Performance Study and Implementation of Accurate Solar PV Power Prediction Methods for the Nagréongo Power Plant in Burkina Faso

Sami Florent Palm (), Aboubakar Gomna, Sani Moussa Kadri, Dominique Bonkoungou, Adélaïde Lareba Ouedraogo, Yrébégnan Moussa Soro and Marie Sawadogo
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Sami Florent Palm: Laboratoire Energies Renouvelable et Efficacité Energétique, Institut International d’Ingénierie de l’Eau et de l’Environnement (2iE), Rue de la Science, Ouagadougou 01 BP 594, Burkina Faso
Aboubakar Gomna: Laboratoire Energies Renouvelable et Efficacité Energétique, Institut International d’Ingénierie de l’Eau et de l’Environnement (2iE), Rue de la Science, Ouagadougou 01 BP 594, Burkina Faso
Sani Moussa Kadri: Laboratoire Energies Renouvelable et Efficacité Energétique, Institut International d’Ingénierie de l’Eau et de l’Environnement (2iE), Rue de la Science, Ouagadougou 01 BP 594, Burkina Faso
Dominique Bonkoungou: Science and Technology Laboratory (LaST), Thomas Sankara University, Ouagadougou 01 BP 12792, Burkina Faso
Adélaïde Lareba Ouedraogo: Training and Research Unit in Exact and Applied Sciences (UFR/SEA), Laboratory of Thermal and Renewable Energies (LETRE), Joseph Ki-Zebro University, Ouagadougou 09 BP 1635, Burkina Faso
Yrébégnan Moussa Soro: Laboratoire Energies Renouvelable et Efficacité Energétique, Institut International d’Ingénierie de l’Eau et de l’Environnement (2iE), Rue de la Science, Ouagadougou 01 BP 594, Burkina Faso
Marie Sawadogo: Laboratoire Energies Renouvelable et Efficacité Energétique, Institut International d’Ingénierie de l’Eau et de l’Environnement (2iE), Rue de la Science, Ouagadougou 01 BP 594, Burkina Faso

Energies, 2025, vol. 18, issue 19, 1-30

Abstract: This study aimed to implement an effective power prediction method to support the optimal management of the 30 MW Nagréongo solar photovoltaic (PV) plant in Burkina Faso. Initially, the performance of the PV plant was assessed by an external consultant based on data recorded in 2023 and 2024, revealing efficiency with a performance ratio (PR) of 73.73% in 2023, which improved to 77.43% in 2024. To forecast the plant’s power output, several deep learning models—namely LSTM, a GRU, LSTM-GRU, and an RNN—were applied using historical power data recorded at five-minute intervals during the 2024 periods of January–February; March–April; and July–August. All the deep learning models achieved accurate short-term forecasting for the 30 MW Nagréongo PV plant, with the seasonal performance shaped by the Sahelian weather regimes. The GRU performed best during the dry season (nRMSE ≈ 4%) and LSTM excelled in the hot months (nRMSE ≈ 2%), while the hybrid LSTM-GRU model proved most robust under rainy-season variability. Overall, the forecasting errors remained within 2–5% of plant capacity, demonstrating the suitability of these architectures for grid integration and operational planning in Sahel PV systems.

Keywords: solar photovoltaic (PV); power forecasting; deep learning; Nagréongo plant (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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