Wind power forecasting: A transfer learning approach incorporating temporal convolution and adversarial training

Abstract

Accurate forecasting of wind power is crucial for efficiently scheduling power grids that incorporate wind energy. However, distribution instability of time-varying data can undermine the generalization capability of forecasting models in future periods. This paper introduces a transfer learning approach incorporating adversarial training and temporal convolution to address wind power forecasting from the distribution-centric perspective. The proposed approach can be divided into two joint modules: the temporal domain split and domain-adversarial temporal convolutional network. The former segregates training data into temporal domains characterized by the most significant distribution differences, while the latter aims is designed to learn shared knowledge from the segmented temporal domains. The shared knowledge is independent of distribution shifts, which can be generalized to future testing well. Specifically, the domain-adversarial temporal convolutional network is composed of a base model incorporating temporal convolutional network and a domain classifier. Both components are jointly optimized by minimizing power prediction loss and maximizing temporal domain classification loss. The data from actual wind turbines are employed to validate the proposed approach. Experimental results show that the superior performance of the proposed approach compared to benchmark models, achieving a remarkable 24.56% improvement in accuracy by introducing adversarial training.