一种基于EDDL的非侵入式负荷检测模型
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浙江华云信息科技有限公司,浙江 杭州 310030

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李晨(1990-),女,学士,工程师,主要研究方向为信息技术.email:lichen_199003@163.com.
夏立典(1992-),男,学士,主要研究方向为信息技术.
章超(1990-),男,学士,主要研究方向为信息技术.
叶杨锋(1991-),男,学士,主要研究方向为信息技术.

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A non-intrusive load detection model based on EDDL
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Zhejiang Huayun Information Technology Co.,LTD.,Hangzhou Zhejiang 310030,China

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    摘要:

    针对目前非侵入式负荷检测时存在检测精确度低的问题,提出一种基于事件驱动-深度学习(EDDL)的负荷检测模型。通过零交叉检测电流数据,基于事件驱动机制从大量数据中发现关键事件;将包含关键事件的电流序列转换至图像空间,并代入基于深度学习的负荷检测模型,从而实现端对端的非侵入式负荷检测。实验结果表明,与多分类支持向量机(MSVM)、前馈神经网络(FNN)、卷积神经网络(CNN)和长短时记忆网络(LSTM)模型相比,所提EDDL模型综合性能更优,检测准确率和精确度分别为94.67%和91.76%。仿真结果验证了所提模型可基于事件驱动机制挖掘电流数据,并基于深度学习模型有效提取电流数据特征,从而实现高精确度的非侵入式电力负荷检测。该模型对非侵入式电力负荷检测研究具有一定借鉴作用。

    Abstract:

    A load detection model based on Event Driven and Deep Learning(EDDL) is proposed to address the issue of low detection accuracy in current non-invasive load detection. The current data is detected through zero crossing, and the key events are discovered from a large amount of data based on event driven mechanisms. The end-to-end non-invasive load detection is achieved by converting the current sequence containing key events into image space and incorporating it into a deep learning based load detection model. The experimental results show that compared with the Multi-class Support Vector Machine(MSVM), Feedforward Neural Network(FNN), Convolution Neural Network(CNN), and Long Short Term Memory (LSTM) models, the proposed EDDL model has better overall performance, with detection accuracy and accuracy of 94.67% and 91.76%, respectively. The simulation results verify that the proposed model can mine current data based on event driven mechanisms and effectively extract current data features based on deep learning models, thus achieving high-precision non-invasive power load detection. This model has certain reference value for the research of non-invasive power load detection.

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李晨,夏立典,章超,叶杨锋.一种基于EDDL的非侵入式负荷检测模型[J].太赫兹科学与电子信息学报,2023,(11):1381~1386

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  • 收稿日期:2023-06-30
  • 最后修改日期:2023-07-21
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  • 在线发布日期: 2023-11-28
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