Reducing energy consumption while providing a high-quality environment for building occupants has become an important target worthy of consideration in the pre-design stage. A reasonable design can achieve both better performance and energy conservation. Parametric design tools show potential to integrate performance simulation and control elements into the early design stage. The large number of design scheme iterations, however, increases the computational load and simulation time, hampering the search for optimized solutions. This paper proposes an integration of parametric design and optimization methods with performance simulation, machine learning, and algorithmic generation. Architectural schemes were modeled parametrically, and numerous iterations were generated systematically and imported into neural networks. Generative Adversarial Networks (GANs) were used to predict environmental performance based on the simulation results. Then, multi-object optimization can be achieved through the fast evolution of the genetic algorithm binding with the database. The test case used in this paper demonstrates that this approach can solve the optimization problem with less time and computational cost, and it provides architects with a fast and easily implemented tool to optimize design strategies based on specific environmental objectives.

Evaluating solar radiation distribution at the urban scale is crucial for optimizing the placement and size of solar installations and managing urban heat. This study introduces a method for predicting urban solar radiation using 2D mapping data, applying a Generative Adversarial Network (GAN) model to the city of Boston. Traditional solar radiation simulation methods, such as 3D modeling and satellite imagery, require complex and resource-intensive data inputs. In contrast, this research allows open-source 2D urban geographic information—such as building footprints, heights, and terrain—to predict solar radiation at various spatial scales (150 m, 300 m, and 500 m). The GAN model, using detailed 3D urban modeling and simulation results, trained paired datasets of geographic information and solar radiation heatmaps. It achieved high accuracy and resolution, with the 300 m scale model demonstrating the best performance (R² = 0.864). The model’s capability to generate high-resolution (2 m) solar radiation maps from simplified inputs demonstrates the potential of GANs for urban climate data prediction, offering a rapid and efficient alternative to traditional methods. This approach holds significant potential for urban planning, particularly in optimizing photovoltaic (PV) system layouts and managing the UHI effect.