Simultaneous multi-step wind speed prediction on multiple farms using multi-task deep learning

Authors

Rafael Ayllón-Gavilán

Antonio Manuel Gómez-Orellana

Víctor Manuel Vargas-Yun

David Guijo-Rubio

Jorge Pérez-Aracil

Sancho Salcedo-Sanz

Pedro Antonio Gutiérrez

César Hervás-Martínez

Published in Journal

Integrated Computer-Aided Engineering

ENGINEERING, MULTIDISCIPLINARY

Impact Factor JCR 2024

5.3

JCR Ranking

22 / 179

Position

ISSN 1069-2509

Vol. 32

No. 4

Pages 366-378

Abstract

In this paper, we present the MUSONet model, which leverages information from different sources (in this case, wind farms) to perform a multi-step wind speed prediction. The main goal of this approach is improving the global prediction accuracy, specifically at longer prediction horizons. Thus, the proposed model is able to simultaneously predict the wind speed at three different prediction horizons (6h, 12h, and 24h), across three different wind farms located in Spain. We also evaluate the performance of the presented methodology by considering three different activation functions for hidden neurons in the neural network: Sigmoid, ReLU, and ELUs+2L. The results show that the proposed multi-source approach improves the performance of the single-source counterpart for the longer prediction horizons (12h and 24h). In addition, the proposed multi-source method reduces by over 30

Keywords

deep neural networks multi-task algorithms wind farms wind speed prediction

BibTex Citation

@article{Ayllon2025Simultaneous,
	author = {Ayll{\' o}n-Gavil{\' a}n, Rafael and G{\' o}mez-Orellana, Antonio Manuel and Vargas-Yun, V{\' i}ctor Manuel and Guijo-Rubio, David and P{\' e}rez-Aracil, Jorge and Salcedo-Sanz, Sancho and Guti{\' e}rrez, Pedro Antonio and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar},
	journal = {Integrated Computer-Aided Engineering},
	doi = {10.1177/10692509251337224},
	number = {4},
	year = {2025},
	pages = {366--378},
	title = {Simultaneous multi-step wind speed prediction on multiple farms using multi-task deep learning},
	url = {https://doi.org/10.1177/10692509251337224},
	howpublished = {https://doi.org/10.1177/10692509251337224},
	volume = {32},
}

BibTex Unicode Citation

@article{Ayllon2025Simultaneous,
	author = {Ayllón-Gavilán, Rafael and Gómez-Orellana, Antonio Manuel and Vargas-Yun, Víctor Manuel and Guijo-Rubio, David and Pérez-Aracil, Jorge and Salcedo-Sanz, Sancho and Gutiérrez, Pedro Antonio and Hervás-Martínez, César},
	journal = {Integrated Computer-Aided Engineering},
	doi = {10.1177/10692509251337224},
	number = {4},
	year = {2025},
	pages = {366--378},
	title = {Simultaneous multi-step wind speed prediction on multiple farms using multi-task deep learning},
	url = {https://doi.org/10.1177/10692509251337224},
	howpublished = {https://doi.org/10.1177/10692509251337224},
	volume = {32},
}

APA Citation

Ayllón-Gavilán, R., Gómez-Orellana, A. M., Vargas-Yun, V. M., Guijo-Rubio, D., Pérez-Aracil, J., Salcedo-Sanz, S., Gutiérrez, P. A., & Hervás-Martínez, C. (2025). Simultaneous multi-step wind speed prediction on multiple farms using multi-task deep learning. Integrated Computer-Aided Engineering, 32(4), 366–378. https://doi.org/10.1177/10692509251337224

RIS Citation

TY  - JOUR
AU  - Ayllón-Gavilán, Rafael
AU  - Gómez-Orellana, Antonio Manuel
AU  - Vargas-Yun, Víctor Manuel
AU  - Guijo-Rubio, David
AU  - Pérez-Aracil, Jorge
AU  - Salcedo-Sanz, Sancho
AU  - Gutiérrez, Pedro Antonio
AU  - Hervás-Martínez, César
DA  - 2025///
PY  - 2025
DO  - 10.1177/10692509251337224
ID  - temp_id_669912994845
IS  - 4
SP  - 366-378
T2  - Integrated Computer-Aided Engineering
TI  - Simultaneous multi-step wind speed prediction on multiple farms using 
multi-task deep learning
UR  - https://doi.org/10.1177/10692509251337224
VL  - 32
ER  -

CV Citation

R. Ayllón-Gavilán, A.M. Gómez-Orellana, V.M. Vargas-Yun, D. Guijo-Rubio, J. Pérez-Aracil, S. Salcedo-Sanz (CA), P.A. Gutiérrez, C. Hervás-Martínez (3/8). "Simultaneous multi-step wind speed prediction on multiple farms using multi-task deep learning". Integrated Computer-Aided Engineering,  Vol. 32(4), pp. 366-378, 2025. (Q1, IF: 5.3).