Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan

Deep learning models are increasingly applied in the field of solar radiation prediction. However, the substantial demand for labeled data limits their rapid application in newly established systems. Traditional transfer learning employs pre-training and fine-tuning methods to reduce the use of data in the target system. However, it still necessitates a small amount of labeled data for fine-tuning. This results in extensive time and cost for data collection, delaying the deployment of prediction models and optimization algorithms and leading to energy wastage. In this study, we employed the Adversarial Discriminative Domain Adaptation (ADDA) approach to achieve transfer learning under zero-label conditions in the target system, enabling new systems to harness the knowledge from other systems to create predictive models. Using the measured solar radiation data from Tokyo and Okinawa, two sets of experiments were designed with interchanged source and target domains to validate the efficacy and robustness of the proposed model. The results indicate that compared with the method of directly using the source domain model, transfer learning can enhance the predictive accuracy of the test set by at least 14% in both experiments, exhibiting more stable predictive performance and reduced prediction outliers.