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Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

Author

Listed:
  • Erasmo Cadenas

    (Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Col. Centro, CP 58000 Morelia, Michoacan, Mexico)

  • Wilfrido Rivera

    (Instituto de Energias Renovables, Universidad Nacional Autonoma de Mexico, Apartado postal 34, CP 62580 Temixco, Morelos, Mexico
    These authors contributed equally to this work.)

  • Rafael Campos-Amezcua

    (Instituto de Energias Renovables, Universidad Nacional Autonoma de Mexico, Apartado postal 34, CP 62580 Temixco, Morelos, Mexico
    These authors contributed equally to this work.)

  • Christopher Heard

    (Division de Ciencias de la Comunicacion y Diseno, Departamento de Teoria y Procesos del Diseno, Diseno Ambiental, Universidad Autonoma Metropolitana Unidad Cuajimalpa, Torre III, 5to. piso, Av. Vasco de Quiroga 4871, Col. Santa Fe Cuajimalpa, Del. Cuajimalpa, Mexico D.F. 11850, Mexico
    These authors contributed equally to this work.)

Abstract

Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.

Suggested Citation

  • Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:2:p:109-:d:63927
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    References listed on IDEAS

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