Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting

Abstract

A common dilemma with deep-learning-based solar power forecasting models is their heavy dependence on a large amount of training data. Few-Shot Solar Power Forecasting (FSSPF) has been investigated in this paper, which aims to obtain accurate forecasting models with limited training data. Integrating Transfer Learning and Meta-Learning, approaches of Feature Transfer and Rapid Adaptation (FTRA), have been proposed for FSSPF. Specifically, the adopted model will be divided into Transferable learner and Adaptive learner. Using massive training data from source solar plants, Transferable learner and Adaptive learner will be pre-trained through a Transfer Learning and Meta-Learning algorithm, respectively. Ultimately, the parameters of the Adaptive learner will undergo fine-tuning using the limited training data obtained directly from the target solar plant. Three open solar power forecasting datasets (GEFCom2014) were utilized to conduct 24-h-ahead FSSPF experiments. The results illustrate that the proposed FTRA is able to outperform other FSSPF approaches, under various amounts of training data as well as different deep-learning models. Notably, with only 10-day training data, the proposed FTRA can achieve an RMSR of 8.42%, which will be lower than the 0.5% achieved by the state-of-the-art approaches.