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A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts

Author

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  • Niematallah Elamin

    () (Graduate School of Economics, Osaka University)

  • Mototsugu Fukushige

    () (Graduate School of Economics, Osaka University)

Abstract

Electricity peak demand forecasting is a key exercise undertaken to avoid power blackouts and system failure. In this paper, the next day's load peak demand is estimated and forecasted. The challenge is to generate a peak demand forecast that is capable of avoiding the risk of a power blackout. We take an empirical approach to the question of estimating quantiles to indicate forecast uncertainty. Point forecasts generated from quantile regression are compared with the prediction intervals of linear regression. In addition, and to justify the result, their out-of-sample forecasting performance is compared. Distinctively from previous studies on load forecasting, models are evaluated based on their ability to avoid under-prediction i.e. avoid the risk of power blackouts. The analysis shows that quantile regression tends to under predict less than linear regression. Thus quantile regression is more appropriate for avoiding power blackouts.

Suggested Citation

  • Niematallah Elamin & Mototsugu Fukushige, 2016. "A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts," Discussion Papers in Economics and Business 16-22, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
  • Handle: RePEc:osk:wpaper:1622
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    File URL: http://www2.econ.osaka-u.ac.jp/library/global/dp/1622.pdf
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    References listed on IDEAS

    as
    1. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    2. Tao Hong & Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts," HSC Research Reports HSC/14/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    3. Bidong Liu & Jakub Nowotarski & Tao Hong & Rafal Weron, 2015. "Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts," HSC Research Reports HSC/15/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
    6. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    7. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Electricity peak demand; Quantile regression; Prediction intervals; Blackout;

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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