Nonlinear autoregressive integrated neural network model for short-term load forecasting

被引：58
|
作者：
Chow, TWS
Leung, CT
机构：
[1] Department of Electronic Engineering, City University of Hong Kong, Kowloon
来源：
IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION | 1996年 / 143卷 / 05期
关键词：
short-term load forecasting; weather compensation neural network; nonlinear autoregressive integrated model;
D O I：
10.1049/ip-gtd:19960600
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A novel neural network technique for electric load forecasting based on weather compensation is presented. The proposed method is a nonlinear generalisation of Box and Jenkins approach for nonstationary time-series prediction. A nonlinear autoregressive integrated (NARI) model is identified to be the most appropriate model to include the weather compensation in short-term electric load forecasting. A weather compensation neural network based on an NARI model is implemented for one-day ahead electric load forecasting. This weather compensation neural network can accurately predict the change of electric load consumption of the coming day. The results, based on Hong Kong Island historical load indicate that this methodology is capable of providing more accurate load forecast with a 0.9% reduction in forecast error.
引用
收藏
页码：500 / 506
页数：7
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