## 3 Results and Discussion
### Simulation of PV Power-Stealing Identification Based on Interval Prediction  
In Section 3.2, the good prediction performance of the QRLSTM interval prediction model was introduced. In this section, the trained QRLSTM model will be used for interval prediction, and then power-stealing identification will be completed based on the interval prediction results. User 1 was selected to be the benchmark user. The benchmark user must ensure the accuracy of its power generation. If necessary, appropriate on-site inspection can be carried out on the benchmark user. Only after it is confirmed that it meets the requirements of power generation operation can it be established as the benchmark user. The meteorological data of the benchmark user is input into the trained QRLSTM model for prediction to obtain the prediction interval. The prediction interval and the power generation data of the users to be identified are input variables in the photovoltaic power-stealing identification process. According to the judgment standard and power-stealing identification process formulated in Section 2.3, four constructed power-stealing users in turn are identified, and the identification results are shown in Table 4. It can be seen from Table 4 that all the constructed users can be effectively identified. The constructed users 1 and 2 are identified in the first layer of screening, and the basis of identification is that 6 moments in one day and 3 moments at 6:00-8:00 and 16:00-18:00 exceed the prediction interval. The constructed users 3 and 4 have a more concealed way of power stealing and enter the second layer screening, but still can be identified, with the \(e_{NAD}\) of 22.6% and 27.1%. The judgment results are in line with the actual situation, which can confirm the effectiveness of the method proposed in this paper. By comparing the actual output value of other users with the falling situation of the predicted interval, and then checking one by one according to the judgment indicators of the three-layer screening, the final results are shown in Table 5. According to the monthly total suspicion days, the degree of the user's power-stealing can be identified. Users 2-7 are not suspected of power stealing, and user 8 is suspected of mild power stealing. Constructed users 1 and 2 are suspected of major power stealing, while constructed users 3 and 4 are suspected of moderate power stealing. This method effectively completes the identification and analysis of power stealing by photovoltaic users. The recognition results of the proposed method are compared with those of the method proposed in Reference [14], as shown in Table 6. Both of them can effectively identify the power-stealing user, but the method proposed in this paper is more systematic and comprehensive, and it can qualitatively analyze the severity of power stealing. For PV User 7 and User 8, they are not screened in the method of Reference [14], but the final judgment result has not been determined, which needs to be combined with the results of the next cycle. As can be seen from Table 5, user 7 is not suspected of stealing power, which reflects that the MIV-heuristic forward search method has some errors. User 8 is indeed a mild power stealer, and the method in this paper can e effectively identified it within one cycle, which is more timely than the method in Reference [14]. Moreover, the three-layer screening structure can be used for more comprehensive identification, including short- and medium-term identification. Although the method proposed in this paper can identify photovoltaic power-stealing users more accurately and comprehensively, there are still some limitations in this paper. In the subsequent research, the identification accuracy of power stealing can be improved by further optimization from the perspectives of identifying indicators of power stealing and improving the accuracy of the photovoltaic output prediction model.