Design and analysis of energy efficient urban buildings based on BIM model.

The article focuses on the design and analysis of energy-efficient urban buildings using a Building Information Modeling (BIM)-based framework. It constructs a multi-objective (MO) optimization model addressing building energy consumption (EC) and user comfort, employing the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) and introducing an agent-assisted multi-objective particle swarm optimization (MOPSO) model enhanced by a surrogate model (SMO) to improve optimization efficiency and convergence. Performance tests using benchmark functions demonstrate that the proposed agent-assisted MOPSO model outperforms traditional MOEA/D and MOPSO models in convergence speed, fitness value, and computational time. Case studies on urban single-room office buildings and traditional northern residential buildings show that the proposed model achieves significant reductions in energy consumption and uncomfortable hours, with superior optimization results compared to other models. The research suggests that integrating BIM with advanced multi-objective optimization algorithms provides effective technical support for sustainable and energy-saving building design, though future work should incorporate additional variables such as optoelectronics and smart home technologies to enhance model accuracy.