Building electrical consumption patterns forecasting based on a novel hybrid deep learning model

Accurate prediction of electrical energy consumption in smart buildings is a critical challenge for optimizing energy management systems, reducing costs, and improving overall efficiency. Existing models often fail to account for the complex and nonlinear characteristics of energy consumption patterns, resulting in suboptimal prediction performance. This paper addresses the problem of accurate energy forecasting by proposing an intelligent hybrid model that integrates advanced feature selection, signal decomposition, and deep learning techniques. Specifically, the proposed model comprises three key components: (i) a mutual information-based feature selection method to identify the most significant input variables influencing energy consumption; (ii) a variational mode decomposition (VMD) approach to decompose the original energy consumption signal into intrinsic mode functions (IMFs), capturing relevant trends and eliminating noise; and (iii) a long short-term memory (LSTM) neural network to perform time-series forecasting of the target energy consumption values. The performance of the proposed model was evaluated using real-world datasets collected from a smart two-story residential building in Houston, Texas, USA. A comparative analysis was conducted against benchmark models, including the generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS), to validate the efficacy of the proposed approach. The results demonstrate that the proposed hybrid model achieves a significantly lower average root mean square error (RMSE) of 0.1192, compared to 0.264 for the GRNN and 0.319 for the ANFIS models, indicating superior prediction accuracy. These findings highlight the effectiveness of integrating mutual information, VMD, and LSTM for improving energy consumption forecasting in smart buildings. The proposed model provides a robust and accurate tool for energy management, enabling smart buildings to enhance operational optimization and energy efficiency.