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Leveraging AI for Sustainable Energy Development in Solar Power Plants Operating Under Shading Conditions

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Listed:
  • Farhad Khosrojerdi

    (Cando Green Construction Inc., 14845 Yonge St., Aurora, ON L4G 6H8, Canada)

  • Stéphane Gagnon

    (Département des Sciences Administratives, Université du Québec en Outaouais, Gatineau, QC J7X 3X7, Canada)

  • Raul Valverde

    (John Molson School of Business, Concordia University, Montreal, QC H3H 0A1, Canada)

Abstract

In a photovoltaic (PV) system, shading caused by weather and environmental factors can significantly impact electricity production. For over a decade, artificial intelligence (AI) techniques have been applied to enhance energy production efficiency in the solar energy sector. This paper demonstrates how AI-based control systems can improve energy output in a solar power plant under shading conditions. The findings highlight that AI contributes to the sustainable development of the solar power sector. Specifically, maximum power point tracking (MPPT) control systems, utilizing metaheuristic and computer-based algorithms, enable PV arrays to mitigate the impacts of shading effectively. The effect of shading on a PV module is also simulated using MATLAB R2018b. Using actual PV data from a solar power plant, power outputs are compared in two scenarios: (I) PV systems without a control system and (II) PV arrays equipped with MPPT boards. The System Advisor Model (SAM) is employed to calculate the monthly energy output of the case study. The results confirm that PV systems using MPPT technology generate significantly more monthly energy compared to those without MPPTs.

Suggested Citation

  • Farhad Khosrojerdi & Stéphane Gagnon & Raul Valverde, 2025. "Leveraging AI for Sustainable Energy Development in Solar Power Plants Operating Under Shading Conditions," Energies, MDPI, vol. 18(11), pages 1-12, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2960-:d:1671688
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    References listed on IDEAS

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    1. Jordehi, A. Rezaee, 2016. "Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1127-1138.
    2. Carole Brunet & Oumarou Savadogo & Pierre Baptiste & Michel A Bouchard & Jean Chrysostome Rakotoary & Andry Ravoninjatovo & Céline Cholez & Corinne Gendron & Nicolas Merveille, 2020. "Impacts Generated by a Large-Scale Solar Photovoltaic Power Plant Can Lead to Conflicts between Sustainable Development Goals: A Review of Key Lessons Learned in Madagascar," Sustainability, MDPI, vol. 12(18), pages 1-33, September.
    3. Najwa Syahirah Mohamed Nor Izam & Zarina Itam & Wong Leong Sing & Agusril Syamsir, 2022. "Sustainable Development Perspectives of Solar Energy Technologies with Focus on Solar Photovoltaic—A Review," Energies, MDPI, vol. 15(8), pages 1-15, April.
    4. Roberto Pierdicca & Marina Paolanti & Andrea Felicetti & Fabio Piccinini & Primo Zingaretti, 2020. "Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images," Energies, MDPI, vol. 13(24), pages 1-17, December.
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