基于k-近邻(k-NN)分类器的非侵入式电器负荷监测(NIALM)的实现

Q3 Engineering
Amleset Kelati, Hossam Gaber, J. Plosila, H. Tenhunen
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引用次数: 9

摘要

非侵入式电器负载监测(NIALM)用于通过区分家庭电器的电压和电流变化来分析个人的家庭能耗。该方法从聚合的家庭能量消耗中识别每个电器的负载消耗。NIALM还将通过间接检测电器使用的异常变化来提供每个电器的负载消耗信息。所提出的NIALM方法基于从电力信号的负载消耗测量中提取特征,以便对电器的运行状态进行分类。在这项工作中,我们通过使用机器学习(ML)技术提高了识别精度和基于设备运行状态的设备检测;即k近邻(k-NN)分类算法。用于执行该过程的数据集来自多个家庭电器的功率、电压和电流信号的公共可用数据集(PLAID)。这被用作基准数据集。为每个设备收集和处理PLAID数据集,我们基于k-NN算法的分类结果实现了高精度,并能够获得经济高效的解决方案。此外,与其他提出的不同ML选项相比,k-NN分类器被证明是NIALM技术的一种有效方法。基于所使用的数据集,使用k-NN分类器获得的平均F分数测量为90%。讨论了这些发现背后的可能原因,并提出了进一步探索的领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of non-intrusive appliances load monitoring (NIALM) on k-nearest neighbors (k-NN) classifier
Nonintrusive Appliance Load Monitoring (NIALM) is used to analyze individual’s house energy consumption by distinguishing variations in voltage and current of appliances in a household. The method identifies load consumption of each appliance from the aggregated home energy consumption. NIALM will also provide information of load consumptions of each appliance by indirectly detecting the abnormal changes of appliance usage. The proposed NIALM approach is based on features extraction from load consumptions measurements of electrical power signals in order to classify appliance’s state of operation. In this work, we have improved the identification accuracy and the detection of appliances based on their operational state by employing Machine Learning (ML) technique; namely k-nearest neighbor (k-NN) classification algorithm. The dataset used to perform this process is from the publicly available (PLAID) of power, voltage and current signals of appliances from several houses. This is used as benchmark data set. The PLAID dataset is collected and processed for each appliance and our classification results based on k-NN algorithm achieved high accuracy and is able to gain cost-effective solution. In addition, the result shows that k-NN classifier is a proven as an efficient method for NIALM techniques when compared with other proposed different ML options. Based on the used dataset, the average F-score measure obtained using the k-NN classifier is 90%. Possible reasons behind these findings are discussed and areas for further exploration are proposed.
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来源期刊
AIMS Electronics and Electrical Engineering
AIMS Electronics and Electrical Engineering Engineering-Control and Systems Engineering
CiteScore
2.40
自引率
0.00%
发文量
19
审稿时长
8 weeks
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