**5. Conclusions**

Considering the accuracy and real-time of NILM during actual operation, this paper studies an effective identification method based on the convolutional neural network. Under the high frequency data acquisition mode, this paper adopts the load separation model to obtain the current and voltage waveforms of independent loads and records the corresponding label information using the

Bayesian classification model. Then, the convolutional neural network model is briefly trained by the two-dimensional load data in the library to form a classification model suitable for each signature library, realizing long-term load identification in real-time.

In this paper, the two-dimensional image load data are used for identification. This type of data can preserve the contour and shape signature of a waveform completely and avoid the complexity of data reconstruction as in signature extraction and classification. Furthermore, the contour and shape signatures extracted by the convolutional neural network reduce the influence of noise or harmonics on the identification results. The measured data are used to verify the algorithm proposed in our work. The method performs better than the other compared algorithms. With the increase in load categories and number of users, the advantages of the proposed algorithm are clear. The overall accuracy is higher than 92% and the operation time is less than 1.25 s. Thus, the proposed method can identify the switching load e ffectively using the convolutional neural network and the obtained corresponding power consumption of each load can be calculated accurately. The whole process provides a complete implementation idea for NILM, which can be automatically executed without intervention.

In future work, the dynamic loads with various transients should be considered. Additionally, the influence of load phase information requires further research, and required the resolution and accuracy for measurement may be another research point.

**Author Contributions:** X.W. and D.J. conceived and designed the experiments; D.J. and Y.D. contributed experimental tools and analyzed the results; X.W. and D.J. wrote and revised the paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by [the Fundamental Research Funds for the Central Universities] gran<sup>t</sup> number [2020MS002].

**Conflicts of Interest:** The authors declare no conflict of interest.
