Meta-reservoir computing for learning a time series predictive model of wind power

Wind energy has become an essential part of the energy power source of current power systems since it is eco-friendly and sustainable. To optimize the operations of wind farms with the constraint of satisfying the power demand, it is critical to provide accurate predictions of wind power generated in the future. Although deep learning models have greatly improved prediction accuracy, the overfitting issue limits the application of deep learning models trained under one condition to another. A huge number of data are required to train a new deep learning model for another environment, which is sometimes not practical in some urgent situations with only very little training data on wind power. In this paper, we propose a novel learning method, named meta-reservoir computing (MRC), to address the above issue, combining reservoir computing and meta-learning. The reservoir computing method improves the computational efficiency of training a deep neural network for time series data. On the other hand, meta-learning is used to improve the initial point and other hyperparameters of reservoir computing. The proposed MRC method is validated using an experimental dataset of wind power compared with the traditional training method. The results show that the MRC method can obtain an accurate predictive model of wind power with only a few shots of data.