基于RFAM-TCN模型和多模态特征优选的非侵入式负荷分解

邬峥涛; 李子晨; 夏杨红; 倪旖旎

doi:10.19912/j.0254-0096.tynxb.2025-0180

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (6) : 109-119. DOI: 10.19912/j.0254-0096.tynxb.2025-0180

基于RFAM-TCN模型和多模态特征优选的非侵入式负荷分解

邬峥涛¹, 李子晨², 夏杨红², 倪旖旎²

作者信息 +

NON-INTRUSIVE LOAD DECOMPOSITION APPROACH BASED ON RFAM-TCN MODEL AND MULTI-MODAL FEATURE OPTIMIZATION

Wu Zhengtao¹, Li Zichen², Xia Yanghong², Ni Yini²

Author information +

文章历史 +

摘要

为实现准确的负荷感知和高效的设备管控,该研究结合多模态特征优选方法与改进时间卷积网络提出一种新型的非侵入式负荷分解（NILD）方法以提高辨识精度与效率,该方法利用负荷多尺度最大重叠离散小波变换结果作为输入特征并结合最大相关性与最小冗余度（mRMR）算法进行特征筛选以确保多模态特征的有效整合。同时在时域卷积网络（TCN）模型中加入感受野注意力机制（RFAM）以提高不同时间尺度特征的提取能力。最后,通过PLAID等数据集等进行算法验证,结果表明所提方法能在使用较少特征的前提下实现更为精确高效的负荷分解,高于传统BiLSTM等模型5.31%的回归准确度,对光伏等新能源户用设备的检出准确率达到96.6%以上。

Abstract

To achieve accurate load awareness and efficient device control, this study proposes a novel non-intrusive load decomposition （NILD） method by combining multi-modal feature selection techniques with an improved time convolutional network to enhance identification accuracy and efficiency. In this method, the load multi-scale maximum overlapping discrete wavelet transform results are introduced as input features, and the feature is combined with the mRMR algorithm to ensure the effective integration of multi-modal features. At the same time, the adaptive receptive field attention mechanism （RFAM） is added to the TCN method,improving the ability to extract features at different time scales. Finally, the algorithm is validated on datasets such as PLAID. The results show that the proposed method can achieve more accurate and efficient load decomposition with fewer features,which is higher than the regression accuracy of the traditional BiLSTM model of 5.31%,and the detection rate of new energy household equipment such as photovoltaic power has also reached over 96.6%.

导出引用

邬峥涛, 李子晨, 夏杨红, 倪旖旎. 基于RFAM-TCN模型和多模态特征优选的非侵入式负荷分解[J]. 太阳能学报. 2026, 47(6): 109-119 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0180

Wu Zhengtao, Li Zichen, Xia Yanghong, Ni Yini. NON-INTRUSIVE LOAD DECOMPOSITION APPROACH BASED ON RFAM-TCN MODEL AND MULTI-MODAL FEATURE OPTIMIZATION[J]. Acta Energiae Solaris Sinica. 2026, 47(6): 109-119 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0180

中图分类号： TM732

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