Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Khosrojerdi, F.; Gagnon, S.; Valverde, R.
Leveraging AI for Sustainable Energy Development in Solar Power Plants Operating Under Shading Conditions Journal Article
In: Energies, vol. 18, no. 11, 2025, ISSN: 19961073 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Abstract | Links | BibTeX | Tags: Computer control systems, Condition, Electric power system control, Energy, Energy forecasting, Energy output, Maximum Power Point Tracking, MPPT, Photovoltaic arrays, Photovoltaic systems, Power, PSC, SDGs, Solar energy, Solar fuels, Solar heating, sustainable development, Sustainable energy development
@article{khosrojerdi_leveraging_2025,
title = {Leveraging AI for Sustainable Energy Development in Solar Power Plants Operating Under Shading Conditions},
author = {F. Khosrojerdi and S. Gagnon and R. Valverde},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007798431&doi=10.3390%2fen18112960&partnerID=40&md5=2a1c721151c469168b124841a0edd4d7},
doi = {10.3390/en18112960},
issn = {19961073 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Energies},
volume = {18},
number = {11},
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. © 2025 by the authors.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {Computer control systems, Condition, Electric power system control, Energy, Energy forecasting, Energy output, Maximum Power Point Tracking, MPPT, Photovoltaic arrays, Photovoltaic systems, Power, PSC, SDGs, Solar energy, Solar fuels, Solar heating, sustainable development, Sustainable energy development},
pubstate = {published},
tppubtype = {article}
}
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. © 2025 by the authors.
2.
Khosrojerdi, F.; Gagnon, S.; Valverde, R.
Full Shading for Photovoltaic Systems Operating under Snow Conditions Proceedings Article
In: 2021 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2021, pp. 82–87, Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-0-7381-4535-8.
Abstract | Links | BibTeX | Tags: Computer software, Condition, Energy consumer, Energy forecasting, Photovoltaic cells, Photovoltaic planning, Photovoltaic shading, Photovoltaic systems, Photovoltaics, Snow, Snow-covered module, Solar energy, Solar power generation, Solar power plants, USC
@inproceedings{khosrojerdi_full_2021,
title = {Full Shading for Photovoltaic Systems Operating under Snow Conditions},
author = {F. Khosrojerdi and S. Gagnon and R. Valverde},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116021648&doi=10.1109%2fSEGE52446.2021.9534991&partnerID=40&md5=3a4487157a809d03ae5c46c10499f865},
doi = {10.1109/SEGE52446.2021.9534991},
isbn = {978-0-7381-4535-8},
year = {2021},
date = {2021-01-01},
booktitle = {2021 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2021},
pages = {82–87},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Photovoltaic (PV) installers and non-technical solar energy consumers use PV planning software for the system design and simulation. End-users rely on the designed system and power estimations provided by these tools. However, most planning software products fail to consider shading conditions. This problem affects energy forecasting for solar power plants located in cold climates. In this paper, we define the status of full shading for a snow-covered panel and the minimum depth of snow creating it. Using a case study, we design the project by the most reliable planning software, System Advisor Model (SAM). We show that the simulation overestimates power generations for snowy months. To identify shading conditions and the correlated performance reductions, we compare the SAM results with the measured data collected onsite. As a result, the minimum depth of snow that can create full shading and zero production is detected. Moreover, comparing the measured data with the simulated power helps us to define a rule-base system providing PV performance reduction factors. It assists solar sector practitioners to plan a PV project accurately, especially for the locations where snowfall is an important environmental factor for several months. © 2021 IEEE.},
keywords = {Computer software, Condition, Energy consumer, Energy forecasting, Photovoltaic cells, Photovoltaic planning, Photovoltaic shading, Photovoltaic systems, Photovoltaics, Snow, Snow-covered module, Solar energy, Solar power generation, Solar power plants, USC},
pubstate = {published},
tppubtype = {inproceedings}
}
Photovoltaic (PV) installers and non-technical solar energy consumers use PV planning software for the system design and simulation. End-users rely on the designed system and power estimations provided by these tools. However, most planning software products fail to consider shading conditions. This problem affects energy forecasting for solar power plants located in cold climates. In this paper, we define the status of full shading for a snow-covered panel and the minimum depth of snow creating it. Using a case study, we design the project by the most reliable planning software, System Advisor Model (SAM). We show that the simulation overestimates power generations for snowy months. To identify shading conditions and the correlated performance reductions, we compare the SAM results with the measured data collected onsite. As a result, the minimum depth of snow that can create full shading and zero production is detected. Moreover, comparing the measured data with the simulated power helps us to define a rule-base system providing PV performance reduction factors. It assists solar sector practitioners to plan a PV project accurately, especially for the locations where snowfall is an important environmental factor for several months. © 2021 IEEE.