A semi-parametric time series approach in modeling hourly electricity loads
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.
Volume (Year): 25 (2006)
Issue (Month): 8 ()
|Contact details of provider:|| Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966|
When requesting a correction, please mention this item's handle: RePEc:jof:jforec:v:25:y:2006:i:8:p:537-559. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If references are entirely missing, you can add them using this form.