Título: Short Term Load Forecasting via a Hierarchical Neural Model
Autores: Carpinteiro, Otávio A. S.; Silva, Alexandre P. Alves da; Reis, Agnaldo J. Rocha
Resumo: 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 is trained and compared to a Multi-Layer Perceptron load forecaster. It is required to compute the one to twenty four steps ahead recursive load forecasts. The paper presents the results, and evaluates them.
Código DOI: 10.21528/CBRN2001-011
Artigo em pdf: 5cbrn_011.pdf
Arquivo BibTex: 5cbrn_011.bib