###### Abstract
Among them, deep learning methods exhibit advantages in the field of energy consumption prediction compared to ordinary machine learning methods [27]. They can automatically learn complex nonlinear relationships, adapt to large-scale data, effectively capture spatiotemporal relationships, and enable end-to-end learning. Therefore, deep learning methods are widely applied in the predictive research of complex systems, including energy consumption in large-scale public buildings. Predicting energy consumption in large public buildings is highly challenging due to variations in functional areas, diverse time-series characteristics, complex building structures, sensor data noise, and various influencing factors on energy consumption [28]. To address this, researchers propose methods utilizing feature engineering and signal decomposition techniques for energy consumption prediction in large public buildings, demonstrating promising predictive accuracy [29; 30]. [PERSON] et al. [31] combined Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) neural networks to construct a short-term load prediction model. They applied the EMD method to decompose the load into several Intrinsic Mode Functions (IMFs) and a residue, while the LSTM neural network was used to predict each IMF and residue, yielding the final prediction. [PERSON] et al. [32] employed an EMD-Elman neural network prediction model for wind speed prediction and compared its performance with other widely used methods, showing a significant improvement in predictive accuracy. Addressing the issue of high feature complexity in the prediction process, [PERSON] et al. [33] combined the entropy weight method with gray relational analysis for feature extraction to enhance the accuracy of cooling load prediction. [PERSON] et al. [34] proposed the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm to enhance the performance of EEMD. Compared to EMD and EEMD, CEEMDAN improves denoising capabilities, preserves the physical meaning of the signal, enhances the physical interpretability of mode functions, and offers more flexible parameter settings and better convergence [35]. While these studies employ signal decomposition to enhance predictive accuracy, none of them individually perform regression feature selection on the decomposed components. Therefore, in this study, we utilize the CEEMDAN method for data signal decomposition and concurrently conduct feature importance screening on the different decomposition outcomes. This approach aids in more accurately predicting energy consumption data. In conclusion, while some studies have employed ensemble learning methods involving signal decomposition and feature engineering, these approaches often lack feature screening or the utilization of different machine learning models for different frequency signals after decomposition. Therefore, this study proposes a model for predicting energy consumption in large public buildings by combining CEEMDAN signal decomposition, composite feature screening, and Bilstm-attention prediction. This approach aims to enhance the accuracy of energy consumption prediction through signal decomposition and feature screening. The core idea involves decoupling the highly nonlinear energy consumption data of large public buildings using CEEMDAN signal decomposition, obtaining decomposed Intrinsic Mode Function (IMF) components and residuals. For each IMF component and residual, composite feature screening is applied separately, considering the varying importance of features for each component. Finally, these components are input into the Bilstm-attention model for individual predictions. The structure diagram of this paper is shown in Figure 1. The results for each component are summed to obtain the final prediction. The experimental results show that the model is effective in predicting the energy consumption of complex large public buildings.