Principles of electricity demand forecasting .1. Methodologies

被引：26
作者：
AlAlawi, SM [1 ]
Islam, SM [1 ]
机构：
[1] UNIV NEWCASTLE, DEPT ELECT & COMP ENGN, NEWCASTLE, NSW 2308, AUSTRALIA
来源：
POWER ENGINEERING JOURNAL | 1996年 / 10卷 / 03期
关键词：
D O I：
10.1049/pe:19960306
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The importance of electricity demand forecasting, factors influencing the various ranges of demand forecasting, and their correlation and contribution to demand are discussed. Various models which can be used to identify the demand pattern and underlying growth to predict future demand are presented. A step-by-step method of building a forecasting model is also provided.
引用
收藏
页码：139 / 143
页数：5
相关论文
共 9 条
[1]  
Barakat E. H., 1993, Power Engineering Journal, V7, P177, DOI 10.1049/pe:19930045
[2]  
Box GEP, 1976, Time series analysis: Forecasting and control
[3]  
Bunn D., 1985, Comparative models for electrical load forecasting
[4]  
Burden R. L., 1985, Numerical Analysis
[5]  
ISLAM SM, C REC IPEC95 SING, P690
[6]  
Mendenhall W., 1994, INTRO PROBABILITY ST
[7]   ELECTRIC-LOAD FORECASTING USING AN ARTIFICIAL NEURAL NETWORK [J].
PARK, DC ;
ELSHARKAWI, MA ;
MARKS, RJ ;
ATLAS, LE ;
DAMBORG, MJ .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1991, 6 (02) :442-449
[8]   AN EXPERT SYSTEM BASED ALGORITHM FOR SHORT-TERM LOAD FORECAST [J].
RAHMAN, S ;
BHATNAGAR, R .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1988, 3 (02) :392-399
[9]  
Stein R., 1993, AI Expert, V8, P42
1