Multi-objective optimization for the daylighting and thermal comfort performance of elevated subway station buildings in cold climate zone of China

Abstract

The rapid development of urban rail transit brings about the rapid growth of elevated subway station buildings, while the demand for comfortable waiting environments on the passenger platforms is also increasing. Due to the fact that elevated stations generally have a transparent envelope, there is a contradiction between daylighting, heating, and air conditioning. Therefore, multi-objective optimization design is essential. This article selects the cold regions of northern China as the research object. A field investigation was carried out, and Tianjin University Town Station was found to be a typical station in cold climate zone of China. A prototypical model was built based on it using Grasshopper in Rhino. Sensitivity analysis was used to screen key independent variables, and strength Pareto evolutionary algorithm II was used to optimize the daylighting and thermal comfort performance for the elevated subway station buildings. Two optimization processes, step-by-step and one-step, were used to obtain the non-dominated solution set, and the optimal solution was obtained by technique for order preference by similarity to an ideal solution analysis. The suggested values for the design variables of the prototypical model are also displayed. Reducing the skylight's window-to-wall ratio helps improve daylighting and thermal comfort. Besides, the one-step optimization method has higher efficiency and can obtain better results. The findings proposed here can guide the development of elevated subway station buildings in north China and other areas with similar conditions.