Long-term load forecasting using system type neural network architecture

被引：0
|
作者：
Hobbs, Nathaniel J. [1 ]
Kim, Byoung H. [1 ]
Lee, Kwang Y. [2 ]
机构：
[1] Penn State Univ, Dept Elect Engn, University Pk, PA 16802 USA
[2] Baylor Univ, Dept Elect & Comp Engn, Waco, TX 76798 USA
来源：
2007 INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS APPLICATIONS TO POWER SYSTEMS, VOLS 1 AND 2 | 2007年
基金：
美国国家科学基金会;
关键词：
decomposition; load forecasting; neural network; system-type architecture;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper presents a methodology for long-term electric power demands using a semigroup based system-type neural network architecture. The assumption is that given enough data, the next year's loads can be predicted using only components from the previous few years. This methodology is applied to recent load data, and the next year's load data is satisfactorily forecasted. This method also provides a more in depth forecasted time interval than other methods that just predict the average or peak power demand in the interval.
引用
收藏
页码：435 / +
页数：3
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