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DYNA

versão impressa ISSN 0012-7353

Resumo

LIMA DE MENEZES, Moisés; CASTRO SOUZA, Reinaldo  e  MOREIRA PESSANHA, José Francisco. Electricity consumption forecasting using singular spectrum analysis. Dyna rev.fac.nac.minas [online]. 2015, vol.82, n.190, pp.138-146. ISSN 0012-7353.  http://dx.doi.org/10.15446/dyna.v82n190.43652.

Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this paper, we also present the main results of a computational experiment with the monthly residential consumption of electricity in Brazil.

Palavras-chave : electricity consumption forecasting; singular spectrum analysis; time series; power system planning.

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