A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, Nigeria
The unreliability of solar energy as an alternative source of electricity is a source of concern to stakeholders. To mitigate this challenge, researchers have proposed photovoltaic (PV) power output forecasting which is aimed at predicting the power output of a PV plant. This study develops and validates a three-tier neural network model for forecasting the output of a mini PV plant located in Ifo, Ogun State, Nigeria. The result of the developed model was compared with a state-of-the-art mathematical model using three statistical tools of mean bias error (MBE), root mean square error (RMSE) and mean average percentage error (MAPE) over a period of three months. From the monthly evaluation, results reveal that the MBE values of the three-tier model were lower than that of the mathematical model with a difference of 0.08, 0.03, and 0.09. In terms of the RMSE, the difference between the three-tier and mathematical model values are 0.07, 0.01 and 0.02. The MAPE differences between the two models were 0.05, 0.00 and 0.02. In all the obtained results, the three-tier model showed a consistently better performance than the mathematical model which validates it as a reliable tool for forecasting the power output of a PV plant.
Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, Nigeria
The unreliability of solar energy as an alternative source of electricity is a source of concern to stakeholders. To mitigate this challenge, researchers have proposed photovoltaic (PV) power output forecasting which is aimed at predicting the power output of a PV plant. This study develops and validates a three-tier neural network model for forecasting the output of a mini PV plant located in Ifo, Ogun State, Nigeria. The result of the developed model was compared with a state-of-the-art mathematical model using three statistical tools of mean bias error (MBE), root mean square error (RMSE) and mean average percentage error (MAPE) over a period of three months. From the monthly evaluation, results reveal that the MBE values of the three-tier model were lower than that of the mathematical model with a difference of 0.08, 0.03, and 0.09. In terms of the RMSE, the difference between the three-tier and mathematical model values are 0.07, 0.01 and 0.02. The MAPE differences between the two models were 0.05, 0.00 and 0.02. In all the obtained results, the three-tier model showed a consistently better performance than the mathematical model which validates it as a reliable tool for forecasting the power output of a PV plant.
Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, Nigeria
M. O. Osifeko (author) / O. Folorunsho (author) / O. I. Sanusi (author) / P. O. Alao (author) / O. O. Ade-Ikuesan (author) / O. G. Olasunkanmi (author)
2018
Article (Journal)
Electronic Resource
Unknown
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