Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability
This paper aims to develop an analytical model for the prediction of the electricity produced in a Photovoltaic Power Station (PVS). In this context, the developed mathematical model is implemented in a Simulink Model. The obtained simulation results are compared to the experimental data, the result...
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my.uniten.dspace-342172024-10-14T11:18:28Z Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability Alhousni F.K. Alnaimi F.B.I. Okonkwo P.C. Ben Belgacem I. Mohamed H. Barhoumi E.M. 57215064807 58027086700 56720223700 57205617086 57136356100 35766392000 analytical model experimental results photovoltaic prediction analytical method dust electricity generation optimization photovoltaic system power generation This paper aims to develop an analytical model for the prediction of the electricity produced in a Photovoltaic Power Station (PVS). In this context, the developed mathematical model is implemented in a Simulink Model. The obtained simulation results are compared to the experimental data, the results obtained from the software Homer-Pro model, and the results given by the online PV calculator (Photovoltaic Geographical Information System), developed by the European commission. The comparison results show the reliability of the developed analytical model for specific months of the year. However, an error of 10% between simulations and experimental results is observed for July and August. This error is mainly due to the effects of humidity and dust that were not considered in the analytical model. Nevertheless, the monthly and yearly produced electricity values show the robustness of the proposed model to predict the PVS generated power. The developed model will be used as a powerful tool for data prediction and the optimization of electricity generation. This permits us to reduce the losses in power generation by optimizing the connected generating power stations to the power grid. � 2023 by the authors. Final 2024-10-14T03:18:28Z 2024-10-14T03:18:28Z 2023 Article 10.3390/su15118904 2-s2.0-85161472549 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161472549&doi=10.3390%2fsu15118904&partnerID=40&md5=7f5c7c76b8a35959b91f2e74be65171c https://irepository.uniten.edu.my/handle/123456789/34217 15 11 8904 All Open Access Gold Open Access MDPI Scopus |
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analytical model experimental results photovoltaic prediction analytical method dust electricity generation optimization photovoltaic system power generation |
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analytical model experimental results photovoltaic prediction analytical method dust electricity generation optimization photovoltaic system power generation Alhousni F.K. Alnaimi F.B.I. Okonkwo P.C. Ben Belgacem I. Mohamed H. Barhoumi E.M. Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability |
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This paper aims to develop an analytical model for the prediction of the electricity produced in a Photovoltaic Power Station (PVS). In this context, the developed mathematical model is implemented in a Simulink Model. The obtained simulation results are compared to the experimental data, the results obtained from the software Homer-Pro model, and the results given by the online PV calculator (Photovoltaic Geographical Information System), developed by the European commission. The comparison results show the reliability of the developed analytical model for specific months of the year. However, an error of 10% between simulations and experimental results is observed for July and August. This error is mainly due to the effects of humidity and dust that were not considered in the analytical model. Nevertheless, the monthly and yearly produced electricity values show the robustness of the proposed model to predict the PVS generated power. The developed model will be used as a powerful tool for data prediction and the optimization of electricity generation. This permits us to reduce the losses in power generation by optimizing the connected generating power stations to the power grid. � 2023 by the authors. |
| author2 |
57215064807 |
| author_facet |
57215064807 Alhousni F.K. Alnaimi F.B.I. Okonkwo P.C. Ben Belgacem I. Mohamed H. Barhoumi E.M. |
| format |
Article |
| author |
Alhousni F.K. Alnaimi F.B.I. Okonkwo P.C. Ben Belgacem I. Mohamed H. Barhoumi E.M. |
| author_sort |
Alhousni F.K. |
| title |
Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability |
| title_short |
Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability |
| title_full |
Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability |
| title_fullStr |
Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability |
| title_full_unstemmed |
Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability |
| title_sort |
photovoltaic power prediction using analytical models and homer-pro: investigation of results reliability |
| publisher |
MDPI |
| publishDate |
2024 |
| _version_ |
1814061171817316352 |
| score |
13.222552 |