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DYNA

versão impressa ISSN 0012-7353

Resumo

VELASQUEZ HENAO, JUAN DAVID; RUEDA MEJIA, VIVIANA MARIA  e  FRANCO CARDONA, CARLOS JAIME. ELECTRICITY DEMAND FORECASTING USING A SARIMA-MULTIPLICATIVE SINGLE NEURON HYBRID MODEL. Dyna rev.fac.nac.minas [online]. 2013, vol.80, n.180, pp.4-8. ISSN 0012-7353.

The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artificial neural networks are applied to forecast the remaining nonlinearities in the shocks of the SARIMA model. In this paper, we propose a simple nonlinear time series forecasting model by combining the SARIMA model with a multiplicative single neuron using the same inputs as the SARIMA model. To evaluate the capacity of the new approach, the monthly electricity demand in the Colombian energy market is forecasted and compared with the SARIMA and multiplicative single neuron models.

Palavras-chave : SARIMA; artificial neural networks; time series prediction; energy demand; energy markets; nonlinear models.

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