IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2022-04-25.html
   My bibliography  Save this article

Forecasting Uncertainty Intervals for Return Period of Extreme Daily Electricity Consumption

Author

Listed:
  • Katleho Makatjane

    (Department of Statistics, University of Botswana, Gaborone, Botswana.)

Abstract

The use of extreme value theory (EVT) is usually aimed at quantifying the asymptotic behaviour of extreme quantiles. The generalised Pareto distribution (GPD) with peaks-over-threshold (POT) approach is applied to bootstrap uncertainty intervals for the return periods of extreme daily electricity consumption in South Africa. The leeway of extremes on daily electricity consumption studied here is the impetus behind this study. To examine the effect of a time-based and extreme non-stationary trend in a dataset, a non-stationary GPD is cast-off in computing the shape parameter and, this resulted in the establishment of a type III GPD known as a Weibull class for the South African electricity sector. Results of this study revealed a non-stationary trend with a prediction power of 89.6% for the winter season and 85.65% non-winter season. This means that EVT provides a robust basis for statistical modelling of extreme values. Furthermore, a base for future researchers for conducting studies on emerging markets, more specifically in the South African context has also been contributed.

Suggested Citation

  • Katleho Makatjane, 2022. "Forecasting Uncertainty Intervals for Return Period of Extreme Daily Electricity Consumption," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 217-225, July.
  • Handle: RePEc:eco:journ2:2022-04-25
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijeep/article/download/12901/6836
    Download Restriction: no

    File URL: https://www.econjournals.com/index.php/ijeep/article/view/12901
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Seyoon Lee & Joseph H. T. Kim, 2019. "Exponentiated generalized Pareto distribution: Properties and applications towards extreme value theory," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(8), pages 2014-2038, April.
    2. Cavaliere, Giuseppe & Georgiev, Iliyan, 2013. "Exploiting Infinite Variance Through Dummy Variables In Nonstationary Autoregressions," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1162-1195, December.
    3. repec:bot:quadip:118 is not listed on IDEAS
    4. Hlalefang Khobai, 2018. "Electricity Consumption and Economic Growth: A Panel Data Approach for Brazil, Russia, India, China and South Africa Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 283-289.
    5. Anjum, Hassan & Malik, Farooq, 2020. "Forecasting risk in the US Dollar exchange rate under volatility shifts," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    6. Sigauke, Caston & Verster, Andréhette & Chikobvu, Delson, 2013. "Extreme daily increases in peak electricity demand: Tail-quantile estimation," Energy Policy, Elsevier, vol. 53(C), pages 90-96.
    7. Thomas Siegl & Ansgar West, 2001. "Statistical bootstrapping methods in VaR calculation," Applied Mathematical Finance, Taylor & Francis Journals, vol. 8(3), pages 167-181.
    8. Saralees Nadarajah & Bo Zhang & Stephen Chan, 2014. "Estimation methods for expected shortfall," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 271-291, February.
    9. Knowledge Chinhamu & Chun-Kai Huang & Chun-Sung Huang & Jahvaid Hammujuddy, 2015. "Empirical Analyses of Extreme Value Models for the South African Mining Index," South African Journal of Economics, Economic Society of South Africa, vol. 83(1), pages 41-55, March.
    10. Yang, Yang & Ignatavičiūtė, Eglė & Šiaulys, Jonas, 2015. "Conditional tail expectation of randomly weighted sums with heavy-tailed distributions," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 20-28.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    2. Søren Johansen & Bent Nielsen, 2013. "Outlier Detection in Regression Using an Iterated One-Step Approximation to the Huber-Skip Estimator," Econometrics, MDPI, vol. 1(1), pages 1-18, May.
    3. Søren Johansen & Lukasz Gatarek, 2014. "Optimal hedging with the cointegrated vector autoregressive model," CREATES Research Papers 2014-40, Department of Economics and Business Economics, Aarhus University.
    4. Xing-Fang Huang & Ting Zhang & Yang Yang & Tao Jiang, 2017. "Ruin Probabilities in a Dependent Discrete-Time Risk Model With Gamma-Like Tailed Insurance Risks," Risks, MDPI, vol. 5(1), pages 1-14, March.
    5. Guillen, Montserrat & Bermúdez, Lluís & Pitarque, Albert, 2021. "Joint generalized quantile and conditional tail expectation regression for insurance risk analysis," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 1-8.
    6. Teles Huo & Miguel St. Aubyn, 2022. "Electricity, Exergy and Economic Growth in Mozambique," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 439-446, July.
    7. Søren Johansen & Bent Nielsen, 2011. "Asymptotic theory for iterated one-step Huber-skip estimators," Discussion Papers 11-29, University of Copenhagen. Department of Economics.
    8. Norman Maswanganyi & Caston Sigauke & Edmore Ranganai, 2021. "Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data," Energies, MDPI, vol. 14(20), pages 1-21, October.
    9. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    10. P. B. Zondi & Z. Robinson, 2021. "The Relationship between Government Debt and Economic Growth in South Africa with Specific Reference to Eskom," EuroEconomica, Danubius University of Galati, issue 2(40), pages 17-34, November.
    11. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    12. Sigauke, Caston & Bere, Alphonce, 2017. "Modelling non-stationary time series using a peaks over threshold distribution with time varying covariates and threshold: An application to peak electricity demand," Energy, Elsevier, vol. 119(C), pages 152-166.
    13. Christos Floros & Konstantinos Gkillas & Christos Kountzakis, 2022. "Generalized Johnson Distributions and Risk Functionals," Mathematics, MDPI, vol. 10(17), pages 1-12, September.
    14. Nyiko Worship Hlongwane & Olebogeng David Daw, 2022. "Testing Environmental Kuznets Curve Hold in South Africa: An Econometric Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 385-394, May.
    15. Alexander, Carol & Kaeck, Andreas & Sumawong, Anannit, 2019. "A parsimonious parametric model for generating margin requirements for futures," European Journal of Operational Research, Elsevier, vol. 273(1), pages 31-43.
    16. Landsman, Zinoviy & Makov, Udi & Shushi, Tomer, 2016. "Tail conditional moments for elliptical and log-elliptical distributions," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 179-188.
    17. Juan Carlos Escanciano, 2020. "Uniform Rates for Kernel Estimators of Weakly Dependent Data," Papers 2005.09951, arXiv.org.
    18. Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2023. "Hidden semi-Markov models for rainfall-related insurance claims," Discussion Papers 2023/17, Norwegian School of Economics, Department of Business and Management Science.
    19. Hlongwane, Nyiko Worship & Daw, Olebogeng David, 2022. "Electricity consumption and population growth in South Africa: A panel approach," MPRA Paper 113828, University Library of Munich, Germany.
    20. Sander Barendse, 2017. "Interquantile Expectation Regression," Tinbergen Institute Discussion Papers 17-034/III, Tinbergen Institute.

    More about this item

    Keywords

    Bayesian; Extreme Value Theory; Generalised Pareto Distribution; Markov-chain-Monte-Carlo; Peaks-Over-Threshold.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ2:2022-04-25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.