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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sami Florent Palm & Aboubakar Gomna & Sani Moussa Kadri & Dominique Bonkoungou & Adélaïde Lareba Ouedraogo & Yrébégnan Moussa Soro & Marie Sawadogo, 2025. "Performance Study and Implementation of Accurate Solar PV Power Prediction Methods for the Nagréongo Power Plant in Burkina Faso," Energies, MDPI, vol. 18(19), pages 1-30, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:19:p:5285-:d:1765606
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    References listed on IDEAS

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