Joint Modeling of Wind Speed and Power via a Nonparametric Approach

Saulo Custodio de Aquino Ferreira, Paula Medina Maçaira and Fernando Luiz Cyrino Oliveira ()
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Saulo Custodio de Aquino Ferreira: Industrial Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22451-040, Brazil
Paula Medina Maçaira: Industrial Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22451-040, Brazil
Fernando Luiz Cyrino Oliveira: Industrial Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22451-040, Brazil

Energies, 2024, vol. 17, issue 14, 1-24

Abstract: Power output from wind turbines is influenced by wind speed, but the traditional theoretical power curve approach introduces uncertainty into wind energy forecasting models. This is because it assumes a consistent power output for a given wind speed. To address this issue, a new nonparametric method has been proposed. It uses K-means clustering to estimate wind speed intervals, applies kernel density estimation (KDE) to establish the probability density function (PDF) for each interval and employs Monte Carlo simulation to predict power output based on the PDF. The method was tested using data from the MERRA-2 database, covering five wind farms in Brazil. The results showed that the new method outperformed the conventional estimation technique, improving estimates by an average of 47 to 49%. This study contributes by (i) proposing a new nonparametric method for modeling the relationship between wind speed and power; (ii) emphasizing the superiority of probabilistic modeling in capturing the natural variability in wind generation; (iii) demonstrating the benefits of temporally segregating data; (iv) highlighting how different wind farms within the same region can have distinct generation profiles due to environmental and technical factors; and (v) underscoring the significance and reliability of the data provided by the MERRA-2 database.

Keywords: wind power; wind speed clustering; kernel density estimation; power curve modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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