A hierarchical neural model in short-term load forecasting

This paper proposes a novel neural model to the problem of short-term load forecasting (STLF). The neural model is made up of two self-organizing map (SOM) 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 primar...

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Bibliographic Details
Published in:Applied soft computing 2004-09, Vol.4 (4), p.405-412
Main Authors: Carpinteiro, Otavio A.S, Reis, Agnaldo J.R, da Silva, Alexandre P.A
Format: Article
Language:English
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Summary:This paper proposes a novel neural model to the problem of short-term load forecasting (STLF). The neural model is made up of two self-organizing map (SOM) 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 on load data extracted from a Brazilian electric utility, and compared to a multilayer perceptron (MLP) load forecaster. It was required to predict once every hour the electric load during the next 24 h. The paper presents the results, the conclusions, and points out some directions for future work.
ISSN:1568-4946
DOI:10.1016/j.asoc.2004.02.005