  There is recent interest in using model hubs, a collection of pre-trained
models, in computer vision tasks. To utilize the model hub, we first select a
source model and then adapt the model for the target to compensate for
differences. While there is yet limited research on model selection and
adaption for computer vision tasks, this holds even more for the field of
renewable power. At the same time, it is a crucial challenge to provide
forecasts for the increasing demand for power forecasts based on weather
features from a numerical weather prediction. We close these gaps by conducting
the first thorough experiment for model selection and adaptation for transfer
learning in renewable power forecast, adopting recent results from the field of
computer vision on 667 wind and photovoltaic parks. To the best of our
knowledge, this makes it the most extensive study for transfer learning in
renewable power forecasts reducing the computational effort and improving the
forecast error. Therefore, we adopt source models based on target data from
different seasons and limit the amount of training data. As an extension of the
current state of the art, we utilize a Bayesian linear regression for
forecasting the response based on features extracted from a neural network.
This approach outperforms the baseline with only seven days of training data.
We further show how combining multiple models through ensembles can
significantly improve the model selection and adaptation approach.
