A hierarchical neural model in short-term load forecasting

被引：2
|
作者：
Carpinteiro, OAS [1 ]
da Silva, APA [1 ]
机构：
[1] Escola Fed Engn Itajuba, Inst Engn Eletr, BR-37500903 Itajuba, MG, Brazil
来源：
SIXTH BRAZILIAN SYMPOSIUM ON NEURAL NETWORKS, VOL 1, PROCEEDINGS | 2000年
关键词：
D O I：
10.1109/SBRN.2000.889725
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets one on top of the other. If has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It uas required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates them.
引用
收藏
页码：120 / 124
页数：5
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