Enhancing urban solar irradiation prediction with shadow-attention graph neural networks: Implications for net-zero energy buildings in New York City

Abstract

Assessing building photovoltaic (PV) potential is crucial for urban energy planning and achieving Net-Zero Energy Building (NZEB) goals. However, urban-scale assessment still faces limitations. Most studies have neglected the importance of facade PV potential. Moreover, they often treat buildings as isolated entities, overlooking the impact of interactions between buildings, such as shading effects. To address these shortcomings, this study proposes a novel Shadow-Attention Graph Neural Network (SAGNN) to accurately predict solar irradiation for large-scale urban buildings. Analyzing 1.08 million buildings in New York City at a 1-meter spatial resolution, the study explores the potential for achieving NZEB and Net-Zero Electricity Building (NZEL) status. Results show that building rooftops and facades have annual PV power generation potential of approximately 29,851.9 GWh and 32,062.2 GWh, respectively. However, the PV potential is still insufficient to achieve the NZEB goal for the entire city. Nevertheless, utilizing both roof and facade PV can enable many areas to achieve NZEL. Cost-benefit analysis reveals that rooftop PV systems are more economically viable than facades, with a payback period of 7 years and a net-benefit of $71.98 billion over the 25-year life cycle. This research provides scientific decision support for urban PV planning and NZEB policy formulation.