【摘要】 非侵入式负荷监测作为家庭用电精细化管理的重要手段，对推进节能减排、实现“双碳”目标具有积极作用。然而，利用原始电压-电流轨迹图像特征很难实现高精度负荷识别。因此，提出了一种基于格拉姆角差场（GADF）图像编码的非侵入式负荷识别方法。首先，对设备采集到的高频稳态数据进行预处理，获得一个完整基波周期电流和电压信号。然后，利用GADF分别对一维电压和电流信号进行图像编码，生成相对应的二维图像。最后，通过叠加融合输入到卷积块注意力模型中完成负荷识别。为了验证所提方法的有效性，利用公共数据集PLAID和WHITED进行实验分析。结果表明，该方法具有很高的识别精度，PLAID数据集平均识别准确率达到99.45%,WHITED数据集平均识别准确率达到99.24%。更多还原

【Abstract】 Non-intrusive load monitoring, as an essential means for fine-grained management of household electricity consumption, plays a significant role in promoting energy conservation and emission reduction for achieving the dualcarbon goal. However, it is challenging to achieve high-precision load identification using a single voltage-current trajectory image. Therefore, a non-intrusive load identification method based on the fusion of Gramian angular difference field(GADF) image coding is proposed. First, the high-frequency steady-state data collected by the device are preprocessed to obtain a complete base-wave period current and voltage signal. Then, the one-dimensional voltage and current signals are encoded separately using the GADF to generate the corresponding two-dimensional feature images, and load identification is performed via superimposed fusion input to a neural network based on a convolutional block attention module. The public datasets PLAID and WHITED are used for testing experiments to verify the effectiveness of the proposed method. The results indicate that the method has a high recognition accuracy, with average accuracies of 99. 45% and 99. 24% for the PLAID and WHITED datasets, respectively.更多还原