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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Chaib, Messaouda
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Control & Systems Engineering Electrical & Electronics Engineering

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Evaluation of the operational efficiency of 3 MWp photovoltaic power plant situated in the Sahara Desert of Algeria Chaib, Messaouda; Dahbi, Abdeldjalil; Benatiallah, Ali
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2618-2630

Abstract

The primary aim of this study is to examine the behaviour of the photovoltaic plant installed in Kabertene, Algeria, under desert climate conditions. Simulations and modeling have been carried out for the photovoltaic cell, panel, and array under standard test conditions as well as a range of different operating conditions. Furthermore, the photovoltaic (PV) plant has been simulated using measured data, PVsyst, and MATLAB Simulink. The aim was to assess the solar energy potential within the examined geographic area and evaluate the performance of the PV plant. The performance of the obtained results was compared in different cases. The real results closely match those of PVsyst and MATLAB software when using real data and the real performance ratio, because it takes into consideration the cleanliness state of the PV panels. The results indicate that environmental factors, especially solar radiation and temperature, exert a considerable effect on the operational capacity of the PV system. This leads to variations in the electrical energy output and the performance ratio of the system. The total yearly energy injected into the grid was 5028.21625 MWh. The annual average daily PV system performance ratio is 67%. Finally, the research concludes that the area has substantial solar potential, encouraging the development of new PV plants.
Machine learning based models for solar energy Cherifi, Dalila; Dahbi, Abdeldjalil; Sebbane, Mohamed Lamine; Baali, Bassem; Kadri, Ahmed Yassine; Chaib, Messaouda
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp752-764

Abstract

Photovoltaic (PV) technology is one of the most promising forms of renewable energy. However, power generation from PV technologies is highly dependent on variable weather conditions, which are neither constant nor controllable, which can affect grid stability. Accurate forecasting of PV power production is essential to ensure reliable operation within the power system. The primary challenge of this study is to accurately predict photovoltaic energy production, considering that weather conditions, such as irradiance, temperature, and wind speed, are random variables. The key contribution of this article is developing a machine learning model to predict the energy production of a real PV power plant in Algeria. Using real measurements sourced from the Center of Renewable Energy Development (CDER) in Adrar, Algeria, in 2021. The data are from two PV power plants located in harsh desert climate conditions. The results presented in this study offer a comparison of several predictive methods applied to real-world data from a PV power plant situated in the Saharan Region. Our findings reveal that the artificial neural network (ANN) model yields the most accurate predictions of 94.96%, with the smallest prediction error: root mean square (RMSE) and mean absolute error (MAE) are 7.78% and 3.80%, respectively.
Co-Authors Ali Benatiallah Baali, Bassem Cherifi, Dalila Dahbi, Abdeldjalil Kadri, Ahmed Yassine Sebbane, Mohamed Lamine