Abstract
Renewable energy sources are increasing their importance and presence in the energy production sector due to their null or almost null greenhouse gas emissions. However, the main disadvantage of this type of system is the dependence on weather conditions. This research proposes using a virtual sensor to measure the wind speed 10 m above ground through variables measured at ground level. The modeling process followed a feature selection step before applying four machine learning techniques. The implemented virtual sensor accurately estimated the wind speed at 10 m, an interesting tool to eliminate the physical sensor’s buy, installation, and maintenance.
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Acknowledgments
Antonio Díaz-Longueira’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to Ph.D. (http://gain.xunta.gal), under the “Axudas á etapa predoutoral” grant with reference: ED481A2023072.
Míriam Timiraos’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to industrial Ph.D. (http://gain.xunta.gal), under the Doutoramento Industrial 2022 grant with reference: \(04\_IN606D\_2022\_2692965\)
This work has been supported by Centro Mixto de Investigación UDC-NAVANTIA (IN853C 2022/01), funded by GAIN (Xunta de Galicia) and ERDF Galicia 2021–2027.
Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the “Formación de Profesorado Universitario” grant with reference FPU21/00932.
Xunta de Galicia. Grants for the consolidation and structuring of competitive research units, GPC (ED431B 2023/49)
CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021-27 operational program (Ref. ED431G 2023/01).
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Díaz-Longueira, A., Arcano-Bea, P., Timiraos, M., Michelena, Á., de Cos Juez, F.J., Calvo-Rolle, J.L. (2024). Wind Speed Virtual Sensor for Small Wind Turbine. In: Zayas-Gato, F., Díaz-Longueira, A., Casteleiro-Roca, JL., Jove, E. (eds) Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applications, 21st International Conference. DCAI 2024. Lecture Notes in Networks and Systems, vol 1173. Springer, Cham. https://doi.org/10.1007/978-3-031-73910-1_8
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