International Journal of Mathematics and Computational Science
Articles Information
International Journal of Mathematics and Computational Science, Vol.4, No.2, Jun. 2018, Pub. Date: May 28, 2018
A Hybrid SARIMA-NARX Nonlinear Dynamics Model for Predicting Solar Radiation in Makurdi
Pages: 35-47 Views: 405 Downloads: 174
[01] Emmanuel Vezua Tikyaa, Department of Physics, Federal University Dutsin-Ma, Katsina State, Nigeria.
[02] Matthias Idugba Echi, Department of Physics, University of Agriculture Makurdi, Benue State, Nigeria.
[03] Bernadette Chidomnso Isikwue, Department of Physics, University of Agriculture Makurdi, Benue State, Nigeria.
[04] Alexander Nwabueze Amah, Department of Physics, University of Agriculture Makurdi, Benue State, Nigeria.
In this paper a hybrid SARIMA-NARX neural network model was successfully developed, trained using 16 years data obtained from the Nigerian Meteorological Agency (NIMET) and tested by forecasting daily solar radiation time series in Makurdi. The intrinsic parameters of the model was optimized using the predetermined nonlinear dynamics of the meteorological data in order to get the right neural network configuration, save time and ensure accurate forecasts. The results of the model testing showed that, the model performed better and faster using the Levenberg-Marquardt training function with daily solar radiation successfully forecasted using minimum temperature and maximum temperature as exogenous variables. The daily solar radiation in Makurdi for the year 2016 was successfully predicted to validate the model using the hybrid model generating a RMSE value of 1.6475, correlation coefficient of 0.8782, MAE of 1.2042 and MAPE of 5.9695%. After validation, forecasts of daily solar radiation were then made for 2016 and 2017 with quite good accuracy recorded. It was also observed that the data trends were accurately predicted as a result of the SARIMA model adopted while the NARX model generated the nonlinear part of the time series with relatively fair but acceptable RMSE values which could be as a result of the poor correlation of the meteorological variables emanating from the presence of missing data and noise in the meteorological data used.
NARX, SARIMA, Neural Network, Chaos Theory, Forecasting
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