A GA-Based Approach for Building Regularized Sparse Polynomial Models for Wind Turbine Power Curves

Haroldo C. Maya; Guilherme A. Barreto

doi:10.5753/eniac.2018.4455

Haroldo C. Maya UFC
Guilherme A. Barreto UFC

DOI: https://doi.org/10.5753/eniac.2018.4455

Resumo

In this paper, the classical polynomial model for wind turbines power curve estimation is revisited aiming at an automatic and parsimonious design. In this regard, using genetic algorithms we introduce a methodoloy for estimating a suitable order for the polynomial as well its relevant terms. The proposed methodology is compared with the state of the art in estimating the power curve of wind turbines, such as logistic models (with 4 and 5 parameters), artificial neural networks and weighted polynomial regression. We also show that the proposed approach performs better than the standard LASSO approach for building regularized sparse models. The results indicate that the proposed methodology consistently outperforms all the evaluated alternative methods.

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