Precise energy modeling and green retrofitting optimization of existing buildings based on BIM and deep learning approaches

The construction industry has emerged as a major contributor to global energy consumption and greenhouse gas emissions amidst continuously rising worldwide energy demands. Enhancing building energy efficiency represents a critical intervention for achieving energy conservation and emission reduction targets. In the context of smart city development, such optimization efforts provide substantial momentum for sustainable urban growth. This study introduces a novel methodology that integrates Transformer models with Graph Neural Networks (GNNs) to improve the accuracy and operability of building energy efficiency prediction through advanced deep learning techniques. By leveraging Building Information Modeling (BIM) data to model spatial structures and energy consumption patterns, GNNs effectively capture complex relationships between building components, thereby strengthening the characterization of multidimensional interactions within structures. The self-attention mechanism in Transformers enables the model to focus on key factors such as energy consumption hotspots and temporal variations, enhancing learning capabilities across both spatial and temporal dimensions. To further augment optimization performance, we incorporate Generative Adversarial Networks (GANs) to generate diverse green renovation schemes, expanding optimization pathways and enhancing model adaptability and robustness. Experimental validation using BIM data demonstrates that our integrated approach outperforms traditional energy efficiency optimization models, increasing energy savings by nearly 4 These findings establish that BIM data-integrated deep learning optimization methodologies offer significant potential for providing effective energy efficiency prediction and optimization decision support. Such approaches directly contribute to building design and operations in smart cities, advancing the realization of green buildings and sustainable urban development.