MTTLA-DLW: Multi-task TCN-Bi-LSTM transfer learning approach with dynamic loss weights based on feature correlations of the training samples for short-term wind power prediction

Wind power prediction for newly built wind farms is usually faced with the problem of no sufficient historical data. To efficiently extract the useful features from related wind farms, a novel transfer learning method based on temporal convolutional network (TCN)-Bi-long short-term memory (LSTM) with dynamic loss weights is proposed. Firstly, a novel multi-task TCN-Bi-LSTM model is designed to extract common features. The separate TCNs, and common Bi-LSTM layers of the proposed model are designed to extract the temporal features from related wind farms. Secondly, in the pre-training stage, to optimize the training process of the neural networks, a dynamic loss-weighting strategy is proposed for multi-task learning (MTL) to select the most related features, which increase the prediction accuracy by providing a suitable optimization object. Thirdly, the multi-task TCN-Bi-LSTM model is re-trained based on the samples from the target wind farm. Finally, a dataset of seven wind farms was employed to evaluate the efficiency of the proposed MTL structure and the dynamic loss-weighting strategy. The result shows that the root mean squared error of the 12-h short-term prediction can be decreased by 4.19% compared with the traditional single-task learning model, which verifies the validity of the proposed multi-task transfer learning method.