Title :
A hierarchical neural model in short-term load forecasting
Author :
Carpinteiro, Otávio A S ; Da Silva, Alexandre P A ; Feichas, Carlos H L
Author_Institution :
Inst. de Engenharia, Escola Fed. de Engenharia de Itajuba, Brazil
Abstract :
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. It 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 was required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates them
Keywords :
electricity supply industry; load forecasting; self-organising feature maps; Brazilian electric utility; hierarchical neural model; load forecasting; neural model; neural nets; self-organizing map; short-term forecasting; Data mining; Load forecasting; Load modeling; Neural networks; Power generation; Power industry; Power system modeling; Power system planning; Predictive models; Weather forecasting;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.859403
