Hierarchical Deep Learning Framework for Multi-Scale Energy Modeling in Smart Building Environments

In contemporary urban landscapes, the quest for sustainable energy consumption has propelled the evolution of smart building technologies. These structures are equipped with advanced sensors, actuators, and control systems to optimize energy utilization while ensuring occupant comfort and operational efficiency. Central to this paradigm is energy modeling, a process that entails the analysis and prediction of energy consumption patterns within building environments. Efficient energy modeling holds paramount significance for devising effective strategies to mitigate energy wastage, reduce operational costs, and minimize environmental footprints. This research presents a novel approach, the Hierarchical Deep Learning Framework for Multi-scale Energy Modeling in Smart Building Environments, aimed at enhancing energy modeling efficiency. The Deep Energy Fusion (DEF) algorithm is introduced by integrating Deep Belief Networks (DBNs) with a hierarchical energy modeling architecture. DEF leverages DBNs' capability to learn complex hierarchical representations of energy data across various scales, facilitating a comprehensive understanding of energy consumption patterns. To evaluate DEF's performance, the simulation analysis are conducted by comparing it with existing algorithms. Metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R^2) are employed to assess prediction accuracy. Our results demonstrate that DEF outperforms traditional algorithms in capturing both local and global dependencies within the energy data, thereby improving energy modeling accuracy. Moreover, DEF showcases superior performance in terms of computational efficiency and scalability. This research contributes to advancing energy modeling techniques in smart buildings, offering a robust framework for optimizing energy management strategies