Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training

Accurate forecasting of wind power is of significance for the operation and scheduling of the power grid in which wind power is integrated. However, distribution shifts caused by time-varying data would damage the stable generalization of forecasting models in future testing. In this paper, a temporal domain generalization approach incorporating adversarial relationship-based training and hybrid model is proposed to address wind power forecasting from the distribution perspective. Two joint modules, the temporal domain split and domain-adversarial hybrid model, are employed to function as a forecasting framework. The former splits training data into temporal domains with the maximum distribution differences, while the latter aims to learn shared knowledge from the above domains. The shared knowledge is independent of distribution shifts, which can be generalized to future testing well. Specifically, the domain-adversarial hybrid model, which is jointly optimized by minimizing the loss of power prediction and maximizing the loss of temporal domain classification, can be divided into a base model incorporating temporal convolutional network and gated recurrent unit, and a domain classifier. The data from actual wind turbines are performed to validate the proposed approach. The experimental results show that the proposed approach has superior performance to benchmark models and the significant improvement in accuracy reached 39.48% using adversarial relationship-based training.