###### Abstract
The proposed detection index improves the accuracy of photovoltaic power-stealing identification, but this approach is only applicable to the photovoltaic simulator power-stealing method, not to the other three common power-stealing methods. [PERSON] et al. [14] proposed an identification method for abnormal photovoltaic users based on MIV (mean impact value) and heuristic forward searching. However, this method is limited, has no timeliness, and the results have a certain randomness. In regard to photovoltaic power generation forecasting, there has been much research to improve the accuracy of photovoltaic power generation forecasts. The higher the prediction accuracy, the more accurate the identification of electricity stealing. [PERSON] et al. [15] proposed two MFFNN-based optimization techniques to predict the DC output power of a PV plant. [PERSON] et al. [16] proposed an Ultra-short-term power forecasting method based on NWP similarity analysis. At this stage, for photovoltaic power generation forecasting, point forecasting is mostly used, but the information provided by point forecasting results is limited, which is not conducive to reflecting the randomness of forecasting. Therefore, the interval prediction method has been introduced. Interval prediction is an uncertainty prediction, which can reflect the volatility of photovoltaic power generation and can also obtain the probability distribution characteristics of photovoltaic output. [PERSON] et al. [17] proposed a photovoltaic interval prediction method based on the adaptive-rolling matching-prediction correction mode. [PERSON] et al. [18] established a linear programming-based prediction interval construction model for photovoltaic power generation. In summary, the main method of photovoltaic power-stealing identification is to compare the predicted photovoltaic power generation with the photovoltaic metering meter. However, the main problem of this method is that the prediction accuracy of photovoltaic power generation in the existing research still has room for improvement, so it is necessary to constantly update the algorithm to improve the prediction accuracy. The other category of identification methods which do not rely on photovoltaic prediction also has some problems, such as difficulty in data acquisition, insufficient accuracy and limited scope of application. These problems are considered comprehensively in carrying out the work of this paper. This work aims to study a photovoltaic power-stealing identification method based on similar-day clustering and quantile regression long short-term memory (QRLSTM) neural interval prediction. The following are the primary contributions of this work:  
* The principles of four photovoltaic power-stealing methods and the data characteristics after power stealing were investigated, and four groups of power-stealing user data were artificially constructed to fully verify the effect of the proposed method. * The method of combining similar-day clustering and the QRLSTM network was used to forecast photovoltaic power generation under different types of weather conditions to improve the prediction accuracy. It was considered that the efficiency of photovoltaic power generation in the same weather is similar. * In order to make the identification result of the final power stealing more stable and accurate, three-layer screening criteria were set from the time scale, which were specifically manifested as short-term, medium-term and long-term.