Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains

Transfer Learning (TL) in Deep Neural Networks is gaining importance because in most of the cases, the labeling of data is costly and time-consuming. Additionally, TL provides effective weight initialization. This paper introduces the idea of Adaptive Transfer Learning in Deep Neural Networks (ATL-DNN) for wind power prediction. Adaptive TL of Deep Neural Networks is proposed, which makes the proposed system an adaptive one as regards training on a different wind farm is concerned. The proposed ATL-DNN technique is tested for short-term wind power predictions, where continuously arriving information has to be exploited. Adaptive TL not only helps in providing good weight initialization, but is also helpful to utilize the online data that is continuously being generated by wind farms. Additionally, the proposed ATL-DNN technique is shown to transfer knowledge between different task domains (wind power to wind speed prediction) and from one region to another region. The simulation results show that proposed ATL-DNN technique achieves average values of 0.0637,0.0986, Keywords ---- Wind power prediction; Adaptive transfer learning; Deep learning; Ensemble learning 1. Introduction Many countries across the world use wind power as a renewable energy resource. Accurate prediction of wind power plays a significant role in generating smooth power from a turbine. There are numerous factors which affect the predicted power of a wind power prediction system, like fluctuation in speed of the wind with respect to time, geographical location, and climatic conditions.