###### Abstract  
Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large public buildings often pose challenges in improving prediction accuracy. In this study, we propose a combined prediction model that combines signal decomposition, feature screening, and deep learning. First, we employ the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose energy consumption data. Next, we propose the Maximum Mutual Information Coefficient (MIC)-Fast Correlation Based Filter (FCBF) combined feature screening method for feature selection on the decomposed components. Finally, the selected input features and corresponding components are fed into the Bi-directional Long Short-Term Memory Attention Mechanism (BiLSTMAM) model for prediction, and the aggregated results yield the energy consumption forecast. The proposed approach is validated using energy consumption data from a large public building in Shaanxi Province, China. Compared with the other five comparison methods, the RMSE reduction of the CEEMDAN-MIC-FCBF-BiLSTMAM model proposed in this study ranged from 57.23% to 82.49%. Experimental results demonstrate that the combination of CEEMDAN, MIC-FCBF, and BiLSTMAM modeling markedly improves the accuracy of energy consumption predictions in buildings, offering a potent method for optimizing energy management and promoting sustainability in large-scale facilities. feature engineering; Bilstm-attention; large public building; energy consumption prediction +
Footnote †: journal: Medical  
the building energy consumption process. Although they offer high transparency and prediction accuracy, they face challenges due to high data requirements, complexity, and dependence on deep domain knowledge [9]. Gray-box models, positioned at the intersection of white-box and black-box models in theoretical frameworks, combine physical system principles with empirical knowledge. By incorporating parameter estimation and tuning, gray-box models exhibit data adaptability and robustness, demonstrating ideal transparency and flexibility in handling incomplete data or complex systems. However, they require domain expertise for parameter adjustments, demanding a certain level of understanding and prior knowledge, leading to relatively higher computational costs [10; 11]. In the field of large-scale public building energy consumption prediction, black-box models have achieved success due to their simplicity in modeling, high prediction accuracy, and ability to address nonlinear problems [12]. However, feature screening and handling energy consumption data noise are critical factors affecting the prediction accuracy of black-box models. Therefore, many researchers have focused on studying energy consumption prediction methods based on black-box models [13; 14]. Over the years, various scholars have conducted research on energy consumption prediction using black-box models, primarily involving statistical methods and machine learning methods [15; 16]. Statistical methods [17], including multivariate linear regression [18] and autoregressive integrated moving average (ARIMA) [19], can fit linear relationships in data. However, the models of statistical methods are usually simpler, and it is difficult to capture complex time-series dynamics or complex relationships in high-dimensional spaces when dealing with nonlinear time-series data or high-dimensional data, thus generating large errors. As the internal complexity of buildings continues to increase, machine learning methods demonstrate advantages in dealing with nonlinear, non-stationary, and high-dimensional data [20]. Machine learning-based energy consumption prediction models encompass several types, such as Support Vector Machines (SVMs) [21; 22], Artificial Neural Networks (ANNs) [23; 24], and deep learning (DL) [25; 26].