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

Rong Chen, John L. Harris, Jun M. Liu and Lon-Mu Liu
Additional contact information
John L. Harris: Progress Energy, Inc., Raleigh, North Carolina, USA, Postal: Progress Energy, Inc., Raleigh, North Carolina, USA
Jun M. Liu: Georgia Southern University, Statesboro, Georgia, USA, Postal: Georgia Southern University, Statesboro, Georgia, USA
Lon-Mu Liu: University of Illinois at Chicago, Chicago, Illinois, USA, Postal: University of Illinois at Chicago, Chicago, Illinois, USA

Journal of Forecasting, 2006, vol. 25, issue 8, 537-559

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.

Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:25:y:2006:i:8:p:537-559

DOI: 10.1002/for.1006

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