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Constrained nonparametric regression analysis of load curves

Author

Listed:
  • Juan RodrÎguez-Poo

    () (Departamento de EconomÎa, Universidad de Cantabria, Avda. de los Castros s/n, 39005 Santander, Spain)

Abstract

In this paper, we analyze household load curves through the use of Constrained Smoothing Splines. These estimators are natural smoothing splines that allow to incorporate periodic shape constraints. Since the time pattern of electricity demand combines strong periodical regularities with abrupt changes along time, a nonparametric regression estimator that is able to incorporate regularity constrains appears to be very well suited to approach load curves. In the paper we also propose a method to compute the penalty parameters that appear in the constrained smoothing spline estimator, we show some statistical properties and finally we construct confidence intervals.

Suggested Citation

  • Juan RodrÎguez-Poo, 2000. "Constrained nonparametric regression analysis of load curves," Empirical Economics, Springer, vol. 25(2), pages 229-246.
  • Handle: RePEc:spr:empeco:v:25:y:2000:i:2:p:229-246
    Note: received: February 1998/final version accepted: July 1999
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    Citations

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    Cited by:

    1. Antonio Rubia, 2001. "Testing For Weekly Seasonal Unit Roots In Daily Electricity Demand: Evidence From Deregulated Markets," Working Papers. Serie EC 2001-21, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    2. Matteo Manera & Angelo Marzullo, 2003. "Modelling the Load Curve of Aggregate Electricity Consumption Using Principal Components," Working Papers 2003.95, Fondazione Eni Enrico Mattei.

    More about this item

    Keywords

    Natural smoothing splines; load-curves; nonparametric regression under shape constrains;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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