Abstract
This paper focuses on a problem of wind speed estimation in wind farms by proposing an ensemble of regressors in which the output of four different systems (Neural Networks (NNs), Suppor Vector Regressors (SVRs) and Gaussian Processes (GPRs)) will be the input of a final prediction system (An Extreme Learning Machine (ELM) in this case). Moreover, we propose to use variables from atmospheric reanalysis data as predictive inputs for the systems, which gives us the possibility of hybridizing numerical weather models with ML techniques for wind speed prediction in real systems. The experimental evaluation of the proposed system in real data from a wind farm in Spain has been carried out, with the subsequent discussion about the performance of the different ML regressors and the ensemble method tested in this wind speed prediction problem.
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Acknowledgements
This work has been partially supported by Comunidad de Madrid, under project number S2013/ICE-2933, and by project TIN2014-54583-C2-2-R of the Spanish Ministerial Commission of Science and Technology (MICYT).
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Cornejo-Bueno, L., Acevedo-Rodríguez, J., Prieto, L., Salcedo-Sanz, S. (2018). A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_9
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