A novel meta-learning approach for few-shot short-term wind power forecasting

Few-Shot Short-Term Wind Power Forecasting (FS-STWPF) is designed to develop accurate short-term wind power forecasting models with limited training data, reducing the losses suffered by wind farms and power systems due to the data scarcity. Based on the idea of extracting valuable knowledge from the source wind farms and then applying it to the target wind farm, a novel Meta-Learning approach (WG-Reptile) has been proposed in this paper. Building on the existing Reptile algorithm, two specific designs have been made in WG-Reptile for FS-STWPF: (1) Within-Task Samples Assignment method based on Operational Scenario (WTSAOS) has been proposed to improve the adaptability of the models to changing conditions. (2) Gradients Conflict Attenuation method based on Cosine Similarity (GCACS) has been proposed to enhance the effect of knowledge fusion from different source wind farms. Two open wind power forecasting datasets and three deep learning models have been used to implement 24-h-ahead FS-STWPF experiments with different amounts of training data. The results illustrate that the proposed WG-Reptile is able to outperform the other few-shot learning approaches. Intuitively, with only 30-day training data, the accuracy of the proposed WG-Reptile can be equivalent to the conventional supervised learning approaches trained on 6-month.