*5.2. Parameter Study*

To study the effect of the length of the intercepted window *T* on the proposed model, we conducted an experiment on dataset (a) and dataset (b) by changing the value of *T* from 10 to 500 with a step size of 10. Figure 8a,b shows the experiment results of dataset (a) and dataset (b), respectively.

**Figure 8.** Performances of different *T*. (**a**) Trends of indicators in dataset (a); (**b**) trends of indicators in dataset (b).

The length of the intercepted window has an important impact on the performance, as shown in Figure 8. Four indicators all increased with the increasing of *T* at first. Then they no longer increased and began to fluctuate when *T* exceeded a certain value and continued to increase. In Figure 8a, PR, RR and F1 achieved their maxima when *T* was about 270. In Figure 8b, four indicators achieved their maxima when *T* was about 200. This is mainly because that the features of electricity theft data are easier to be extracted with more electricity consumption information when *T* increases, which leads to the improvement of the performance.

Therefore, to achieve the best performance of the proposed model, it is necessary to investigate an appropriate length of the intercepted window *T* for electricity theft detection.

#### *5.3. Data Augmentation Analysis*

The proposed data augmentation method was used to augmen<sup>t</sup> the electricity theft data in dataset (b). To study the effectiveness of the proposed data augmentation method, we varied the value of *AG*, which represents the repeated times, from 0 to 20 with a step of 1. At the same time, other parameters were fixed.

The comparison results of different *AG* in training ratios of 50% and 80% are given in Figure 9a,b respectively. The four indicators all increased at first as *AG* increased, while the classification accuracy decreased after *AG* exceeded a value. The indicators had a positive relationship with *AG* in the early stage because the increase in the amount of electricity theft data during the training was of grea<sup>t</sup> help to the classification accuracy, which can effectively increase the number of TP (true positives) in the classification result. Therefore, all four indicators had an upward trend.

**Figure 9.** Performances of different *AG*. (**a**) Trends of indicators with a 50% training ratio; (**b**) trends of indicators with an 80% training ratio.

When *AG* continued to increase, AR, RR and F1 dropped, while PR fluctuated. The main reason is that the normal data were labeled as electricity theft during the data preprocessing when the repeated time was too large, so the training model tended to classify the normal users into abnormal users. As a result, the numbers of FP (false positives) and FN (false negatives) increased, while the numbers of TP and TN (true negative) declined in the classification result. Therefore, the classification accuracy dropped and most indicators decreased, especially the most important indicator F1.

All in all, the data augmentation which increases the number of electricity theft data points for CNN training can improve the classification accuracy e ffectively. It is also important to choose an appropriate *AG* to achieve better classification results, because the indicators for accuracy may fluctuate with inappropriate *AG*.
