Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability

Fadhil Khadoum Alhousni (), Firas Basim Ismail Alnaimi, Paul C. Okonkwo, Ikram Ben Belgacem, Hassan Mohamed and El Manaa Barhoumi
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Fadhil Khadoum Alhousni: Power Generation Unit, Institute of Power Engineering (IPE), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia
Firas Basim Ismail Alnaimi: Power Generation Unit, Institute of Power Engineering (IPE), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia
Paul C. Okonkwo: College of Engineering, Dhofar University, Salalah 211, Oman
Ikram Ben Belgacem: Laboratoire de Génie Mécanique, Ecole Nationale d’Ingénieurs, Université de Monastir, Monastir 5019, Tunisia
Hassan Mohamed: Institute of Sustainable Energy, Universiti Tenaga Nasional, Jalan Ikram-UNITEN, Kajang 43000, Selangor, Malaysia
El Manaa Barhoumi: College of Engineering, Dhofar University, Salalah 211, Oman

Sustainability, 2023, vol. 15, issue 11, 1-13

Abstract: 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.

Keywords: photovoltaic; prediction; analytical model; experimental results (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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