基于改进GMM-CNN-GRU混合的非侵入式负荷监测方法研究

杨秀, 李安, 孙改平, 田英杰, 刘方, 潘瑞媛, 吴吉海

杨秀, 李安, 孙改平, 田英杰, 刘方, 潘瑞媛, 吴吉海. 基于改进GMM-CNN-GRU混合的非侵入式负荷监测方法研究[J]. 电力系统保护与控制, 2022, 50(14): 65-75. DOI: 10.19783/j.cnki.pspc.211238
引用本文: 杨秀, 李安, 孙改平, 田英杰, 刘方, 潘瑞媛, 吴吉海. 基于改进GMM-CNN-GRU混合的非侵入式负荷监测方法研究[J]. 电力系统保护与控制, 2022, 50(14): 65-75. DOI: 10.19783/j.cnki.pspc.211238
YANG Xiu, LI An, SUN Gaiping, TIAN Yingjie, LIU Fang, PAN Ruiyuan, WU Jihai. Non-invasive load monitoring based on an improved GMM-CNN-GRU combination[J]. Power System Protection and Control, 2022, 50(14): 65-75. DOI: 10.19783/j.cnki.pspc.211238
Citation: YANG Xiu, LI An, SUN Gaiping, TIAN Yingjie, LIU Fang, PAN Ruiyuan, WU Jihai. Non-invasive load monitoring based on an improved GMM-CNN-GRU combination[J]. Power System Protection and Control, 2022, 50(14): 65-75. DOI: 10.19783/j.cnki.pspc.211238

基于改进GMM-CNN-GRU混合的非侵入式负荷监测方法研究

基金项目: 

国家自然科学基金项目资助(61872230)

上海电力人工智能工程技术研究中心研究项目资助(19D72252800)~~

详细信息
    作者简介:

    杨秀(1972—),男,教授,博士生导师,从事分布式发电与微电网技术;Email:yangxiu721102@126.com李安(1997—),男,硕士研究生,从事非侵入式负荷监测与分解;E-mail:spiderla97@163.com孙改平(1984—),女,讲师,从事电力系统优化调度。E-mail:sunfrog2002@163.com

  • 中图分类号: TM714;TP183

Non-invasive load monitoring based on an improved GMM-CNN-GRU combination

Funds: 

supported by the National Natural Science Foundation of China (No.61872230)

  • 摘要: 为挖掘用户侧节能减排潜力,对用户用电行为进行精细化分析和管理,提升电能利用效率,提出了一种基于高斯混合模型聚类和深度神经网络相结合的非侵入式负荷监测方法。首先,针对同一电器常出现功率相近但运行状态不一致问题,利用高斯混合模型聚类算法中“软分类”和类簇灵活的优势,对负荷工作状态进行精细分类,形成负荷用电设备实际运行情况的负荷状态特征库。其次,针对常见的应用于非侵入式负荷监测模型的深度神经网络在多标签分类时存在识别精度低等问题,提出卷积神经网络与门控循环单元混合的深度神经网络模型。最后,综合考虑外部环境数据对家庭用户用能习惯的影响,在AMPds2数据集上开展验证分析,并与其他模型进行对比。结果表明,所提的非侵入式负荷监测模型具有较高的准确性。
    Abstract: A non-intrusive load monitoring method based on Gaussian mixture model clustering combined with a deep neural network is proposed to explore the potential of energy saving and emission reduction at the customer side.It also fine-tunes the analysis and management of customer electricity consumption behavior,and improves the efficiency of electricity use.First,we tackle the problem that the same electrical appliance often has similar power but inconsistent operating status.In order to classify the load working status in fine manner,the advantages of"soft classification"and flexible clustering in the Gaussian mixture model clustering algorithm can be used to form a load status feature library that conforms to the actual operating conditions of electrical equipment.Secondly,note that in the common deep neural networks applied to non-invasive load monitoring models,there are problems such as low recognition accuracy in multi-label classification.Thus a deep neural network model with a mixture of convolutional neural networks and gated recurrent units is proposed.Finally,the validation analysis is carried out on the AMPds2 dataset by considering the influence of external environmental data on the energy consumption habits of household users,and the results are compared with other models.The results show that the proposed non-invasive load monitoring model has high accuracy.
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出版历程
  • 收稿日期:  2021-09-07
  • 刊出日期:  2022-07-15

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