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

Xin Ren, Yimei Wang, Zhi Cao, Fuhao Chen, Yujia Li and Jie Yan ()
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Xin Ren: China Huaneng Clean Energy Research Institute, Beijing 102209, China
Yimei Wang: China Huaneng Clean Energy Research Institute, Beijing 102209, China
Zhi Cao: China Huaneng Group Co., Ltd., Beijing 100031, China
Fuhao Chen: School of New Energy, North China Electric Power University, Beijing 102206, China
Yujia Li: School of New Energy, North China Electric Power University, Beijing 102206, China
Jie Yan: School of New Energy, North China Electric Power University, Beijing 102206, China

Energies, 2023, vol. 16, issue 17, 1-13

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.

Keywords: Few-Shot Solar Power Forecasting; deep-learning; transfer learning; meta-learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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