| Autor: |
M. O. Osifeko, O. Folorunsho, O. I. Sanusi, P. O. Alao, O. O. Ade-Ikuesan, O. G. Olasunkanmi |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
Arid Zone Journal of Engineering, Technology and Environment, Vol 14, Iss 4, Pp 583-592 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2545-5818 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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