@article{schreiber2023model,
  abstract = {There is recent interest in using model hubs – a collection of pre-trained models – in computer vision tasks. To employ a model hub, we first select a source model and then adapt the model for the target to compensate for differences. There still needs to be more research on model selection and adaption for renewable power forecasts. In particular, none of the related work examines different model selection and adaptation strategies for neural network architectures. Also, none of the current studies investigates the influence of available training samples and considers seasonality in the evaluation. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent developments from the field of computer vision on 667 wind and photovoltaic parks from six datasets. We simulate different amounts of training samples for each season to calculate informative forecast errors. We examine the marginal likelihood and forecast error for model selection for those amounts. Furthermore, we study four adaption strategies. 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 and shows that fine-tuning is not beneficial with less than three months of data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach such that we have a similar mean error with only 30 days of training data which is otherwise only possible with an entire year of training data. We achieve a mean error of 9.8 and 14 percent for the most realistic dataset for PV and wind with only seven days of training data.},
  author = {Schreiber, Jens and Sick, Bernhard},
  doi = {10.1016/j.egyai.2023.100249},
  interhash = {961108a1b9cc1ea342cbbcd37215fe9d},
  intrahash = {f3749c7578008290d2bfd41e406aba23},
  journal = {Energy and AI},
  pages = 100249,
  title = {Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts},
  url = {https://www.sciencedirect.com/science/article/pii/S2666546823000216},
  volume = 14,
  year = 2023
}

@article{schreiber2022multi,
  abstract = {Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts.},
  author = {Schreiber, Jens and Sick, Bernhard},
  doi = {10.3390/en15218062},
  interhash = {ba8256fd79524715c235461c0870a820},
  intrahash = {9d715e9d588fb03a0a5294ea7ad95692},
  journal = {Energies},
  number = 21,
  pages = 8062,
  publisher = {MDPI},
  title = {Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts},
  volume = 15,
  year = 2022
}

@inproceedings{SVS21,
  author = {Schreiber, Jens and Vogt, Stephan and Sick, Bernhard},
  booktitle = {ECML PKDD 2021},
  interhash = {57589fa0b63324bff67e3ad9117986ba},
  intrahash = {c569956eab75964c4a49f6d43d4552ce},
  note = {(accepted)},
  title = {Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast},
  year = 2021
}