Research on Short-Term Electricity Load Forecasting Based on Improved CNN-GRU Model

For stable operation of the power system and to meet its demand for short-term power load forecasting accuracy, a short-term power load forecasting method based on the improved Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) model is proposed. A Kernel Principal Component Analysis (KPCA) is used to process the multidimensional input data, and the primary influencing factors are effectively extracted as inputs for the subsequent prediction model. A CNN-GRU combination model with an improved Osprey Optimization Algorithm (OOA) is constructed for training and prediction, and an attention mechanism is introduced to strengthen the influence of important information for enhancing the prediction performance of the prediction model. Finally, the eXtreme Gradient Boosting (XGBoost) algorithm optimized by Bayesian Hyperparameters (BH) theory is used to optimize the prediction error, a simulation model is constructed for comparison with multiple models, and the effectiveness of the proposed method is verified based on the obtained prediction effect curves and various performance indexes. The experimental results show that the Mean Absolute Percentage Error (MAPE) of the proposed CNN-GRU model during training and testing are 1.56% and 1.99%, respectively, indicating that the proposed model has improved prediction accuracy.