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%.

This paper investigates the energy performances of a hybrid system composed of a phase change materials-ventilated Trombe wall (PCMs-VTW) and a photovoltaic/thermal panel integrated with phase change material (PV/T-PCM). Equivalent overall output energy (Q_E) was proposed for energy performance evaluation regarding different energy forms, diversified conversions and hybrid thermal storages. This study focuses on parameters’ optimization of the PV/T-PCM system and parameters in the PCMs-VTW are kept optimal. Based on the experimentally validated numerical modelling, nine trial experiments have been conducted following Taguchi L₉ (3⁴) standard orthogonal array. The higher the better concept was implemented and the optimal combination of operating parameters was thereafter identified by using signal-to-noise (S/N) ratio and Analysis of Variance (ANOVA) method. The results show that Q_E is highly dependent on the mass flow rate, followed by the diameter of active cooling water pipe. However, the inlet cooling water temperature and the thickness of PCM have limited influence on Q_E. The optimal combination of each factor was identified as B3A3C2D1 (mass flow rate of 1 kg/s, diameter of water pipe of 0.6 m, inlet cooling water temperature of 15 °C and the thickness of PCM of 20 mm) with the highest Q_E of 20,700 kWh.

A Chinese traditional narrow street, named Cold-Lane, can create a microclimatic zone that provides pedestrian thermal comfort under hot and humid climate conditions. This phenomenon was observed through experimental measurement during the summer of 2016. The heat transfer rate over the pedestrian body surface was calculated to reveal why pedestrians experience a cool sensation, and computational flow dynamics (CFD) simulation was carried out to study the influence of the street aspect ratio on the shading effect. It was found that the perception of thermal comfort can be attributed mainly to the radiation between the relatively cool surrounding walls and the human body, and the wind velocity has little effect on sensible heat dissipation. The cool horizontal and vertical surfaces in the street canyon are mainly due to the shading effect as a result of the small aspect ratio, which is a typical characteristic of the traditional Chinese street. The shading effect of the high walls on both sides creates the cooling effect of this narrow street.