Resilient Propagation for Multivariate Wind Power Prediction

Jannes Stubbemann, Nils Andre Treiber, Oliver Kramer

2015

Abstract

Wind power prediction based on statistical learning has the potential to outperform classical physical weather prediction models. Neural networks have been successfully applied to wind prediction in the past. In this paper, we apply neural networks to the spatio-temporal prediction model we proposed in the past. We concentrate on a comparison between classical backpropagation and the more advanced resilient propagation (RPROP) variants. The analysis is based on time series data from the NREL western wind data set. The experimental results show that RPROP+ and iRPROP+ significantly outperform the classical backpropagation variants.

Paper Citation

in Harvard Style

Stubbemann J., Andre Treiber N. and Kramer O. (2015). Resilient Propagation for Multivariate Wind Power Prediction . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 333-337. DOI: 10.5220/0005284403330337

in Bibtex Style

@conference{icpram15,
author={Jannes Stubbemann and Nils Andre Treiber and Oliver Kramer},
title={Resilient Propagation for Multivariate Wind Power Prediction},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={333-337},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005284403330337},
isbn={978-989-758-077-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - Resilient Propagation for Multivariate Wind Power Prediction
SN - 978-989-758-077-2
AU - Stubbemann J.
AU - Andre Treiber N.
AU - Kramer O.
PY - 2015
SP - 333
EP - 337
DO - 10.5220/0005284403330337