Generalizable Solar Irradiation Prediction using Large Transformer Models with Sky Imagery

Abstract

Deployment of solar power system in new locations impose several challenges on the operations of local and regional power grids due to the inherent variation in ground-level solar irradiance. This work proposes a novel real-time solar now-casting methodology for solar irradiance prediction based on deep transfer learning from ground-based sky imagery. Existing approaches use statistical methods or Convolutional Neural Networks for irradiation regression trained for a particular location that cannot be transferred to new locations deploying potentially different imaging sensors. This observation motivated us to introduce a large deep neural network based on Vision Transformers that is generalizable and transferable to different scenarios.The system is developed using multiple years of solar irradiance and sky image recordings in two locations. We captured our own data set in Princeton, NJ, USA and also used open-source ASI16 benchmark dataset captured in Golden, CO, USA. The method is validated against these two locations of diverse geographic, climatic conditions and sensor variation. Results show that the proposed method is robust and highly accurate (85-90% accuracy) for multiple locations deployment with 50% less data requirement from new locations.