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.

References

  1. Castillo, P. A., Merelo, J. J., Prieto, A., Rivas, V., and Romero, G. (2000). G-prop: Global optimization of multilayer perceptrons using GAs. Neurocomputing, 35:149-163.
  2. Catalao, J. P. S., Pousinho, H. M. I., and Mendes, V. M. F. (2009). An artificial neural network approach for short-term wind power forecasting in portugal. In Intelligent System Applications to Power Systems, 2009. ISAP 7809. 15th International Conference on, pages 1- 5.
  3. Han, S., Liu, Y., and Yan, J. (2011). Neural network ensemble method study for wind power prediction. In Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific, pages 1-4.
  4. Heinermann, J. and Kramer, O. (2014). Precise wind power prediction with SVM ensemble regression. In Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15- 19, 2014. Proceedings, pages 797-804.
  5. Igel, C. and Hüsken, M. (2003). Empirical evaluation of the improved rprop learning algorithm. Neurocomputing, 50:105-123.
  6. Kramer, O., Gieseke, F., and Satzger, B. (2013). Wind energy prediction and monitoring with neural computation. Neurocomputing, 109:84-93.
  7. Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., and Yan, Z. (2009). A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews, 13(4):915 - 920.
  8. Mohamed A. Mohandes, S. R. and Halawani, T. O. (1998). A neural networks approach for wind speed prediction. Renewable Energy, 13(3):345 - 354.
  9. Riedmiller, M. and Braun, H. (1992). Rprop - a fast adaptive learning algorithm. Proc. of ISCIS, VII.
  10. Rumelhart, D., Hintont, G., and Williams, R. (1986). Learning representations by back-propagating errors. Nature, 323:533-536.
  11. Treiber, N. A., Heinermann, J., and Kramer, O. (2013). Aggregation of features for wind energy prediction with support vector regression and nearest neighbors. In European Conference on Machine Learning, DARE Workshop.
  12. Treiber, N. A. and Kramer, O. (2014). Evolutionary turbine selection for wind power predictions. In KI 2014: Advances in Artificial Intelligence - 37th Annual German Conference on AI, Stuttgart, Germany, September 22- 26, 2014. Proceedings, pages 267-272.
Download


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