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

Farhad Khosrojerdi, Stéphane Gagnon and Raul Valverde ()
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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

Energies, 2025, vol. 18, issue 11, 1-12

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.

Keywords: solar energy; energy forecasting; PSC; sustainable development; MPPT; SDGs (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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