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Bootstrapping the Probability Distribution of Peak Electricity Demand

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Demand For effective capacity planning, an electric utility requires an estimate of the probability distribution of future maximum demand, rather than simply a point prediction of expected peak. This paper proposes a method of obtaining this using the bootstrapping technique of B. Efron_(1979) and this is applied to the peak demand of an actual utility, Ontario Hydro. While the technique is constructed from the standard procedure of forecasting a future variable using regression coefficients and known values for the right-hand side variables, it is modified to allow for uncertainty in these independent variable forecasts as well. Copyright 1987 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
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  • Michael R. Veall, 1985. "Bootstrapping the Probability Distribution of Peak Electricity Demand," University of Western Ontario, Departmental Research Report Series 8506, University of Western Ontario, Department of Economics.
    • Handle: RePEc:uwo:uwowop:8506
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    Cited by:

    1. Gregory, Allan W. & McCurdy, Thomas H., 1986. "The unbiasedness hypothesis in the forward foreign exchange market: A specification analysis with application to France, Italy, Japan, the United Kingdom and West Germany," European Economic Review, Elsevier, vol. 30(2), pages 365-381, April.
    2. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-146, March.
    3. Li, Hongyi & Maddala, G. S., 1997. "Bootstrapping cointegrating regressions," Journal of Econometrics, Elsevier, vol. 80(2), pages 297-318, October.
    4. Allen, P. Geoffrey & Morzuch, Bernard J., 1995. "Comparing probability forecasts derived from theoretical distributions," International Journal of Forecasting, Elsevier, vol. 11(1), pages 147-157, March.
    5. Drago Papler & Štefan Bojnec, 2015. "Competitiveness and Factors of Delivery of Electricity," Faculty of Management Koper Monograph Series, University of Primorska, Faculty of Management Koper, number 978-961-266-188-5, June.
    6. Mohammed A. Al-Sahlawi, 1990. "Forecasting the Demand for Electricity in Saudi Arabia," The Energy Journal, , vol. 11(1), pages 119-126, January.
    7. Tonsor, Glynn T. & Dhuyvetter, Kevin C. & Mintert, James R., 2004. "Improving Cattle Basis Forecasting," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(2), pages 1-14, August.
    8. Grigoletto, Matteo, 1998. "Bootstrap prediction intervals for autoregressions: some alternatives," International Journal of Forecasting, Elsevier, vol. 14(4), pages 447-456, December.

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