随着智能电网的迅速发展，为了有效提高电能的使用率，合理规划电能资源，建立全国范围内的智能用电和负荷监测系统，非侵入式负荷监测(NILM)和分解问题一直受到广泛关注。为提高非侵入性负载分解性能，提出一种基于耦合神经网络的非侵入式负荷分解方法。首先，对数据集进行归一化和预处理。其次，构建一种将卷积神经网络(CNN)与双向门控循环单元(BiGRU)相结合的混合深度学习模型，对数据的空间特性和时序特性进行充分挖掘，并加入注意力机制，关注重要信息，剔除冗余特征。最后，采用国内自测数据集进行试验，使用不同的评价指标对该耦合神经网络进行评估，并与其他的常用分解模型进行对比。试验结果表明，所提方法的均值绝对误差与绝对误差和相较于其他分解方法都有所降低，均值绝对误差平均下降了35.9%，绝对误差和平均下降了39.9%。

With the rapid development of smart grid, the problem of nonintrusive load monitoring (NILM) and disaggregation has been widely concerned in order to effectively improve the utilization of power, rationally plan the power resources, and establish the nationwide smart power and load monitoring system. To improve the performance of nonintrusive load disaggregation, a nonintrusive load disaggregation method based on coupled neural network is presented. Firstly, the dataset is normalized and preprocessed. Secondly, a hybrid deep learning model is constructed, which combines convolution neural network (CNN) with bidirectional gated recurrent unit (BiGRU). The spatial and temporal characteristics of data are fully explored, and attention mechanism is added to focus on important information and eliminate redundant features. Finally, the domestic selftest data set is used for the experiment, and the coupled neural network is evaluated with different evaluation indexes, and compared with other commonly used disaggregation models. The experimental results show that the mean absolute error and sum of absolute error of the proposed method are reduced compared with other disaggregation methods, the mean absolute error is reduced by 35.9% and the sum of absolute error is reduced by 39.9% on average.

TM714