Fitting Compound Archimedean Copulas to Data for Modeling Electricity Demand

Kelner, Moshe; Landsman, Zinoviy; Makov, Udi

doi:10.5539/ijsp.v10n5p20

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used a Box-Cox transformation for the load data adaptation and a copula function to characterize the relation between peak demand and temperature. Reference [22] modeled the relationship between minimum daily electricity demand and maximum daily temperature and used a multi-parameter compound Archimedean copula with a flexible dependence structure, which increased the flexibility of the dependence structure and improved the fit to the data. Reference [23] evaluated peak electricity demand using Gaussian mixture models and copula functions and provided a time-correlated statistical model.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Peak Demand Estimation Using Members of the Clayton Generalized Gamma Copula Family

2022

Self Cite

View full text Add to dashboard Cite

Climate change impacts many aspects of life and requires innovative thinking on various issues. The electricity sector is affected in several ways, including changes in the production components and consumption patterns. One of the most important issues for Independent System Operators, a state-controlled organization responsible for ensuring the reliability, availability, and quality of electricity delivery in the country, is the response to climate change. This is reflected in the appropriate design of production units to cope with the increase in demand due to extreme heat and cold events and the development of models aimed at predicting the probability of such events. In our work, we address this challenge by proposing a novel probability model for peak demand as a function of wet temperature (henceforth simply temperature), which is a weighting of temperature and humidity. We study the relationship between peak demand and temperature using a new Archimedean copula family, shown to be effective for this purpose. This family, the Clayton generalized Gamma, is a multi-parameter copula function that comprises several members. Two new measures of fit, an economic measure and a conditional coverage measure, were introduced to select the most appropriate family member based on the empirical data of daily peak demand and minimum temperature in the winter. The Clayton Gamma copula showed the lowest cost measure and the best conditional coverage and was, therefore, proven to be the most appropriate member of the family.

show abstract

Section: Introductionmentioning

confidence: 99%