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A semi-parametric time series approach in modeling hourly electricity loads

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Author Info

  • Rong Chen
  • John L. Harris

    (Progress Energy, Inc., Raleigh, North Carolina, USA)

  • Jun M. Liu

    (Georgia Southern University, Statesboro, Georgia, USA)

  • Lon-Mu Liu

    (University of Illinois at Chicago, Chicago, Illinois, USA)

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    Abstract

    In this paper we develop a semi-parametric approach to model nonlinear relationships in serially correlated data. To illustrate the usefulness of this approach, we apply it to a set of hourly electricity load data. This approach takes into consideration the effect of temperature combined with those of time-of-day and type-of-day via nonparametric estimation. In addition, an ARIMA model is used to model the serial correlation in the data. An iterative backfitting algorithm is used to estimate the model. Post-sample forecasting performance is evaluated and comparative results are presented.  Copyright © 2006 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/for.1006
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 25 (2006)
    Issue (Month): 8 ()
    Pages: 537-559

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    Handle: RePEc:jof:jforec:v:25:y:2006:i:8:p:537-559

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    Cited by:
    1. V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.
    2. V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.
    3. 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.

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