This paper proposed an optimization method to minimize the building energy consumption and visual discomfort for a passive building in Shanghai, China. A total of 35 design parameters relating to building form, envelope properties, thermostat settings, and green roof configurations were considered. First, the Latin hypercube sampling method (LHSM) was used to generate a set of design samples, and the energy consumption and visual discomfort of the samples were obtained through computer simulation and calculation. Second, four machine learning prediction models, including stepwise linear regression (SLR), back-propagation neural networks (BPNN), support vector machine (SVM), and random forest (RF) models, were developed. It was found that the BPNN model performed the best, with average absolute relative errors of 3.27% and 1.25% for energy consumption and visual comfort, respectively. Third, six optimization algorithms were selected to couple with the BPNN models to find the optimal design solutions. The multi-objective ant lion optimization (MOALO) algorithm was found to be the best algorithm. Finally, optimization with different groups of design variables was conducted by using the MOALO algorithm with the associated outcomes being analyzed. Compared with the reference building, the optimal solutions helped reduce energy consumption up to 34.8% and improved visual discomfort up to 100%.

In China, rapid urbanization brought about the problems of increased building energy consumption and decreased area of green space as well as poor air quality and heat island effect. Building envelope integrated green plants (BIGP), which is also called as vertical greening, is regarded as the potential solution to the energy and environmental issues. This article verifies and analyzes the energy saving potential of BIGP in China's hot summer and cold winter regions through comparative experiments between a vertical greening room and a reference room. The winter time experiment was carried out from December 2017 to January 2018, and the summer time experiment was from July to August 2018. During winter, the heat flux density of the exterior wall is reduced by 3.11 W/m² with BIGP, and the hourly power consumption of the reference room is 1.22 times that of the room with BIGP. The energy saving rate of BIGP is approximately 18%. During summer, the heat flux density of the exterior wall of the reference room is 4.15 W/m² larger than that of the vertical green room and the hourly power consumption is 1.33 times that of the vertical greening room. The energy saving rate of BIGP is about 25%.