Generating electrical demand time series applying SRA technique to complement NAR and sARIMA models

García, Jorge L. Tena; Calderón, Erasmo Cadenas; Heras, Eduardo Rangel; Ontiveros, Christian Morales

doi:10.1007/s12053-019-09774-2

Cited by 11 publications

(1 citation statement)

References 45 publications

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“…Like the persistence model, the nonlinear autoregressive (NAR) neural network has been used by Benmouiza, Cheknane [9,46] and García-Tena et al [47] to forecast time series or as a benchmark to compare more complex models.…”

Section: Comparison Of the H-narx-cvm Model Against Other Modelsmentioning

confidence: 99%

Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model

et al. 2022

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In this paper, a methodology for short-term forecasting of power generated by a photovoltaic module is reported. The method incorporates a nonlinear autoregressive with exogenous inputs (NARX) fed by the solar radiation and temperature times series, as well as an estimation of power time series obtained by implementing an ideal single diode model. This synthetic time series was validated against an actual photovoltaic module. The NARX model has been implemented in conjunction with the corrective vector multiplier (CVM) technique, which uses solar radiation under clear sky conditions to adjust the forecasting results. In addition, collinearity and the Granger causality tests were used to choose the input variables. The forecasting horizon was 24-h-ahead. The hybrid NARX-CVM model was compared to a nonlinear autoregressive neural network and persistence model using the typic forecasting error measures such as the mean bias error, mean squared error, root mean squared error and forecast skill. The results showed that the forecasting skills of the hybrid model are about 34% against the NAR model and about 42% against the Persistence model. The model was validated by blind forecasting. The results demonstrated evidence of the quality of the conformed forecasting model and the convenience of its implementation and building.

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Section: Comparison Of the H-narx-cvm Model Against Other Modelsmentioning

confidence: 99%