Medium- and Long-Term Wind Speed Prediction Using the Multi-task Learning Paradigm
Antonio Manuel Gómez-Orellana 
Víctor Manuel Vargas-Yun 
David Guijo-Rubio 
Jorge Pérez-Aracil 
Pedro Antonio Gutiérrez 
Sancho Salcedo-Sanz 
César Hervás-Martínez Bioinspired systems for translational applications: From robotics to social engineering
Abstract
Renewable energies, particularly wind energy, have gain significant attention due to their clean and inexhaustible nature. Despite their commendable efficiency and minimal environmental impact, wind energy faces challenges such as stochasticity and intermittence. Machine learning methods offer a promising avenue for mitigating these challenges, particularly through wind speed prediction, which is crucial for optimising wind turbine performance. One important aspect to consider, regardless of the methodology employed and the approach used to tackle the wind speed prediction problem, is the prediction horizon. Most of the works in the literature have been designed to deal with a single prediction horizon. However, in this study, we propose a multi-task learning framework capable of simultaneously handling various prediction horizons. For this purpose, Artificial Neural Networks (ANNs) are considered, specifically a multilayer perceptron. Our study focuses on medium- and long-term prediction horizons (6 h, 12 h, and 24 h ahead), using wind speed data collected over ten years from a Spanish wind farm, along with ERA5 reanalysis variables that serve as input for the wind speed prediction. The results obtained indicate that the proposed multi-task model performing the three prediction horizons simultaneously can achieve comparable performance to corresponding single-task models while offering simplicity in terms of lower complexity, which includes the number of neurons and links, as well as computational resources.
Keywords
BibTex Citation
@inproceedings{Gomez2024Medium,
author = {G{\' o}mez-Orellana, Antonio Manuel and Vargas-Yun, V{\' i}ctor Manuel and Guijo-Rubio, David and P{\' e}rez-Aracil, Jorge and Guti{\' e}rrez, Pedro Antonio and Salcedo-Sanz, Sancho and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar},
booktitle = {Bioinspired systems for translational applications: From robotics to social engineering},
doi = {10.1007/978-3-031-61137-7_27},
year = {2024},
pages = {293--302},
title = {Medium- and {Long}-{Term} {Wind} {Speed} {Prediction} {Using} the {Multi}-task {Learning} {Paradigm}},
url = {https://link.springer.com/chapter/10.1007/978-3-031-61137-7_27},
howpublished = {https://link.springer.com/chapter/10.1007/978-3-031-61137-7\textunderscore{}27},
volume = {14675},
}
BibTex Unicode Citation
@inproceedings{Gomez2024Medium,
author = {Gómez-Orellana, Antonio Manuel and Vargas-Yun, Víctor Manuel and Guijo-Rubio, David and Pérez-Aracil, Jorge and Gutiérrez, Pedro Antonio and Salcedo-Sanz, Sancho and Hervás-Martínez, César},
booktitle = {Bioinspired systems for translational applications: From robotics to social engineering},
doi = {10.1007/978-3-031-61137-7_27},
year = {2024},
pages = {293--302},
title = {Medium- and {Long}-{Term} {Wind} {Speed} {Prediction} {Using} the {Multi}-task {Learning} {Paradigm}},
url = {https://link.springer.com/chapter/10.1007/978-3-031-61137-7_27},
howpublished = {https://link.springer.com/chapter/10.1007/978-3-031-61137-7\textunderscore{}27},
volume = {14675},
}
APA Citation
Gómez-Orellana, A. M., Vargas-Yun, V. M., Guijo-Rubio, D., Pérez-Aracil, J., Gutiérrez, P. A., Salcedo-Sanz, S., & Hervás-Martínez, C. (2024). Medium- and Long-Term Wind Speed Prediction Using the Multi-task Learning Paradigm. Bioinspired Systems for Translational Applications: From Robotics to Social Engineering, 14675, 293–302. https://doi.org/10.1007/978-3-031-61137-7_27
RIS Citation
TY - CONF
AU - Gómez-Orellana, Antonio Manuel
AU - Vargas-Yun, Víctor Manuel
AU - Guijo-Rubio, David
AU - Pérez-Aracil, Jorge
AU - Gutiérrez, Pedro Antonio
AU - Salcedo-Sanz, Sancho
AU - Hervás-Martínez, César
C3 - Bioinspired systems for translational applications: From robotics to s
ocial engineering
DA - 2024///
C2 - 2024
DO - 10.1007/978-3-031-61137-7_27
ID - temp_id_417211186487
SP - 293-302
TI - Medium- and Long-Term Wind Speed Prediction Using the Multi-task Learn
ing Paradigm
UR - https://link.springer.com/chapter/10.1007/978-3-031-61137-7_27
VL - 14675
ER -
CV Citation
A.M. Gómez-Orellana, V.M. Vargas-Yun, D. Guijo-Rubio (CA), J. Pérez-Aracil, P.A. Gutiérrez, S. Salcedo-Sanz, C. Hervás-Martínez (2/7). "Medium- and Long-Term Wind Speed Prediction Using the Multi-task Learning Paradigm". Bioinspired systems for translational applications: From robotics to social engineering, pp. 293–302, 2024.