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

Author

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  • Eduardo Rangel-Heras

    (Facultad de Ingeniería Mecánica, Universidad Michoacana de San Nicolás de Hidalgo, Santiago Tapia No. 403, Centro, Morelia 58000, Mexico)

  • César Angeles-Camacho

    (Instituto de Ingeniería, Universidad Nacional Autónoma de México, Avenida Universidad No. 3000 Coyoacán, Ciudad Universitaria, Ciudad de México 04510, Mexico)

  • Erasmo Cadenas-Calderón

    (Facultad de Ingeniería Mecánica, Universidad Michoacana de San Nicolás de Hidalgo, Santiago Tapia No. 403, Centro, Morelia 58000, Mexico)

  • Rafael Campos-Amezcua

    (Tecnológico Nacional de México/Centro Nacional de Investigación y Desarrollo Tecnológico, Interior Internado Palmira S/N, Col. Palmira, Cuernavaca 62490, Mexico)

Abstract

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.

Suggested Citation

  • Eduardo Rangel-Heras & César Angeles-Camacho & Erasmo Cadenas-Calderón & Rafael Campos-Amezcua, 2022. "Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model," Energies, MDPI, vol. 15(8), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2842-:d:793021
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