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Forecasting electricity consumption with extra-model information provided by consumers

Author

Listed:
  • Victor Guerrero
  • Edmundo Berumen

Abstract

Univariate time series models make efficient use of available historical records of electricity consumption for short-term forecasting. However, the information (expectations) provided by electricity consumers in an energy-saving survey, even though qualitative, was considered to be particularly important, because the consumers' perception of the future may take into account the changing economic conditions. Our approach to forecasting electricity consumption combines historical data with expectations of the consumers in an optimal manner, using the technique of restricted forecasts. The same technique can be applied in some other forecasting situations in which additional information-besides the historical record of a variable-is available in the form of expectations.

Suggested Citation

  • Victor Guerrero & Edmundo Berumen, 1998. "Forecasting electricity consumption with extra-model information provided by consumers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(2), pages 283-299.
    • Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:283-299
    DOI: 10.1080/02664769823269
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    References listed on IDEAS

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    1. de Alba, Enrique, 1993. "Constrained forecasting in autoregressive time series models: A Bayesian analysis," International Journal of Forecasting, Elsevier, vol. 9(1), pages 95-108, April.
    2. G. E. P. Box & G. C. Tiao, 1976. "Comparison of Forecast and Actuality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(3), pages 195-200, November.
    3. Oller, Lars-Erik, 1990. "Forecasting the business cycle using survey data," International Journal of Forecasting, Elsevier, vol. 6(4), pages 453-461, December.
    4. M. Guerrero, Victor & A. Johnson, Richard, 1988. "Transforming grouped bivariate data to near normality," Statistics & Probability Letters, Elsevier, vol. 6(4), pages 213-224, March.
    5. repec:bla:econom:v:42:y:1975:i:166:p:123-38 is not listed on IDEAS
    6. Guerrero, Victor M., 1993. "Combining historical and preliminary information to obtain timely time series data," International Journal of Forecasting, Elsevier, vol. 9(4), pages 477-485, December.
    7. Fishe, Raymond P. H. & Lahiri, Kajal, 1981. "On the estimation of inflationary expectations from qualitative responses," Journal of Econometrics, Elsevier, vol. 16(1), pages 89-102, May.
    8. Trabelsi, Abdelwahed & Hillmer, Steven C, 1989. "A Benchmarking Approach to Forecast Combination," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(3), pages 353-362, July.
    9. Chatfield, Chris & Yar, Mohammed, 1991. "Prediction intervals for multiplicative Holt-Winters," International Journal of Forecasting, Elsevier, vol. 7(1), pages 31-37, May.
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