MI H R, SHEN S L, WEI Z R,et al.An improved sparrow search algorithm （SSA）-optimized CNN-BiLSTM hybrid model for short-term electric load forecasting[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(5):107-115.

doi:10.16186/j.cnki.1673-9787.2025120085

Received:2025/12/26

Revised:2026/04/28

Published:2026-07-24

An improved sparrow search algorithm （SSA）-optimized CNN-BiLSTM hybrid model for short-term electric load forecasting

Mi Haoran, Shen Senlin, Wei Ziren, Li Wei, Li Yalei

Jiaozuo Power Supply Company， State Grid Henan Electric Power Company， Jiaozuo 454000， Henan， China

Abstract: Objectives Accurate power load forecasting plays a crucial role in ensuring stable power system operation and rational resource allocation. However， with the increasing penetration of renewable energy and the influence of complex meteorological conditions and diversified load structures， the uncertainty and difficulty of load forecasting have further increased. Methods Based on the electric load data of a 110 kV substation in Henan Province in 2022， an ISSA-CNN-BiLSTM hybrid forecasting model optimized by an improved sparrow search algorithm （ISSA） was proposed. First， a feature set was constructed using date information， meteorological data， and similar-day load data， and data preprocessing was performed to handle outliers， missing values， and inconsistent formats. Then， a CNN was employed to extract spatial–temporal features and construct feature vectors， which were subsequently input into a BiLSTM network for training and prediction. In addition， sine-cosine and Cauchy mutation strategies were introduced to enhance the traditional SSA for automatic hyperparameter optimization.  Results Compared with LSTM， PSO-LSTM， CNN-LSTM， and Transformer-BiLSTM models， the proposed ISSA-CNN-BiLSTM model achieved the lowest forecasting error， with a mean absolute percentage error （MAPE） of 1.570 3%， mean absolute error （MAE） of 0.427 6， and root mean square error （RMSE） of 0.645 3， demonstrating significant improvement in forecasting accuracy.  Conclusions The proposed ISSA-CNN-BiLSTM hybrid model effectively reduces the impact of stochastic factors in load forecasting. It enhances the ability to capture temporal dependencies and improves generalization performance， providing a new approach for efficient power system operation and optimal scheduling.

Key words:short-term load forecasting;CNN-BiLSTM hybrid model;improved sparrow search algorithm;prediction accuracy