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

By Rong Chen, John L. Harris, Jun M. Liu and Lon-Mu Liu


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.

DOI identifier: 10.1002/for.1006
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