Leveraging generative AI for renewable energy : photovoltaic panel semantic segmentation case study

Abstract

As solar energy gains prominence, the demand of photovoltaic (PV) panels has increased. To assess photovoltaic power capacity, it is vital to derive accurate distribution information of PV panels. Common cost- effective approach involves deep learning technique such as semantic segmentation. However, available datasets remain scarce and expensive. Fortunately, Generative Artificial Intelligence (Generative AI), specifically text-conditioned diffusion models, exhibits the potential to automatically generate high-resolution synthetic images paired with annotations created from cross-attention maps, serving as training datasets for photovoltaic panel semantic segmentation. In this study, we employ the off-the-shelf Stable Diffusion model to explore the power of Generative AI to address dataset limitations and curtail data collection and annotation expenses. From the outcomings, we believe that Generative AI will play a revolutionary role in renewable energy systems.