LAPSE:2023.25205
Published Article
LAPSE:2023.25205
Extremal Dependence Modelling of Global Horizontal Irradiance with Temperature and Humidity: An Application Using South African Data
March 28, 2023
Abstract
The interaction between global horizontal irradiance (GHI) and temperature helps determine the maximum amount of solar power generated. As temperature increases, GHI increases up to the point that it increases at a decreasing rate and then decreases. Therefore, system operators need to know the maximum possible solar power which can be generated. Using the multivariate adaptive regression splines, extreme value theory and copula models, the present paper seeks to determine the maximum temperature that will result in the generation of the maximum GHI ceteris paribus. The paper also discusses extremal dependence modelling of GHI with temperature and relative humidity (RH) at one radiometric station using South African data from 16 November 2015 to 16 November 2021. Empirical results show that the marginal increases of GHI converge to 0.12 W/m2 when temperature converges to 44.26 °C and the marginal increases of GHI converge to −0.1 W/m2 when RH converges to 103.26%. Conditioning on GHI, the study found that temperature and RH variables have a negative extremal dependence on large values of GHI. Due to the nonlinearity and different structure of the dependence on GHI against temperature and RH, unlike previous literature, we use three Archimedean copula functions: Clayton, Frank and Gumbel, to model the dependence structure. The modelling approach discussed in this paper could be useful to system operators in power utilities who must optimally integrate highly intermittent renewable energies on the grid.
Keywords
archimedean copulas, bivariate threshold excess model, conditional multivariate extreme value, extreme value mixture model, global horizontal irradiance, laplace margins
Suggested Citation
Sigauke C, Ravele T, Jhamba L. Extremal Dependence Modelling of Global Horizontal Irradiance with Temperature and Humidity: An Application Using South African Data. (2023). LAPSE:2023.25205
Author Affiliations
Sigauke C: Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa [ORCID]
Ravele T: Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa [ORCID]
Jhamba L: Department of Physics, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa
Journal Name
Energies
Volume
15
Issue
16
First Page
5965
Year
2022
Publication Date
2022-08-17
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15165965, Publication Type: Journal Article
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LAPSE:2023.25205
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https://doi.org/10.3390/en15165965
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