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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)
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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Publisher 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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