###### Abstract  
In order to defraud state subsidies, some unscrupulous users use improper means to steal photovoltaic (PV) power. This behavior brings potential safety hazards to photovoltaic grid-connected operations. In this paper, a photovoltaic power-stealing identification method based on similar-day clustering and interval prediction of the quantile regression model for long short-term memory neural network (QRLSTM) is proposed. First, photovoltaic data are clustered into three similar days by the similar-day clustering according to weather conditions. Second, compared with the quantile regression neural network (QRNN) prediction method, the good prediction performance of the QRLSTM method is illustrated. Third, using the prediction intervals with different confidence levels on three similar days, according to the time scale (short-term, medium-term and long-term) combined with different electricity-stealing judgment indicators, a three-layer photovoltaic power-stealing screening framework is constructed, and the degree of user power stealing is qualitatively analyzed. Last, the power generation data of eight photovoltaic users in a certain region of northwest China and the data of four groups of artificially constructed power-stealing users are used as an example for simulation. The simulation results prove the feasibility of the proposed method in this paper. photovoltaic power-stealing identification; interval prediction; similar-day clustering; quantile regression model for long short-term memory neural networks; three-layer photovoltaic power-stealing screening 20233  
There are four typical distributed photovoltaic power-stealing methods: increasing voltage method, increasing current method, photovoltaic simulator method and the grid reconnection method [6]. Both the increasing voltage method and the increasing current method need to change the external connection of the original line, which can be found through on-site inspection. The photovoltaic simulator method can control the output of the photovoltaic simulator according to the change of the external meteorological environment, which has high concealability and is not easy to identify. Power stealing by the grid reconnection method needs to ensure that the electricity consumed by the user is greater than the electricity emitted by the photovoltaic panels [7]. The existence of power stealing can be judged by analysing the electricity consumption of users. The uncertainty of photovoltaic power generation, the diversity of photovoltaic power-stealing methods and the randomness of photovoltaic power-stealing behavior have brought great challenges to photovoltaic anti-power stealing. The existing photovoltaic power-stealing identification methods can be mainly divided into two categories: one needs to predict the photovoltaic power generation and compares the predicted value with the measured value of the photovoltaic meter [8; 9]; the other does not need to predict the photovoltaic power generation and directly uses the data of the power station [10; 11; 12]. The first method requires the model to have high prediction accuracy. [PERSON] et al. [8] proposed a power-stealing detection method based on the regression tree model, comparing the real value read from the photovoltaic meter with the predicted value obtained from the regression model to judge the power-stealing behavior. But the prediction accuracy of the proposed model is not high, leading to the identification accuracy of power-stealing behavior being not high. The second method analyzes the data of the power station when there is behavior of power stealing and finds those characteristics of the data that can be used to identify photovoltaic power stealing. [PERSON] et al. [13] put forward a new detection index for the virtual increase of photovoltaic power generation, that is, the changed slope between the data of the photovoltaic meter and the gate meter.