A Regression Framework Design for Short Term Forecasting of Wind Power Based on Model-Agnostic Meta-Learning Strategy

At present, the wind power output prediction has to face with the multi-task challenges including cross environment and cross transducer equipment, so it often needs to conduct targeted training independently for different prediction targets. For this reason, firstly, a short-term prediction method based on model-agnostic meta-learning (abbr. MAML) was proposed. Secondly, based on the ability of the proposed method, by which the new task samples could be rapidly adapted, a new regression training framework was designed. Thirdly, combining with such sequential depth network models as the convolutional neural network-long and short term memory networks (abbr. CNN-LSTM), the Seq2Seq enhanced with the attention mechanism, the Transformer and Synthesizer enhanced with self-attention mechanism, this framework was applied to the wind power forecasting scene. Experiment results show that comparing with conventional pre-training-fine-tuning deep network training method, the proposed method improves such indicators as root-mean-square error (RMSE) and mean square error (abbr. MSE) on the dataset GEFCom2012 for each computing example, meanwhile, the generalization performance of each model on the prediction task, which takes short-term wind power output as the case, obtains a certain improvement. Besides, this training framework can easily convert the mainstream deep learning regression model and its dataset to the matched pattern adapted to model-agnostic meta-learning (abbr. MAML) strategy .