SolarGAN for Meso-Level Solar Radiation Prediction at the Urban Scale: A Case Study in Boston

Abstract

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.