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

被引：23
|
作者：
Liu, Jun M.
Chen, Rong
Liu, Lon-Mu
Harris, John L.
机构：
[1] Univ Illinois, Chicago, IL 60607 USA
[2] Georgia So Univ, Statesboro, GA 30460 USA
[3] Peking Univ, Beijing 100871, Peoples R China
[4] Progress Energy Inc, Raleigh, NC USA
来源：
JOURNAL OF FORECASTING | 2006年 / 25卷 / 08期
关键词：
electricity load forecasting; nonparametric regression; backfitting; time series; ARIMA model;
D O I：
10.1002/for.1006
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
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 timeof-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-sarnple forecasting performance is evaluated and comparative results are presented. Copyright (C) 2006 John Wiley & Sons, Ltd.
引用
收藏
页码：537 / 559
页数：23
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