MI H R, SHEN S L, WEI Z R, et al．An improved SSA-optimized CNN-BiLSTM integrated model for short-term electricity load forecasting［J］. Journal of Henan Polytechnic University( Natural Science)， doi: 10.16186/j.cnki.1673-9787.（  2025120085  ）.

doi: 10.16186/j.cnki.1673-9787.（  2025120085  ）

Received： 2025-12-26

Revised： 2026-04-28

Online：2026-06-05

An improved SSA-optimized CNN-BiLSTM integrated model for short-term electricity load forecasting (Online)

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

          Henan Jiaozuo Power Supply Company,Jiaozuo 454000,Henan,China

Abstract: Objectives Accurate power load forecasting is one of the effective means to ensure stable operation of power grid and rational resource allocation. However, due to the high penetration of renewable energy, as well as the influence of meteorological conditions and diversified load structures, the uncertainty and difficulty of power load forecasting have further increased. Methods Based on the power load data of a 110kV substation in a certain region of Henan Province in 2022, an ISSA-CNN-BiLSTM integrated forecasting model optimized by an improved sparrow search algorithm (ISSA) was investigated. Firstly, by considering date information, meteorological data, and similar daily load data, a feature set was constructed as input variables for the model and the outliers, missing values and non-standard data formats were preprocessed. Secondly, the CNN model was employed to effectively extract the potential connections between sequential/non-sequential data in the feature map and construct the feature vectors of the time series. Then, BiLSTM model was used for training and prediction and the traditional SSA was enhanced by incorporating sine-cosine and Cauchy mutation strategies to achieve automatic optimization of model hyperparameters. Results Compared to the LSTM, PSO-LSTM, CNN-LSTM and Transformer-BiLSTM forecasting models, the ISSA-CNN-BiLSTM model exhibited the lower forecasting error, with the mean absolute percentage error (MAPE) as low as 1.570 3%, mean absolute error (MAE) of 0.427 6 and root mean square error (RMSE) of 0.645 3, which indicated a significant improvement in forecasting accuracy. Conclusions The ISSA-CNN-BiLSTM integrated forecasting model effectively mitigates the impact of stochastic factors in load forecasting. It exhibits enhanced capability in capturing temporal load correlations and possesses stronger generalization performance. This model can provide novel insights and methodologies for the efficient operation and optimal scheduling of actual power systems.

Key words: load forecasting; CNN-BiLSTM fusion model; ISSA; prediction accuracy