Medium- and Long-Term Wind Speed Prediction Using the Multi-task Learning Paradigm

Áreas de investigación:

• Sin categoría

Año:

2024

Tipo de publicación:

Artículo en conferencia

Palabras clave:

Wind speed, renewable energy, multitask paradigm, medium and long term prediction

Autores:

• Gómez-Orellana, Antonio Manuel
• Vargas, Víctor Manuel
• Guijo-Rubio, David
• Pérez-Aracil, Jorge
• Gutiérrez, Pedro Antonio
• Salcedo-Sanz, Sancho
• Hervás-Martínez, César

Volumen:

14675

Título del libro:

Bioinspired Systems for Translational Applications: From Robotics to Social Engineering

Páginas:

293–302

Mes:

Mayo

ISSN:

1611-3349

BibTex:

@conference{Medium_wind_speed_iwinac_2024, author = "Antonio Manuel G{\'o}mez-Orellana and V{\'i}ctor Manuel Vargas and David Guijo-Rubio and Jorge P{\'e}rez-Aracil and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", 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.", booktitle = "Bioinspired Systems for Translational Applications: From Robotics to Social Engineering", doi = "https://link.springer.com/chapter/10.1007/978-3-031-61137-7_27", issn = "1611-3349", keywords = "Wind speed, renewable energy, multitask paradigm, medium and long term prediction", month = "Mayo", pages = "293–302", title = "{M}edium- and {L}ong-{T}erm {W}ind {S}peed {P}rediction {U}sing the {M}ulti-task {L}earning {P}aradigm", url = "doi.org/10.1007/978-3-031-61137-7_27", volume = "14675", year = "2024", }

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.