  Transfer Learning (TL) in Deep Neural Networks is gaining importance because
in most of the applications, the labeling of data is costly and time-consuming.
Additionally, TL also provides an effective weight initialization strategy for
Deep Neural Networks . This paper introduces the idea of Adaptive Transfer
Learning in Deep Neural Networks (ATL-DNN) for wind power prediction.
Specifically, we show in case of wind power prediction that adaptive TL of Deep
Neural Networks system can be adaptively modified as regards training on a
different wind farm is concerned. The proposed ATL-DNN technique is tested for
short-term wind power prediction, 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 incoming data for effective
learning. 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 the
proposed ATL-DNN technique achieves average values of 0.0637,0.0986, and 0.0984
for the Mean-Absolute-Error, Root-Mean-Squared-Error, and
Standard-Deviation-Error, respectively.
