Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning

The prediction of renewable energy plays a critical role in optimizing the operation, fault diagnosis, and other essential tasks within its energy system. Given the scarcity of labeled data and the proliferation of newly established renewable energy systems, the concept of deep transfer learning can enhance the performance of deep learning prediction models in the renewable energy domain. Most existing studies primarily discuss the application of individual transfer learning algorithms, lacking comparative analysis and detailed methodological discourse among them. In this study, we compare the effectiveness of domain adaptation and fine-tuning as transfer learning methods in scenarios with limited labeled data. Furthermore, we introduce a composite transfer learning framework that initially applies domain adaptation followed by fine-tuning. Utilizing solar radiation data measured in Tokyo and Okinawa, we designed two sets of experiments with interchangeable source and target domains to verify the effectiveness and robustness of the proposed model. The experimental outcomes indicate that the sequential application of domain adaptation followed by fine-tuning surpasses the standalone use of either method, achieving prediction accuracy up to 98.89 % of the model trained with two full years of data. Additionally, this approach demonstrates superior prediction stability and lower outlier values.