基于上采样金字塔结构的卷积神经网络的非侵入负荷辨识算法

非侵入式负荷监测（NILM）技术为需求侧管理提供了技术支撑，而非侵入负荷辨识是负荷监测过程中的关键环节。在负荷数据采样过程中无法实现长期的实时高频采集，得到的负荷数据还存在缺乏时序性的问题；同时，卷积神经网络（CNN）存在对低级信号特征表现不足的缺陷。针对以上两个问题，提出了一种基于上采样金字塔结构的CNN非侵入负荷辨识算法。所提算法直接面向采集到的负荷电流信号，利用上采样网络扩展数据在时间维度上的相关信息弥补数据的时序性，并通过双向金字塔一维卷积提取负荷信号的高级与低级特征，以对负荷特征进行全面利用，从而实现对未知负荷信号进行识别的目的。实验结果表明，基于上采样金字塔结构的CNN非侵入负荷辨识算法的识别准确率能够达到95.21%，且具有良好的泛化能力，可有效实现负荷辨识。

Non-intrusive load identification algorithm based on convolutional neural network with upsampling pyramid structure

Non-Intrusive Load Monitoring （NILM） technology provides technical support for demand side management， and non-intrusive load identification is the key link in the process of load monitoring. The long-term sampling process of load data cannot be carried out in real time and high frequency， and the time sequence of the obtained load data is lost. At the same time， the defect of insufficient representation of low-level signal features occurs in Convolution Neural Network （CNN）. In view of the above two problems， a CNN based non-intrusive load identification algorithm with upsampling pyramid structure was proposed. In the proposed algorithm， with direct orientation to the collected load current signals， the time sequence of the data was compensated by the relevant information in the time dimension of the upsampling network expanded data， and the high-level and low-level features of load signals were extracted by the bidirectional pyramid one-dimensional convolution， so that the load characteristics were fully utilized. As a result， the purpose of identifying unknown load signals can be achieved. Experimental results show that the recognition accuracy of non-intrusive load identification algorithm based on CNN with upsampling pyramid structure can reach 95.21%， indicating that the proposed algorithm has a good generalization ability， and can effectively realize load identification.