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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基于RFAM-TCN模型和多模态特征优选的非侵入式负荷分解

更新时间：2026-07-22

- 基于RFAM-TCN模型和多模态特征优选的非侵入式负荷分解
- Vol. 47, Issue 6, Pages: 109-119(2026)
- 作者机构：
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- DOI：10.19912/j.0254-0096.tynxb.2025-0180
  CLC： TM732
- Online First：08 July 2026，
  
  Published：2026
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邬峥涛, 李子晨, 夏杨红, et al. 基于RFAM-TCN模型和多模态特征优选的非侵入式负荷分解[J]. 2026, 47(6): 109-119.
DOI：

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

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%.

关键词

Keywords

references

程祥, 李林芝, 吴浩, 等. 非侵入式负荷监测与分解研究综述[J]. 电网技术, 2016, 40(10): 3108-3117.

李利娟, 刘海, 刘红良, 等. 融合外部注意力机制的序列到点非侵入式负荷分解[J]. 上海交通大学学报, 2024, 58(6): 846-854.

ZHANG Y M, YANG G H, MA S D.Non-intrusive load monitoring based on convolutional neural network with differential input[J]. Procedia cirp, 2019, 83: 670-674.

林顺富, 詹银枫, 李毅, 等. 基于CNN-BiLSTM与DTW的非侵入式住宅负荷监测方法[J]. 电网技术, 2022, 46(5): 1973-1981.

ZHANG Y S, QIAN W Y, YE Y J, et al.A novel non-intrusive load monitoring method based on ResNet-seq2seq networks for energy disaggregation of distributed energy resources integrated with residential houses[J]. Applied energy, 2023, 349: 121703.

YIN L F, GE W.Mobileception-ResNet for transient stability prediction of novel power systems[J]. Energy, 2024, 309: 133163.

ZHOU X X, FENG J R, LI Y.Non-intrusive load decomposition based on CNN-LSTM hybrid deep learning model[J]. Energy reports, 2021, 7: 5762-5771.

HWANG H, KANG S.Nonintrusive load monitoring using an LSTM with feedback structure[J]. IEEE transactions on instrumentation and measurement, 2022, 71: 2509111.

周亚军. 面向单体非侵入式负荷监测的神经网络数据特征分析[D]. 北京: 华北电力大学, 2023.

SIROJAN T, PHUNG B T, AMBIKAIRAJAH E.Deep neural network based energy disaggregation[C]//2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE). Oshawa, ON, Canada, 2018: 73-77.

莫浩杰, 彭勇刚, 蔡田田, 等. 一种基于时频特征融合和极限学习机的非侵入式负荷识别方法[J]. 电工电能新技术, 2023, 42(3): 85-96.

邓旭晖, 陈中, 杨凯, 等. 基于多任务学习卷积网络的非侵入式负荷监测方法[J]. 电力系统自动化, 2023, 47(8): 189-197.

刘仲民, 侯坤福, 高敬更, 等. 基于时间卷积神经网络的非侵入式居民用电负荷分解方法[J]. 电力建设, 2021, 42(3): 97-106.

李想, 刘宇航, 张琪, 等. 小波检测和特征图谱决策的非侵入电动自行车充电实时监测系统[J]. 电力系统自动化, 2023, 47(19): 177-186.

REN Y H, ZHANG S, WANG J, et al.TSC-MI: a temporal spatial convolution neural network fused with mutual information for motor imagery based EEG classification[C]//Brain Informatics. Cham: Springer, 2021: 474-485.

王磊, 马佳琪, 韩肖清, 等. 考虑多状态特征的非侵入式负荷识别方法[J]. 电网技术, 2024, 48(11): 4720-4728.

NI Y N, XIA Y H, LI Z C, et al.A non-intrusive identification approach for residential photovoltaic systems using transient features and TCN with attention mechanisms[J]. Sustainability, 2023, 15(20): 14865.

马乐乐, 孔小兵, 郭磊, 等. 基于最大重叠离散小波变换和深度学习的光伏功率预测[J]. 太阳能学报, 2024, 45(5): 576-583.

KONONENKO I.Estimating attributes: analysis and extensions of RELIEF[C]//Machine Learning: ECML-94. Berlin, Heidelberg: Springer, 1994: 171-182.

温廷新, 郭晓赛. 基于特征组合的ECA-TCN光伏发电功率预测模型[J]. 太阳能学报, 2024, 45(12): 94-100.

LIANG J, NG S K K, KENDALL G, et al. Load signature study: part I: basic concept, structure, and methodology[J]. IEEE transactions on power delivery, 2010, 25(2): 551-560.

MONTEMURRO A, ISAKSEN J L, HANSEN T, et al.Temporal convolutional networks for automatic ECG features extraction[J]. Journal of electrocardiology, 2019, 57: S106-S107.

时培明, 郭轩宇, 杜清灿, 等. 基于TCN-BiLSTM-Attention-ESN的光伏功率预测[J]. 太阳能学报, 2024, 45(9): 304-316.

刘云翔, 马海力, 朱建林, 等. 基于感受野注意力卷积的自动驾驶多任务感知算法[J]. 计算机工程与应用, 2024, 60(20): 133-141.

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