Construction of a standardized model for predicting line losses in distribution networks

Due to the excessive reliance on the accuracy and completeness of input data in existing distribution network line loss (DNLL) prediction models, the reliability of DNLL prediction results is poor. This article innovatively proposes a BPNN line loss (LL) prediction method based on adaptive genetic algorithm (AGA) optimization. Firstly, based on the PCA algorithm, five main influencing indicators of DNLL were selected, including electricity theft and leakage loss, meter error loss, peak electricity load, power supply, and sales volume. Secondly, the DNLL prediction data are preprocessed from two aspects: standardizing the operating parameters of the distribution network (DN) and normalizing the LL data. Then, based on the BP neural network (BPNN) and AGA, a standardized model for predicting LL in DN is constructed. Finally, by analyzing the standardized prediction results of DNLL, it is verified that the standardized model for DNLL prediction built in this paper can effectively improve the reliability of DNLL prediction results.