Probabilistic wind power forecasting combining deep learning architectures

Probabilistic wind power forecasting combining deep learning architectures

Arends, Eric Lacoa (Student TU Delft)
Watson, S.J. (TU Delft Wind Energy)
Basu, S. (TU Delft Atmospheric Remote Sensing)
Cheneka, B.R. (TU Delft Wind Energy)

2020

A series of probabilistic models were bench-marked during the European Energy Markets forecasting Competition 2020 to assess their relative accuracy in predicting aggregated Swedish wind power generation using as input historic weather forecasts from a numerical weather prediction model. In this paper, we report the results of one of these models which uses a deep learning approach integrating two architectures: (a) Convolutional Neural Network (CNN) LeNet-5 based architectrure; (b) Multi-Layer Perceptron (MLP) architecture -with two hidden layers-. These are concatenated into the Smooth Pinball Neural Network (SPNN) framework for quantile regression. Hyperparameters were optimised to produce the best model for every region. When tuned, the re-forecasts from the model performed favorably compared to other machine learning approaches and showed significant improvement on the original competition results, though failed to fully capture spatial patterns in certain cases when compared to other methods.

convolutional neural network
multilayer perceptron
numerical weather prediction
smooth pinball neural network
wind power forecasting

http://resolver.tudelft.nl/uuid:152addea-c35e-4237-9062-a107c721658e

https://doi.org/10.1109/EEM49802.2020.9221929

IEEE

9781728169194

2020 17th International Conference on the European Energy Market, EEM 2020

17th International Conference on the European Energy Market, EEM 2020, 2020-09-16 → 2020-09-18, Stockholm, Sweden

International Conference on the European Energy Market, EEM, 2165-4077, 2020-September

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2020 Eric Lacoa Arends, S.J. Watson, S. Basu, B.R. Cheneka