Modelling wind speed with a univariate probability distribution depending on two baseline functions

Frank Gomes-silva

Makale detayları

Modelling wind speed with a univariate probability distribution depending on two baseline functions

Yazarlar:

Frank Gomes-silva

Yayıncı:

Hacettepe University

Ana Başlıklar:

Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 3, pp.808-827

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290026

pISSN:

2651-477X

Anahtar kelimeler:

Özet:

 Characterizing the wind speed distribution properly is essential for the satisfactory production of potential energy in wind farms, being the mixture models usually employed in the description of such data. However, some mixture models commonly have the undesirable property of non-identifiability. In this work, we present an alternative distribution which is able to fit the wind speed data decently. The new model, called Normal-Weibull-Weibull, is identifiable and its cumulative distribution function is written as a composition of two baseline functions. We discuss structural properties of the class that generates the proposed model, such as the linear representation of the probability density function, moments and moment generating function. We perform a Monte Carlo simulation study to investigate the behavior of the maximum likelihood estimates of the parameters. Finally, we present applications of the new distribution for modelling wind speed data measured in five different cities of the Northeastern Region of Brazil.

 Characterizing the wind speed distribution properly is essential for the satisfactory production of potential energy in wind farms, being the mixture models usually employed in the description of such data. However, some mixture models commonly have the undesirable property of non-identifiability. In this work, we present an alternative distribution which is able to fit the wind speed data decently. The new model, called Normal-Weibull-Weibull, is identifiable and its cumulative distribution function is written as a composition of two baseline functions. We discuss structural properties of the class that generates the proposed model, such as the linear representation of the probability density function, moments and moment generating function. We perform a Monte Carlo simulation study to investigate the behavior of the maximum likelihood estimates of the parameters. Finally, we present applications of the new distribution for modelling wind speed data measured in five different cities of the Northeastern Region of Brazil.

 Characterizing the wind speed distribution properly is essential for the satisfactory production of potential energy in wind farms, being the mixture models usually employed in the description of such data. However, some mixture models commonly have the undesirable property of non-identifiability. In this work, we present an alternative distribution which is able to fit the wind speed data decently. The new model, called Normal-Weibull-Weibull, is identifiable and its cumulative distribution function is written as a composition of two baseline functions. We discuss structural properties of the class that generates the proposed model, such as the linear representation of the probability density function, moments and moment generating function. We perform a Monte Carlo simulation study to investigate the behavior of the maximum likelihood estimates of the parameters. Finally, we present applications of the new distribution for modelling wind speed data measured in five different cities of the Northeastern Region of Brazil.

 Characterizing the wind speed distribution properly is essential for the satisfactory production of potential energy in wind farms, being the mixture models usually employed in the description of such data. However, some mixture models commonly have the undesirable property of non-identifiability. In this work, we present an alternative distribution which is able to fit the wind speed data decently. The new model, called Normal-Weibull-Weibull, is identifiable and its cumulative distribution function is written as a composition of two baseline functions. We discuss structural properties of the class that generates the proposed model, such as the linear representation of the probability density function, moments and moment generating function. We perform a Monte Carlo simulation study to investigate the behavior of the maximum likelihood estimates of the parameters. Finally, we present applications of the new distribution for modelling wind speed data measured in five different cities of the Northeastern Region of Brazil.

Makale detayları

Modelling wind speed with a univariate probability distribution depending on two baseline functions

Yazarlar:

Frank Gomes-silva

Yayıncı:

Hacettepe University

Ana Başlıklar:

Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 3, pp.808-827

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290026

pISSN:

2651-477X

Goodness-of-fit

identifiability

L-BFGS-B algorithm

maximum likelihood

Monte Carlo simulation

Sayı

Makale detayları

Modelling wind speed with a univariate probability distribution depending on two baseline functions

Yazarlar: Frank Gomes-silva

Yayıncı: Hacettepe University

Ana Başlıklar: Statistics

Dergi: Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 3, pp.808-827

Belge Tipi: Makale

Yayın Tarihi: 5/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001290026

pISSN: 2651-477X

Goodness-of-fit

identifiability

L-BFGS-B algorithm

maximum likelihood

Monte Carlo simulation

Sayı

Yazarlar:

Frank Gomes-silva

Yayıncı:

Hacettepe University

Ana Başlıklar:

Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 3, pp.808-827

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290026

pISSN:

2651-477X