基于CNN与K-means聚类的非侵入式电器负荷识别方法
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河北省重点研发计划项目(20311801D); 河北省高层次人才资助项目(A201905008)


Non-intrusive electrical appliance load identification method based on CNN and K-means clustering
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    摘要:

    针对目前非侵入式负荷监测仅能识别单个家用电器、多种家用电器同时运行识别率低的问题,提出一种基于卷积神经网络(CNN)与K-means聚类结合的非侵入式家用电器识别方法。首先,通过改进的CUSUM边沿检测算法对获取的用户用电数据进行时间检测,提取负荷发生投切事件的功率波形;其次,通过高斯滤波法对提取的功率波形进行滤波处理,并将处理后的波形转化为像素图作为负荷特征库,一部分作为训练集用来训练K-means算法改进后的CNN模型,一部分作为测试集测试模型识别的精度;最后,利用搭建的实验平台进行实际测试分析。实验结果表明,所用模型对7种家用电器的识别率均为100%,验证了模型的有效性。通过K-means算法对卷积神经网络进行改进,增大相似特性负荷特征之间的区别,提高负荷辨识的准确率,为非侵入式负荷检测技术开发提供了参考。

    Abstract:

    Aiming at the problem that the current non-invasive load monitoring can only identify a single household appliance,and the recognition rate of multiple household appliances running at the same time is low,a non-invasive household appliance identification method based on CNN and K-means clustering was proposed.Firstly,the improved CUSUM edge detection algorithm was used to detect the time of the obtained user power data and extract the power waveform of the load switching event.Secondly,the extracted power waveform was filtered by Gaussian filtering method,and the processed waveform was transformed into pixel image as the load feature library.One part was used as the training set to train the CNN model improved by K-means algorithm,and the other part was used as the test set to test the model recognition accuracy;Finally,the experimental platform was used for actual test and analysis.The experimental results show that the recognition rate of the model for seven kinds of household appliances is 100%,which verifies the effectiveness of the model.The convolutional neural network is improved by K-means algorithm to increase the difference between similar load characteristics and improve the accuracy of load identification,which provides a reference for the development of non-invasive load detection technology.

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李 争,王 泽,冯 威,安国庆,王 强,陈 贺.基于CNN与K-means聚类的非侵入式电器负荷识别方法[J].河北科技大学学报,2022,42(4):365-373

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  • 收稿日期:2021-11-16
  • 最后修改日期:2022-03-04
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  • 在线发布日期: 2022-09-09
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