Portal de Periódicos da CAPES

Artigo Produção Nacional Revisado por pares

BIBTEX RIS

Electric load forecasting: evaluating the novel hierarchical neuro-fuzzy BSP model

2003; Elsevier BV; Volume: 26; Issue: 2 Linguagem: Inglês

10.1016/s0142-0615(03)00060-7

1879-3517

Marley Vellasco, Marco Aurélio C. Pacheco, Luiz Sabino Ribeiro Neto, F.J. de Souza,

Stock Market Forecasting Methods

This paper introduces a new hybrid neuro-fuzzy model, called HNFB, and evaluates its performance in short-term load forecasting. To this end, two Brazilian electric power companies were used as case studies. A total of three intelligent forecasting systems were tested and compared: neural networks, neuro-fuzzy, and neural/neuro-fuzzy systems. As input variables, the experiments made use of historical load series and of additional variables that influence the load behavior, such as the temperature, the comfort index and the consumption profile. The results reveal the potential of the proposed neuro-fuzzy and neural/neuro-fuzzy models for load forecasting, when compared with neural networks; the mean absolute percentage errors varied between 0.44 and 1.95%, depending on the case study at hand.