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Transmission line loss is an inherent phenomenon in the transmission and transformation stage, and it is an important index and benchmark for evaluating the daily line loss rate in the low-voltage transformer area. When training on large data samples, the number of regions is usually very large, and the line loss rate data set contains a large number of outliers. In order to accurately calculate the daily line loss rate of low-voltage transformers, a robust neural network (RNN) method with a denoising autoencoder (DAE) multipath network model is proposed. It uses the packet loss layer, L2 regularity, and Huber loss. The advantage of the function is to obtain a variety of different outputs, and use the comparison results to calculate the reference value and a reasonable interval, to achieve an accurate evaluation of the quality of the sampled data set and eliminate abnormal values of the line loss rate, thereby improving the stability of data detection. Compared with traditional machine learning models, the proposed RNN has better robustness and accuracy. According to the final result of the proposed RNN, there are about 13% outliers in the entire data point, and the area with no missing values and outliers in the line loss rate within one month only accounts for about 20%, indicating that the reliability of the metering equipment is low .