19 Jul 2018
| 19 Jul 2018
Probabilistic short term wind power forecasts using deep neural networks with discrete target classes
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Cited articles
Delle Monache, L., Nipen, T., Liu, Y., Roux, G., and Stull, R.: Kalman filter
and analog schemes to postprocess numerical weather predictions, Mon.
Weather Rev., 139, 3554–3570, 2011. a
Hersbach, H.: Decomposition of the continuous ranked probability score for
ensemble prediction systems, Weather Forecast., 15, 559–570, 2000. a
Junk, C., Delle Monache, L., Alessandrini, S., Cervone, G., and von Bremen,
L.:
Predictor-weighting strategies for probabilistic wind power forecasting with
an analog ensemble, Meteorol. Z., 24, 361–379, 2015. a
Riedmiller, M. and Braun, H.: A direct adaptive method for faster
backpropagation learning: The RPROP algorithm, in: Proc. of IEEE
International Conference on Neural Networks, IEEE, 28 March–1 April 1993,
San Francisco, CA, USA, 586–591, https://doi.org/10.1109/ICNN.1993.298623, 1993. a
Schmidhuber, J.: Deep learning in neural networks: An overview, Neural
networks, 61, 85–117, 2015. a
