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2018 | OriginalPaper | Chapter

A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms

Authors : L. Cornejo-Bueno, J. Acevedo-Rodríguez, L. Prieto, S. Salcedo-Sanz

Published in: Intelligent Distributed Computing XII

Publisher: Springer International Publishing

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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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previous chapter Drift Detection over Non-stationary Data Streams Using Evolving Spiking Neural Networks
next chapter Bio-inspired Approximation to MPPT Under Real Irradiation Conditions
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Metadata
Title
A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms
Authors
L. Cornejo-Bueno
J. Acevedo-Rodríguez
L. Prieto
S. Salcedo-Sanz
Copyright Year
2018
Book
Intelligent Distributed Computing XII

Print ISBN: 978-3-319-99625-7

Electronic ISBN: 978-3-319-99626-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-99626-4_9

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