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

Traditional buildings face problems such as low construction efficiency and high costs during the construction process, which cannot meet the requirements of green and sustainable development of buildings. To address this, a design model is constructed using BIM technology as a framework, which optimizes the energy efficiency of multiple building indicators and constructs a building energy efficiency optimization model based on decomposition of multiple objectives. Considering the cost and convergence issues of the goal solving model, an agent-assisted multi-objective particle swarm optimization model is introduced to construct an energy-saving design optimization model. The fitness of the particle swarm is optimized through an agent optimization strategy, thereby adjusting the speed and position of particles and improving the model optimization effect. Performed performance testing on the proposed model, and the proposed model has the best optimization performance under the Ackley function. After 100 iterations, it tends to converge. At this point, the best fitness value of the model is 0.452. Applying the proposed technology to specific cases for target optimization of urban single-room office buildings, the proposed optimization model has a shorter search time, a minimum of 2408 uncomfortable hours, and a minimum total energy consumption of 47.5, which are superior to the other three models. Finally, by comparing the comprehensive application effects of the model, the proposed model has the best performance in terms of super volume index, reaching 26,473 in urban office buildings. Compared to the comparison model, the proposed model takes 1.3 h, and the overall optimization time is the shortest. In traditional residential building testing, the proposed model has a super volume index of 50,132, which is also the best and has the shortest training time. It can be seen that the proposed optimization model has excellent optimization ability, and compared to other technologies in energy-saving optimization design, the proposed technology has the best optimization effect. Research technology will provide important technical support for the energy-saving optimization design and construction management of modern buildings.