Passive architectural design refers to improving and creating a thermal and comfortable indoor environment from the perspective of architectural design without relying on construction equipment. Its principle is the application of natural ventilation, natural lighting, temperature and humidity changes, and other parameters, and the purpose is to minimize the consumption of conventional energy. Aksamija discussed the feasibility of achieving net-zero energy consumption targets in commercial building retrofitting by integrating passive design strategies and energy-efficient building systems to improve building performance and reduce energy consumption [14]. Fan et al. focused on the high energy consumption of the atrium in cold areas and analyzed the influence of the proportion, height, layout, and other parameters on energy consumption through an energy consumption simulation. The results revealed that the unit energy consumption increased with an increase in the atrium volume. The surrounding atrium is more energy efficient than the other types. With an improvement in the uniformity of the atrium plane, the total energy consumption of the building increases [23]. Additionally, many scholars have discussed the design, parameters, and materials of building maintenance structures under energy-conservation guidance [24,25,26]. With the maturity of the parametric design platform and the theory and technology of building energy consumption simulations, there have been sufficient studies examining the in-depth quantitative analysis of building energy consumption [27,28]. Amasyali et al. used EnergyPlus and machine learning to understand and improve user behavior and achieved both energy consumption reduction and occupant comfort [29]. Corbin et al. studied a model-predictive control (MPC) environment. The environment integrated MATLAB and EnergyPlus to predict the optimal building control strategy, and this resulted in energy savings of up to 54% compared to the base case and improved thermal comfort for users [30]. Sangireddy and Bhatia et al. endeavored to reduce the amount of computation needed to calculate the energy consumption of different modes and parameter combinations, and they learned from the data generated by simulation and established a support vector machine to achieve the minimum hourly cooling energy consumption level while maintaining the thermal comfort of the residence [31]. The energy consumption of a commercial complex atrium can surge. Previous studies have conducted research examining the layout, structural design, shading, and other parameters. They have also confirmed the feasibility of passive energy-saving design; in spite of this, passive energy-saving studies focused on building spatial forms remain insufficient. Due to the “chimney effect” of the tall atrium of the commercial complex, it has a good natural ventilation capacity. The design of other types of public buildings such as office, cultural, and sports buildings attaches great importance to the natural ventilation efficiency of the atrium spaces. Therefore, it is necessary to consider natural ventilation and energy savings at the beginning of the design of commercial complexes that experience dense human flow and long operational cycles.

Certain researchers calculated indoor thermal comfort using EnergyPlus but ignored the impact of indoor wind speed and temperature [15]. Aghamolaei pointed out that most existing studies have not investigated the joint effect of solar radiation and local wind speed simultaneously. When the model focused on wind, the solar calculation was simplified and vice versa. Therefore, CFD and EnergyPlus should be combined in the research process to improve the accuracy of thermal comfort simulation. This study proposes a novel simulation framework for outdoor environments using CFD and Building Energy Simulation (BES) methods to couple radiation and convective fluxes in outdoor environments [32]. Guo et al. combined EnergyPlus and CFD to evaluate the impacts of climatic conditions and building forms on the natural ventilation of sports venues in subtropical areas [33]. Recently, there has been a gradual increase in the use of CFD software platforms to simulate natural ventilation and improve indoor thermal comfort. However, research combining CFD and EnergyPlus dual-platform simulations to improve resilient ventilation, reduce the frequency of indoor air conditioning, and save energy remains in its infancy. Resilient ventilation has been confirmed to exert a significant impact on the indoor and outdoor thermal comfort of buildings; however, there is still a lack of sufficient quantitative analysis of the effect and duration of different building forms on thermal comfort.

Machine learning (ML) is widely used in the context of data processing and model training, has been relatively well studied, and is a popular topic in the academic field [34,35,36]. Olu-Ajayi pointed out that ML methods are considered the best way to produce the desired results in prediction tasks and have been applied to the field of energy consumption in operational buildings. However, few studies have investigated the applicability of ML methods to predict potential building energy consumption in the early design phase to reduce the construction of low-energy buildings [37]. Zhang revealed the relationship between building shape and thermal comfort performance of semi-outdoor sports venues, selected canopy elevation, façade porosity coefficient, and sun-shade tilt angle as parameters to control building shape and developed a method to optimize building shape using an ANN and a genetic algorithm (GA) [38]. However, a relationship between the building form and energy consumption has not been proposed. Although certain scholars have applied machine learning to explore the relationship between spatial form and architectural performance, existing research is relatively weak. In particular, the relationship between the roof design calculation method and building energy consumption requires further investigation. Low-energy buildings have received considerable attention due to energy conservation policies in various countries worldwide. However, a proper assessment of building energy consumption requires dynamic simulations and the analysis of multiple environments or proposals to obtain the optimal solution. Longo et al. reviewed the optimal design of low-energy buildings and determined that multi-objective optimization and GAs were the most popular [39]. Malatji proposed a multi-objective optimization model and solved it using a GA. In the building energy renovation process, an optimal decision is reached by selecting optimal measures [40]. Moreover, the ANN training model method is mature, and a number of scholars have used artificial neural networks and GAs to improve the computational efficiency and accuracy of time-consuming simulations [41,42]. In the field of computational design, a method of combining machine learning with a GA has long been established, and many scholars have conducted in-depth research and discussions. This is a widely accepted industrial workflow. However, the application of artificial neural network learning and optimization screening to explore the atrium roof shape and building energy consumption must still be further investigated to save simulation time and facilitate architects with regard to adjusting the shape design in the early stage of scheme planning.