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=02986a1a6a4bcd62967f83e278b9edc6},
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.
Masuda, Y.; Taylor, J.; Gagnon, S.; Raisinghani, M.; Jain, R.; Jacobson, I.
IS LEADERSHIP EDUCATION FOR SUSTAINABLE INNOVATION AND DIGITAL TRANSFORMATION: EFFECTIVENESS AND CHALLENGES Proceedings Article
In: Proc. AIS SIGED Int. Conf. Inf. Syst. Educ. Res., ICISER, Association for Information Systems, 2024.
Abstract | Links | BibTeX | Tags: Data quality, Digital Innovation, Digital innovations, Digital transformation, Education computing, Enterprise Architecture, Human resource management, Information systems, IS Community, IS leadership, Knowledge management, Leadership education, Rapid growth, Sustainability, sustainable development, Value creation, Workers'
@inproceedings{masuda_is_2024,
title = {IS LEADERSHIP EDUCATION FOR SUSTAINABLE INNOVATION AND DIGITAL TRANSFORMATION: EFFECTIVENESS AND CHALLENGES},
author = {Y. Masuda and J. Taylor and S. Gagnon and M. Raisinghani and R. Jain and I. Jacobson},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105010296891&partnerID=40&md5=73b27057e28ff407e1a59116e34be23c},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. AIS SIGED Int. Conf. Inf. Syst. Educ. Res., ICISER},
publisher = {Association for Information Systems},
abstract = {The rapid growth of AI is enabling new capabilities for organizations in knowledge management and requiring new worker capabilities in value creation. New generative AI capabilities can facilitate improved data quality and better worker support. However, best practices for effective and safe use of generative AI are still being established. Scholars must continue to develop their curriculum and pedagogy to prepare students for these new challenges. This panel focuses on educating members for Sustainable Leadership positions in IS within the context of Digital Innovation and Transformation, utilizing the framework that encompasses Digital Strategy and Enterprise Architecture. © 2024 ICISER. All Rights Reserved.},
keywords = {Data quality, Digital Innovation, Digital innovations, Digital transformation, Education computing, Enterprise Architecture, Human resource management, Information systems, IS Community, IS leadership, Knowledge management, Leadership education, Rapid growth, Sustainability, sustainable development, Value creation, Workers'},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid growth of AI is enabling new capabilities for organizations in knowledge management and requiring new worker capabilities in value creation. New generative AI capabilities can facilitate improved data quality and better worker support. However, best practices for effective and safe use of generative AI are still being established. Scholars must continue to develop their curriculum and pedagogy to prepare students for these new challenges. This panel focuses on educating members for Sustainable Leadership positions in IS within the context of Digital Innovation and Transformation, utilizing the framework that encompasses Digital Strategy and Enterprise Architecture. © 2024 ICISER. All Rights Reserved.