Abstract
While wind is an abundant source of energy, integrating wind power into existing electricity grids is a major challenge due to its inherent variability. The ability to accurately predict future generation output would greatly mitigate this problem and is thus extremely valuable. Numerical Weather Prediction (NWP) techniques have been the basis of many wind prediction approaches, but the use of machine learning techniques is steadily gaining ground. Deep Learning (DL) is a sub-class of machine learning which has been particularly successful and is now the state of the art for a variety of classification and regression problems, notably image processing and natural language processing. In this paper, we demonstrate the use of Recurrent Neural Networks, a type of DL architecture, to extract patterns from the spatio-temporal information collected from neighboring turbines. These are used to generate short term wind energy forecasts which are then benchmarked against various prediction algorithms. The results show significant improvements over forecasts produced using state of the art algorithms.
Similar content being viewed by others
References
Kramer, O., Gieseke, F., Satzger, B.: Wind energy prediction and monitoring with neural computation. Neurocomputing 109, 84–93 (2013)
Woon, W.L., Kramer, O.: Enhanced SVR ensembles for wind power prediction. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2743–2748. IEEE (2016)
Heinermann, J., Kramer, O.: Machine learning ensembles for wind power prediction. Renew. Energy 89, 671–679 (2016)
Dalto, M., Matuško, J., Vašak, M.: Deep neural networks for ultra-short-term wind forecasting. In: 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 1657–1663. IEEE (2015)
Felder, M., Kaifel, A., Graves, A.: Wind power prediction using mixture density recurrent neural networks. In: Poster Presentation gehalten auf der European Wind Energy Conference (2010)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Baets, L.D., Ruyssinck, J., Peiffer, T., Decruyenaere, J., Turck, F.D., Ongenae, F., Dhaene, T.: Positive blood culture detection in time series data using a BiLSTM network. CoRR abs/1612.00962 (2016). http://arxiv.org/abs/1612.00962
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, vol. 2. Springer, New York (2009). doi:10.1007/978-0-387-84858-7
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Woon, W.L., Oehmcke, S., Kramer, O. (2017). Spatio-Temporal Wind Power Prediction Using Recurrent Neural Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-70139-4_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70138-7
Online ISBN: 978-3-319-70139-4
eBook Packages: Computer ScienceComputer Science (R0)