Electric load forecasting: literature survey and classification of methods

被引:483
作者
Alfares, HK [1 ]
Nazeeruddin, M [1 ]
机构
[1] King Fahd Univ Petr & Minerals, Dept Syst Engn, Dhahran 31261, Saudi Arabia
关键词
D O I
10.1080/00207720110067421
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 [计算机科学与技术];
摘要
A review and categorization of electric load,forecasting techniques is presented. A wide range of methodologies and models,for,forecasting are given in the literature. These techniques are classified here into nine categories: (1) mutltiple regression, (2) exponential smoothing, (3) iterative reweighted least-squares, (4) adaptive load forecasting, (5) stochastic time series, (6) ARMAX models based on genetic algorithms, (7) fuzzy logic, (8) neural networks and (9) expert systems. The methodology for each category is briefly described, the advantages and disadvantages discussed, and the pertinent literature reviewed. Conclusions and comments are made on future research directions.
引用
收藏
页码:23 / 34
页数:12
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