A novel transfer learning strategy for wind power prediction based on TimesNet-GRU architecture

Currently, data-driven deep learning models are widely applied in the field of wind power prediction. However, when historical data are insufficient, deep learning models struggle to exhibit satisfactory predictive performance. In order to overcome the issue of limited training data for new wind farms, this study proposes a novel transfer learning strategy to address the challenge of less-sample learning in short-term wind power prediction. The research is conducted in two stages. In the pre-training stage, the TimesNet-GRU prediction model is established using data from a source wind farm. Parallel TimesNet modules are employed to extract multi-period features from various input feature sequences, followed by the extraction of long- and short-term features from the time series through gate recurrent unit (GRU). In the transfer learning stage, an effective transfer strategy is designed to freeze and retrain certain parameters of the TimesNet-GRU, thereby constructing a prediction model for the target wind farm. To validate the effectiveness of this approach, the results from testing with actual data from five wind farms in northwest China demonstrate that the proposed method exhibits significant advantages over models without transfer learning as explored in this study.