A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon
详细信息 查看全文
- 作者：Kianoosh G. Boroojenia ; kghol002@fiu.edu" class="auth_mail" title="E-mail the corresponding author ; M. Hadi Aminib ; c ; d ; e ; amini@cmu.edu" class="auth_mail" title="E-mail the corresponding author ; Shahab Bahramif ; bahramis@ece.ubc.ca" class="auth_mail" title="E-mail the corresponding author ; S.S. Iyengara ; iyengar@cis.fiu.edu" class="auth_mail" title="E-mail the corresponding author ; Arif I. Sarwatg ; asarwat@fiu.edu" class="auth_mail" title="E-mail the corresponding author ; Orkun Karabasoglub ; c ; d ; e ; karabasoglu@cmu.edu" class="auth_mail" title="E-mail the corresponding author
- 关键词：Autoregressive model ; Moving-average model ; Time-series forecasting ; Electric power demand forecast ; Akaike information criterion ; Bayesian information criterion
- 刊名：Electric Power Systems Research
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：142
- 期：Complete
- 页码：58-73
- 全文大小：4704 K
文摘
- •
A multi-time-scale approach is proposed for electric power demand forecasting.
- •
Historical load is modeled as a time-series with multiple seasonality levels.
- •
Each seasonal cycle of load data is studied without additional input.
- •
Akaike/Bayesian information criteria is used for accuracy evaluation.
- •
Box-Jenkins method is enhanced for modeling the load data over a time period.