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Authors: Jannes Stubbemann ; Nils Andre Treiber and Oliver Kramer

Affiliation: University of Oldenburg, Germany

Keyword(s): Neural Networks, Resilient Propagation, Wind Power Prediction.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Economics, Business and Forecasting Applications ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Regression ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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.

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Paper citation in several formats:
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; ISSN 2184-4313, SciTePress, pages 333-337. DOI: 10.5220/0005284403330337

@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},
issn={2184-4313},
}

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
IS - 2184-4313
AU - Stubbemann, J.
AU - Andre Treiber, N.
AU - Kramer, O.
PY - 2015
SP - 333
EP - 337
DO - 10.5220/0005284403330337
PB - SciTePress

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