基于非侵入式负荷分解的有色金属冶炼工序识别

为进一步简化数据处理过程和提高生产工序识别准确率，提出一种基于非侵入式负荷分解的工序识别方法。首先将每种工序定义为一种用电设备，然后根据非侵入式负荷分解相关理论，分别选取双向长短期记忆网络和时间卷积网络构建负荷分解模型，选择各用电设备对应功率、总功率数据构造数据集对模型进行训练和测试，最后对测试集负荷分解结果进行相关处理得到对应的工序数据。结果表明由基于时间卷积网络的负荷分解方法构成的工序识别模型具有较高的识别准确率，针对测试集的工序识别准确率达98.83%。

Process identification of non-ferrous metal smelting based on non-invasive load decomposition

In order to further simplify the data processing process and improve the accuracy of production process identification, a process identification method based on non-invasive load decomposition was proposed. Firstly, each process was defined as a kind of electrical equipment. Then, according to the relevant theories of non-invasive load decomposition, bidirectional long short-term memory network and temporal convolution network were selected to construct the load decomposition model, and the corresponding power and total power data of each electrical equipment were selected to construct the data set for training and testing the model. Finally, the corresponding process data was obtained by relevant processing of the load decomposition results of the test set. The results show that the process identification model constructed by the load decomposition method based on the temporal convolution network has high recognition accuracy, and the process identification accuracy for the test set is 98.83%.