Abstract
Wind power forecasting (WPF) is imperative to the control and dispatch of the power grid. Firstly, an ultra-short-term prediction method based on multilayer bidirectional gated recurrent unit (Bi-GRU) and fully connected (FC) layer is proposed. The layers of Bi-GRU extract the temporal feature information of wind power and meteorological data, and the FC layer predicts wind power by changing dimensions to match the output vector. Furthermore, a transfer learning (TL) strategy is utilized to establish the prediction model of a target wind farm with fewer data and less training time based on the source wind farm. The proposed method is validated on two wind farms located in China and the results prove its superior prediction performance compared with other approaches.
Highlights
The renewable energy problem is the focus of the 21st century (Zheng et al, 2017; Li et al, 2016)
In order to evaluate the prediction performance of the prediction model, the root mean square error (RMSE), mean absolute error (MAE), and accuracy (Cr) are taken as evaluation metrics according to international standards
In order to explore and verify the advantages of using transfer learning to predict power, the following cases are compared: a) The pre-trained model in the source domain is directly loaded, and denoted as NO_fine-tunning (NO_FT); b) The pre-trained model in the source domain is loaded and the parameters in bidirectional gated recurrent unit (Bi-gated recurrent unit (GRU)) layers are frozen and the parameters of the fully connected (FC) layer are fine-tuned with target-domain data, which is named Fixed_Bi-GRU; c) The pre-trained model in the source domain is loaded and the parameters in the FC layer are frozen and the parameters of BiGRU layers are fine-tuned with target-domain data, which is named Fixed_FC; d) Redefine a prediction model whose structure is the same as that of the source-domain model but whose parameters are not trained at all
Summary
The renewable energy problem is the focus of the 21st century (Zheng et al, 2017; Li et al, 2016). TL is a new method that breaks through traditional machine learning and is widely used in computer vision, text classification, and other fields (Wang et al, 2020; Shen and Raksincharoensak, 2021a; Yang et al, 2021; Yang et al, 2019; Shen et al, 2021b) It can finish pre-training of a model in the source domain with sufficient data and transfer the pre-training model to the target domain after fine-tuning. The second part is to build the wind farm power prediction model in the target domain, and the data pre-processing is the same as the first part. The second part uses the Bi-GRU prediction model and TL method to predict the power of wind farms in the target domain.
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