Abstract
Green energy generation is increasing its presence in individual and large-scale electrical networks, due to the need to reduce the emission of greenhouse gases. This study performs a comparative study between different regression models, linear and non-linear, with the objective of determining the best method for the prediction of the energy generated by a mini wind turbine based on meteorological variables. First, the best configuration for the chosen techniques will be obtained, and then the best models will be compared. These comparisons will be based on different metrics: Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Median Absolute Error and Coefficient of Determination; obtained through a 10-kfold cross-validation.
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Acknowledgements
Míriam Timiraos’s research was supported by the “Xunta de Galicia” (Regional Government of Galicia) through grants to industrial PhD (http://gain.xunta.gal/), under the “Doutoramento Industrial 2022” grant with reference: 04_IN606D_2022_ 2692965.
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.
CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).
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Díaz-Longueira, A. et al. (2023). Comparative Study of Forecasting Techniques for Small Wind Turbine Power Generation by Meteorological Parameters. In: Jove, E., Zayas-Gato, F., Michelena, Á., Calvo-Rolle, J.L. (eds) Distributed Computing and Artificial Intelligence, Special Sessions II - Intelligent Systems Applications, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-031-38616-9_7
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