“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting

Jaume Manero, Javier Béjar and Ulises Cortés
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Jaume Manero: Universitat Politècnica de Catalunya—BarcelonaTECH, 08034 Barcelona, Spain
Javier Béjar: Universitat Politècnica de Catalunya—BarcelonaTECH, 08034 Barcelona, Spain
Ulises Cortés: Universitat Politècnica de Catalunya—BarcelonaTECH, 08034 Barcelona, Spain

Energies, 2019, vol. 12, issue 12, 1-20

Abstract: To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R ² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.

Keywords: wind energy forecasting; time series; deep learning; RNN; MLP; CNN; wind speed forecasting; wind time series; time series; multi-step forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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