On the selection of bivariate parametric models for wind data

Soukissian, Takvor H.; Karathanasi, Flora

doi:10.1016/j.apenergy.2016.11.097

Cited by 54 publications

(42 citation statements)

References 79 publications

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“…Obviously, for the construction of environmental contours, there will be a need for a joint model for circular‐linear data, and different proposals have recently been made for such data, eg, based on a conditional modelling approach, or based on copula‐like constructions involving a circular joining density; see also previous studies . Model selection for several copula‐based joint circular‐linear models for wind data is discussed in Soukissian and Karathanasi, and it is reported that models using the circular joining density proposed by Johnson and Wehrly perform well. Non‐parametric joint models for circular‐linear variables based on kernel density estimation are presented in Han et al and represent yet another alternative for modelling such data.…”

Section: Joint Distribution Of Wind Speed and Directionmentioning

confidence: 99%

Environmental contours for circular‐linear variables based on the direct sampling method

2019

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Environmental contours are often used in the design of engineering structures to identify extreme environmental conditions that may give rise to extreme loads and responses. The perhaps most common application of environmental contours is for wave climate variables such as significant wave height and wave period. However, for the design of wind energy installations, the joint distribution of wind speed and wind direction may be equally important. In this case, joint modelling of linear (wind speed) and circular (wind direction) variables are needed, and methods for establishing environmental contours for circular-linear variables will be required.In this paper, different ways of establishing environmental contours for circular-linear variables will be presented and applied to a joint distribution model for wind speed and wind direction.In particular, the direct sampling approach to environmental contours will be modified to the case where one of the variables is cyclic. In addition, contours based on exceedance planes in polar coordinates will be established, and circular-linear contours will also be calculated based on the inverse FORM (I-FORM) approach.

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Section: Joint Distribution Of Wind Speed and Directionmentioning

confidence: 99%

Environmental contours for circular‐linear variables based on the direct sampling method

2019

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“…This joint modeling of wind speed and direction is important for climatology and a variety of ocean engineering and ecological applications. Further emphasis on the necessity of bivariate modeling of wind speed and wind direction was raised by [1]. One approach to capture both the linear and directional variables is by means of a disc.…”

Section: Introductionmentioning

confidence: 99%

“…An advantage of the proposed model is that it extends the shape characteristics of the model on the unit disc proposed by Jones [3] to account for more flexibility. This approach to modeling wind data differs from those in [1,[11][12][13][14][15], by means of the correlation structure and method for obtaining the joint distribution. The aforementioned models have been built by means of: (i) the Johnson-Wehrly (JW) [7] method; and (ii) a copula approach where the univariate models of best fit are chosen to obtain a joint bivariate distribution for the wind speed and wind direction.…”

Section: Introductionmentioning

confidence: 99%

“…It was pointed out in [16] that the joint modeling of wind speed and wind direction is more significant than a univariate analysis of the wind speed. The univariate distribution of the wind speed or wind direction is not considered since Soukissian, et al [1] pointed out that the best univariate distribution for wind speed does not provide the best bivariate fit for the wind speed and wind direction. There has been much attention of wind power assessment worldwide, however, there has been no analysis of the wind speed and wind direction analysis at Marion Island.…”

Section: Introductionmentioning

confidence: 99%

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From Symmetry to Asymmetry on the Disc Manifold: Modeling of Marion Island Data

et al. 2019

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The joint modeling of angular and linear observations is crucial as data of this nature are prevalent in multiple disciplines, for example the joint modeling of wind direction and another climatological variable such as wind speed or air temperature, the direction an animal moves and the distance moved, or wave direction and wave height. Hence, there is a need for developing flexible distributions on the hyper-disc, which has support of the interior of the hyper-sphere, as it allows for modeling the combination of angular and linear observations. This paper addresses this need by developing flexible distributions for the disc that have the ability to capture any inherent bimodality present in the data. A new class of bivariate distributions is proposed which has support on the unit disc in two dimensions that includes, as a special case, the existing Möbius distribution on the disc. This class is obtained by expressing the density function in a general form using a measurable function termed as generator. Special cases of this generator are considered to demonstrate the flexibility. By applying a conformal mapping to the generator function a new Möbius distribution class emanates. This class of bivariate distributions on the disc is the first to account for bimodality and skewness present in the data. The flexible behavior of the proposed models in terms of bimodality and skewness is graphically demonstrated. Preliminary evidential analysis of the wind data observed at Marion Island reveals the absence of unimodality in the data. The fit of the proposed models, which account for bimodality, to the Marion Island wind data were evaluated analytically and visually.

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“…Recently there is an important study relating to the dependence function in renewable wind energy. For example, [9] examines another type of copulas families to handle the dependency between wind speed and their directions [10] utilize a pair copula (conditional dependence) to analyze the correlation between the winds farms, while [11] use the extremes value theory and copulas function to determines the correlation between wind turbines that compose the wind farm. Others [12] examine the dependence between wind power production and electricity prices.…”

Section: Introductionmentioning

confidence: 99%

Modeling Wind Energy Using Copula

Bahraoui¹,

Bahraoui²,

Bahraoui³

2018

OALib

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In most studies related to wind energy, the quantity of the air density is considered constant, but actually, we know that it is variable and depending on others natural factors. We present a new procedure to estimate the wind density power energy by simulating the components of the air density. The procedure uses the copula theory and demonstrates that the estimated power energy is higher if the air density is not constant.

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On the selection of bivariate parametric models for wind data

Cited by 54 publications

References 79 publications

Environmental contours for circular‐linear variables based on the direct sampling method

Environmental contours for circular‐linear variables based on the direct sampling method

From Symmetry to Asymmetry on the Disc Manifold: Modeling of Marion Island Data

Modeling Wind Energy Using Copula

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