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 vali...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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University of Maiduguri
2018-12-01
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| Series: | Arid Zone Journal of Engineering, Technology and Environment |
| Online Access: | http://azojete.com.ng/index.php/azojete/article/view/491 |
| _version_ | 1818016752151298048 |
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| author | M. O. Osifeko O. Folorunsho O. I. Sanusi P. O. Alao O. O. Ade-Ikuesan O. G. Olasunkanmi |
| author_facet | M. O. Osifeko O. Folorunsho O. I. Sanusi P. O. Alao O. O. Ade-Ikuesan O. G. Olasunkanmi |
| author_sort | M. O. Osifeko |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T07:16:54Z |
| format | Article |
| id | doaj.art-9ba5b96adb5c4d5dbad0918aedcefd99 |
| institution | Directory Open Access Journal |
| issn | 2545-5818 2545-5818 |
| language | English |
| last_indexed | 2024-04-14T07:16:54Z |
| publishDate | 2018-12-01 |
| publisher | University of Maiduguri |
| record_format | Article |
| series | Arid Zone Journal of Engineering, Technology and Environment |
| spelling | doaj.art-9ba5b96adb5c4d5dbad0918aedcefd992022-12-22T02:06:16ZengUniversity of MaiduguriArid Zone Journal of Engineering, Technology and Environment2545-58182545-58182018-12-01144583592Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, NigeriaM. O. Osifeko0O. Folorunsho1O. I. Sanusi2P. O. Alao3O. O. Ade-Ikuesan4O. G. Olasunkanmi5Department of Computer Engineering, Olabisi Onabanjo University, Ago-Iwoye, NigeriaDepartment of Computer Engineering, Olabisi Onabanjo University, Ago-Iwoye, NigeriaDepartment of Computer Engineering, Olabisi Onabanjo University, Ago-Iwoye, NigeriaDepartment of Electrical and Electronic Engineering, Olabisi Onabanjo University, Ago-Iwoye, NigeriaDepartment of Electrical and Electronic Engineering, Olabisi Onabanjo University, Ago-Iwoye, NigeriaDepartment of Electrical and Electronic Engineering, Olabisi Onabanjo University, Ago-Iwoye, NigeriaThe 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.http://azojete.com.ng/index.php/azojete/article/view/491 |
| spellingShingle | M. O. Osifeko O. Folorunsho O. I. Sanusi P. O. Alao O. O. Ade-Ikuesan O. G. Olasunkanmi Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, Nigeria Arid Zone Journal of Engineering, Technology and Environment |
| title | Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, Nigeria |
| title_full | Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, Nigeria |
| title_fullStr | Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, Nigeria |
| title_full_unstemmed | Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, Nigeria |
| title_short | Three-Tier Neural Network Forecast of Power Output from a Mini Photovoltaic Plant in Ogun State, Nigeria |
| title_sort | three tier neural network forecast of power output from a mini photovoltaic plant in ogun state nigeria |
| url | http://azojete.com.ng/index.php/azojete/article/view/491 |
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