Abstract
Accurate short‐term prediction of line loss rate is of great significance for improving the operational efficiency and economic performance of distribution networks. To address the limitations of conventional prediction models in nonlinear feature extraction and hyperparameter optimization, this paper proposes a CNN–LSTM prediction model optimized by an improved sparrow search algorithm incorporating logistic chaotic mapping and a linearly decreasing weight strategy, referred to as the LCSSA–CNN–LSTM model. First, a convolutional neural network is employed to extract local features from multidimensional input variables, and a long short‐term memory network is then used to capture temporal dependencies in the line loss rate series. Second, logistic chaotic mapping is introduced to improve the initialization quality of the sparrow population, while a linearly decreasing weight is incorporated to enhance the balance between global exploration and local exploitation during iterative optimization. Finally, comparative experiments are conducted on a real line loss rate dataset. The results show that the proposed model significantly outperforms the baseline CNN–LSTM model, with the RMSE reduced by 55.79% and the prediction error of representative peak samples controlled within 3%. These results indicate that the proposed model can effectively improve the accuracy and robustness of short‐term line loss rate prediction.