[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/p/fgv/epgewp/491.html
   My bibliography  Save this paper

Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil

Author

Listed:
  • Souza, Leonardo Rocha
  • Soares, Lacir Jorge
Abstract
This paper studies the electricity load demand behavior during the 2001 rationing period, which was implemented because of the Brazilian energetic crisis. The hourly data refers to a utility situated in the southeast of the country. We use the model proposed by Soares and Souza (2003), making use of generalized long memory to model the seasonal behavior of the load. The rationing period is shown to have imposed a structural break in the series, decreasing the load at about 20%. Even so, the forecast accuracy is decreased only marginally, and the forecasts rapidly readapt to the new situation. The forecast errors from this model also permit verifying the public response to pieces of information released regarding the crisis.

Suggested Citation

  • Souza, Leonardo Rocha & Soares, Lacir Jorge, 2003. "Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 491, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    • Handle: RePEc:fgv:epgewp:491
    as

    Download full text from publisher

    File URL: https://repositorio.fgv.br/bitstreams/3759a73f-0103-40f2-9ae5-9df28a282ae9/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
    2. Arteche, Josu & Robinson, Peter M., 1998. "Seasonal and cyclical long memory," LSE Research Online Documents on Economics 2241, London School of Economics and Political Science, LSE Library.
    3. Ferrara, Laurent & Guegan, Dominique, 2001. "Forecasting with k-Factor Gegenbauer Processes: Theory and Applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(8), pages 581-601, December.
    4. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    5. repec:crs:wpaper:9927 is not listed on IDEAS
    6. Laurent Ferrara & Dominique Guegan, 2000. "Forecasting financial time series with generalized long memory processes," Post-Print halshs-00199126, HAL.
    7. Laurent Ferrara & Dominique Guegan, 1999. "Estimation and Applications of Gegenbauer Processes," Working Papers 99-27, Center for Research in Economics and Statistics.
    8. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    9. Henry L. Gray & Nien‐Fan Zhang & Wayne A. Woodward, 1989. "On Generalized Fractional Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 233-257, May.
    10. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    11. Josu Arteche, 2002. "Semiparametric robust tests on seasonal or cyclical long memory time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(3), pages 251-285, May.
    12. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    13. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    14. Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.

    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. Rocha Souza, Leonardo & Jorge Soares, Lacir, 2007. "Electricity rationing and public response," Energy Economics, Elsevier, vol. 29(2), pages 296-311, March.
    2. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
    3. Laurent Ferrara & Dominique Guégan, 2008. "Business surveys modelling with Seasonal-Cyclical Long Memory models," Economics Bulletin, AccessEcon, vol. 3(29), pages 1-10.
    4. Guglielmo Maria Caporale & Juncal Cuñado & Luis A. Gil-Alana, 2013. "Modelling long-run trends and cycles in financial time series data," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 405-421, May.
    5. Dominique Guegan & Laurent Ferrara, 2008. "Fractional and seasonal filtering," PSE-Ecole d'économie de Paris (Postprint) halshs-00646178, HAL.
    6. L.A. Gil-Alanaa, 2007. "Testing The Existence of Multiple Cycles in Financial and Economic Time Series," Annals of Economics and Finance, Society for AEF, vol. 8(1), pages 1-20, May.
    7. repec:ebl:ecbull:v:3:y:2008:i:29:p:1-10 is not listed on IDEAS
    8. L.A. Gil-Alana, 2005. "Fractional Cyclical Structures & Business Cycles in the Specification of the US Real Output," European Research Studies Journal, European Research Studies Journal, vol. 0(1-2), pages 99-126.
    9. Laurent Ferrara & Dominique Guegan, 2008. "Business surveys modelling with Seasonal-Cyclical Long Memory models," Post-Print halshs-00277379, HAL.
    10. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    11. Artiach, Miguel & Arteche, Josu, 2012. "Doubly fractional models for dynamic heteroscedastic cycles," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2139-2158.
    12. Laurent Ferrara & Dominique Guegan, 2008. "Business surveys modelling with Seasonal-Cyclical Long Memory models," Post-Print halshs-00283710, HAL.
    13. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    14. Sandro Sapio, 2004. "Markets Design, Bidding Rules, and Long Memory in Electricity Prices," Revue d'Économie Industrielle, Programme National Persée, vol. 107(1), pages 151-170.
    15. Voges, Michelle & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks," Hannover Economic Papers (HEP) dp-599, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    16. Laurent Ferrara & Dominique Guegan & Zhiping Lu, 2008. "Testing fractional order of long memory processes: a Monte Carlo study," Documents de travail du Centre d'Economie de la Sorbonne b08012, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    17. Laurent Ferrara & Dominique Guegan, 2006. "Fractional seasonality: Models and Application to Economic Activity in the Euro Area," Post-Print halshs-00185370, HAL.
    18. Dominique Guegan, 2003. "A prospective study of the k-factor Gegenbauer processes with heteroscedastic errors and an application to inflation rates," Post-Print halshs-00201314, HAL.
    19. Luis A. Gil-Alana & Juan Carlos Cuestas, 2012. "A Non-linear Approach with Long Range Dependence based on Chebyshev Polynomials," Faculty Working Papers 14/12, School of Economics and Business Administration, University of Navarra.
    20. Guglielmo Maria Caporale & Luis Gil‐Alana, 2014. "Long‐Run and Cyclical Dynamics in the US Stock Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(2), pages 147-161, March.
    21. Arteche, Josu, 2004. "Gaussian semiparametric estimation in long memory in stochastic volatility and signal plus noise models," Journal of Econometrics, Elsevier, vol. 119(1), pages 131-154, March.

    More about this item

    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:fgv:epgewp:491. 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: Núcleo de Computação da FGV EPGE (email available below). General contact details of provider: https://edirc.repec.org/data/epgvfbr.html .

    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.