Single Appliance Recognition Using Statistical Features Based k-NN Classification

机译：基于统计特征的k-NN分类的单设备识别

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Recognizing the appliance according to the flowed electric current through it is quite a meaningful work which can help the electric management system to make effective policy of energy conservation. We designed an algorithm based on an improved k-nearest neighbor which can classify the unlabelled appliances' running power data into its most similar data clusters. In other words, this algorithm is able to recognize the appliance only according to its running power data series. The classification is based upon the multifarious features extracted from the time series data sensed from the running appliance with the power metering sensors. Appliance recognition is performed with a mean accuracy over 90% in five-class classification problem.

机译：根据流过的电流识别电器是一项有意义的工作，可以帮助电气管理系统制定有效的节能策略。我们设计了一种基于改进的k最近邻算法的算法，该算法可以将未标记设备的运行功率数据分类为其最相似的数据集群。换句话说，该算法只能根据设备的运行功率数据序列来识别设备。该分类基于从具有功率计量传感器的运行设备中感测到的时间序列数据中提取的多种功能。在五类分类问题中，设备识别的平均准确率超过90％。

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《International Conference on Cloud Computing and Security》|2017年|631-640|共10页
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作者
Qi Liu; Hao Wu; Xiaodong Liu; Nigel Linge;
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Appliance recognition; Feature extraction; k-NN;

机译：设备识别;特征提取;神经网络;

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Single Appliance Recognition Using Statistical Features Based k-NN Classification

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