Short‐term wind power prediction based on stacked denoised auto‐encoder deep learning and multi‐level transfer learning

Abstract

Abstract Wind power prediction (WPP) has an important impact on the security and reliability operations of the power grid. The major difficulty in power prediction of new, expanded, or reconstructed wind farms is the lack of operational data, which leads to insufficient training of the model and makes the prediction error of wind power become enormous. A short‐term WPP model based on stacked denoised auto‐encoder (SDAE) deep learning and multilevel transfer learning is proposed in this paper. First, the correlation coefficient between the samples of source wind farms and the target wind farm is calculated by using a network trained with the samples from the target wind farm. Second, the samples with high correlation coefficients in source wind farms are graded and migrated to the target wind farm to assist multilevel transfer learning. Finally, the samples from different grades are each used to train a layer of SDAE, and their weights and thresholds are migrated to the final network. The case study shows that the 24‐h‐day‐ahead normalized root‐mean‐square error (NRMSE) and 96‐h‐short‐term NRMSE obtained by the proposed method are 4.48% and 5.11% lower, respectively, compared with the model without transfer learning, which proves the effectiveness of the proposed model.

Keywords

• deep learning
• model migration
• sample correlation
• SDAE
• transfer learning
• wind power prediction