Optimization Algorithm for Green Environment Design Based on Artificial Intelligence

Abstract

In order to build energy-efficient commercial buildings in bustling urban centers and utilize passive means such
as natural ventilation and natural lighting as much as possible to improve indoor environmental quality, the author proposes a
green environment design optimization algorithm based on artificial intelligence. Taking Building A as an example, simulation
technology was used to optimize the performance of the enclosure structure, anti condensation in the glass atrium, and natural
ventilation during the transition season. The sunlight and shadow simulation software BSAT was used to simulate the mutual
occlusion and self occlusion of the building. It was found that external rolling shutters were not required for shading the facade
between axes L2-L5 and 3-9. Using DeST-C, the energy consumption and investment of south facing, east facing, and west facing
envelope structures were compared when using different types of glass. Combining economic efficiency and energy-saving effects,
the optimal southbound enclosure structure scheme for this building is to use Low-e membrane coated hollow double glass with ten
horizontal louvers for external shading; It is recommended to adopt the scheme of hollow double glass (Low-e membrane) or hollow
double glass (Lowe membrane)+external roller shutter in the east-west direction. It is worth noting that for east-west oriented
glass, the Lowe film should be low permeability, mainly to improve the thermal performance in winter and reduce the radiation
heat gain in summer. Using DeST-C for simulation 2 calculation, it was found that when the thermal performance of windows
increased from 3.0W/(m2. K), shading coefficient 0.7 to 2.0W/(m2 K), and shading coefficient 0.5, the maximum cold and heat
load of the building and the cumulative consumption of cold and heat throughout the year were significantly reduced, providing
an effective reference for solving problems.