The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one dimensional in nature, suffer from poor aggregate and load signal feature localization necessitating a larger training dataset spanning very long time periods and normally require signal formatting and pre-processing. We use Gramian angular summation fields to transform the power series into a reduced image dataset that contains a rich set of localized signal features. A computer vision approach allows us to capture as much information as possible, and then propose an image-based mains load recognition system with high performance. In this paper for the entire recognition system, we use convolutional neural networks that very well adapted to vision recognition. The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network. To test the proposed system, some simulations and comparisons are carried out and the results show that our easier to handle method can achieve acceptable performance.

市电信号是建筑物中各种电气设备负载信号的复杂融合。在非侵入式负载监控识别中，我们的主要目标是能够通过基于计算机视觉的方法（与幂级数信号方法相比），以更简单的方式从复杂的总干线信号中提取尽可能多的负载特征。幂级数方法本质上是一维的，其聚集和负载信号特征定位很差，因此需要跨越很长一段时间的较大训练数据集，并且通常需要信号格式化和预处理。我们使用Gramian角求和字段将幂级数转换为包含丰富的局部信号特征集的简化图像数据集。计算机视觉方法使我们能够捕获尽可能多的信息，然后提出了一种基于图像的高性能电网负荷识别系统。在本文中，对于整个识别系统，我们使用非常适合视觉识别的卷积神经网络。负载信号图像分解是通过功能强大的堆叠降噪自动编码器噪声提取网络实现的。为了测试提出的系统，进行了一些仿真和比较，结果表明我们易于处理的方法可以达到可接受的性能。