Optimizing of architectural geometry and tubular daylight guidance system based on genetic algorithm to enhance daylighting and energy performance in underground office buildings

Tubular daylight guidance system (TDGS), when properly selected and designed, can reduce excessive uncontrolled solar gain and daylight in underground spaces, thereby improving the building's energy performance and visual comfort. However, various questions related to their adaptation to different climates, design and optimization methods have not been answered. The aim of this paper is to show that different TDGS designs are needed even in geographic regions with similar climatic conditions. The objective function was defined as minimizing energy use intensity and maximizing daylight use. Sensitivity analysis indicated that the radius of TDGS was the most contributing of all the variables, followed by the aboveground building shape parameter, while the effects of the other variables varied widely. Therefore, this paper proposes a comprehensive optimization framework for small underground office buildings based on genetic algorithmic methods to seek the optimal design for applying tubular daylight guidance system (TDGS). Simulation studies were conducted in Ladybug and Honeybee software and further comparative optimization studies were conducted using the Strength Pareto Evolutionary Algorithm (SPEA-2) in the Octopus tool and the Non-dominated Sorting Genetic Algorithm-II (NSGAII) written in MATLAB. After applying the proposed optimization algorithm to the simulation study, the results demonstrate that the daylighting performance metric (UDI) has been improved to 53.2%-66.3% and the energy performance metric (EUI) has been reduced to 13.4%-78.4%, compared with the benchmark building. The research contribution is to propose a workflow that integrates parametric design, sensitivity analysis and NSGA-II to optimize TDGS designs with various parameters and ranges. In addition, the study proposes guidelines for designing TDGS in the five investigated cities.