类进行能量分解的实验设计过程。分类方法最近被证明是处理数据分解的一种合适的代表性模型，其中主要任务是从聚集数据中识别或预测多个负荷使用情况。实验是通过收集房屋中负荷分配的各个电路支路的基本电气参数来进行的。通过机器学习过程对数据集进行分析，选择最优的参数集、学习算法和模型参数，使学习过程产生的系统能够提供最优的家电负荷预测性能。通过获取每1分钟的电流(I)、有功功率(P)、无功(Q)和功率因数(PF)等电气参数，采用带有决策树的Rakel(随机k标签集)作为多标签分类算法，并进行正确的模型参数配置。F-Score和预测准确率被评估为预测性能，对于大功率电器(热水器、空调)，分别为97%和99%；对于照明，分别为59%和93%；对于插头插座公用事业，分别为75%和92%。
This paper proposes an experimental design process for the application of energy disaggregation using multi-label classification. The classification approach has recently shown to be a suitable representative model for treating data disaggregation in which the primary task is to identify or predict multiple load usage from aggregate data. The experiments were conducted by collecting basic electrical parameters from individual circuit branches of the load distribution in a house. Sets of data were analyzed through machine learning process to select the optimal set of parameters, learning algorithm and model parameter so that the system resulted from the learning process could deliver the optimal predictive performance for appliance loads. By taking the electrical parameters of current (I), real power (P), reactive power (Q), and power factor (PF) at every one-minute and employing RAkEL (RAndom k-labELsets) with Decision Tree as the multi-label classification algorithm together with the right model parameter configuration. F-score and prediction accuracy were evaluated as the predictive performance which found to be 97% and 99%, respectively, for high power appliances (water heater, Air-conditioner); 59% and 93%, respectively for lightings; finally, 75% and 92%, respectively for plug-outlet utilities.