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Modelling the Load Curve of Aggregate Electricity Consumption Using Principal Components


  • Matteo Manera

    (Department of Statistics, University of Milano-Bicocca and Fondazione Eni Enrico Mattei, Milano, Italy)

  • Angelo Marzullo

    (Enifin, Eni S.p.A., Milano, Italy)


Since oil is a non-renewable resource with a high environmental impact, and its most common use is to produce combustibles for electricity, reliable methods for modelling electricity consumption can contribute to a more rational employment of this hydrocarbon fuel. In this paper we apply the Principal Components (PC) method to modelling the load curves of Italy, France and Greece on hourly data of aggregate electricity consumption. The empirical results obtained with the PC approach are compared with those produced by the Fourier and constrained smoothing spline estimators. The PC method represents a much simpler and attractive alternative to modelling electricity consumption since it is extremely easy to compute, it significantly reduces the number of variables to be considered, and generally increases the accuracy of electricity consumption forecasts. As an additional advantage, the PC method is able to accommodate relevant exogenous variables such as daily temperature and environmental factors, and it is extremely versatile in computing out-of-sample forecasts.

Suggested Citation

  • Matteo Manera & Angelo Marzullo, 2003. "Modelling the Load Curve of Aggregate Electricity Consumption Using Principal Components," Working Papers 2003.95, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2003.95

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    References listed on IDEAS

    1. 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.
    2. Hendricks, Wallace & Koenker, Roger & Poirier, Dale J., 1979. "Residential demand for electricity : An econometric approach," Journal of Econometrics, Elsevier, vol. 9(1-2), pages 33-57, January.
    3. Granger, Clive W. J. & Engle, Robert & Ramanathan, Ramu & Andersen, Allan, 1979. "Residential load curves and time-of-day pricing : An econometric analysis," Journal of Econometrics, Elsevier, vol. 9(1-2), pages 13-32, January.
    4. Juan RodrÎguez-Poo, 2000. "Constrained nonparametric regression analysis of load curves," Empirical Economics, Springer, vol. 25(2), pages 229-246.
    5. RODRIGUEZ-POO , Juan M., 1992. "Estimating the Time-of-Day Electricity Demand by Using the Constrained Smoothing Spline Estimator," CORE Discussion Papers 1992054, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Bašta, Milan & Helman, Karel, 2013. "Scale-specific importance of weather variables for explanation of variations of electricity consumption: The case of Prague, Czech Republic," Energy Economics, Elsevier, vol. 40(C), pages 503-514.
    2. Andersen, F.M. & Larsen, H.V. & Gaardestrup, R.B., 2013. "Long term forecasting of hourly electricity consumption in local areas in Denmark," Applied Energy, Elsevier, vol. 110(C), pages 147-162.

    More about this item


    Electricity; Load curves; Principal components; Fourier estimator; Constrained smoothing estimator; Temperature; Non-renewable resources; Hydrocarbon fuels; Environment;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q30 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General


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