## 4 Result and Discussion
### Performance Validation of the Model  
This study documented the hourly energy consumption data, time-related features, and meteorological feature data of a large public building in Shaanxi Province from 1 June to 31 August 2022. The dataset was divided into 70% for model training and 30% for model testing. To demonstrate the effectiveness of hybrid prediction models, experiments were conducted using five prediction models: BiLSTM, CNN-BiGRU, CEEMDAN-CNN-BiLSTM, CEEMDAN-BiLSTM, CEEMDAN-FCBF-BiLSTM, and the proposed CEEMDAN-MIC-FCBF-BiLSTM model. Comparative analyses were performed on the prediction results. Figure 12 illustrates the results obtained by the proposed method and the comparative methods. Figure 13 shows the prediction error diagram of the proposed method and the comparison method. As shown in the figure, the prediction error of the proposed method is the lowest among the comparison methods. In periods of significant energy consumption fluctuations, the predictive performance of a single machine learning model appears to be limited. However, the hybrid prediction model proposed in this study, benefiting from CEEMDAN signal decomposition and MIC-FCBF combined feature selection, demonstrates suitable fitting even during periods of high energy consumption fluctuations. The predictive performance is significantly superior to other models. This highlights the superiority of the hybrid model, especially in addressing situations with substantial energy consumption fluctuations. By integrating various models and feature selection methods, the accuracy and stability of the predictions have been enhanced. Figure 11: Degree of correlation between features and decomposition results SU. Figure 14 presents a radar chart of prediction accuracy evaluation metrics, offering a clear comparison between the proposed model and other models in terms of prediction performance. The specific evaluation metrics are shown in Table 2. The summary of the prediction results is as follows:  
1. The CEEMDAN-MIC-FCBF-BiLSTM model outperforms other models in all accuracy evaluation metrics, exhibiting the smallest MAE, MSE, RMSE, and MAPE and the highest \(R^{2}\) and RPD values. This indicates that the CEEMDAN-MIC-FCBF-BiLSTM model excels in the accuracy and stability of energy consumption prediction for large public buildings. 2. Both the CEEMDAN-MIC-FCBF-BiLSTM model and the CEEMDAN-BiLSTM model outperform the BiLSTM model in various evaluation metrics, showing an  
Figure 12: Prediction results of CEEMDAN-MIC-FCBF-BiLSTM and comparison methods. Figure 13: Prediction error of CEEMDAN-MIC-FCBF-BiLSTM and comparison methods. improvement in model performance. This suggests that CEEMDAN signal decomposition and the incorporation of AM (Attention Mechanism) in BiLSTM contribute to enhanced prediction accuracy, especially in scenarios with complex feature coupling for energy consumption prediction in large public buildings. 3. The RMSE of CEEMDAN-MIC-FCBF-BiLSTMAM is 11.9, demonstrating a 57.23% improvement compared to CEEMDAN-FCBF-BiLSTMAM without MIC value screening and a 67.21% improvement compared to CEEMDAN-BiLSTMAM without feature selection. This indicates that the MIC-FCBF combined feature selection method can significantly improve the model's prediction accuracy. The RMSE is 65.65% lower compared to CNN-BiLSTM, highlighting the effectiveness of feature extraction through CNN for improving prediction accuracy. In conclusion, the superiority of the proposed model, CEEMDAN-MIC-FCBF-BiLSTMAM, lies in its use of the CEEMDAN algorithm for energy consumption data decomposition, the application of the combined feature selection MIC-FCBF method for screening IMF components and residuals post-decomposition, and the incorporation of the Attention Mechanism in BiLSTM.