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Improving Renewable Energy Production Systems Using Artificial Intelligence
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Author(s): Fatima Zahra Aoujil (Biotechnology, Materials, and Environment Team, Faculty of Sciences Agadir, Ibn Zohr University, Morocco), Amol D. Vibhute (Symbiosis Institute of Computer Studies and Research, Pune, India), Yassine Mouniane (Laboratory of Natural Resources and Sustainable Development, Ibn Tofaïl University, Morocco), Slimane Hadjab (Condensed Matter Physics and Nanomaterials Laboratory, University of Jijel, Algeria), Imane Aitouhanni (Mohammed V University in Rabat, Morocco), Soumaya Choukri (Laboratory of Natural Resources and Sustainable Development, Ibn Tofaïl University, Morocco), Fatima Amallal (Physics, Energy, and Information Processing Laboratory, University Ibn Zohr, Morocco)and Brahim El Ouardi (Faculty of Sciences, Ibn Tofail University, Morocco)
Copyright: 2025
Pages: 12
Source title:
Integrating Artificial Intelligence Into the Energy Sector
Source Author(s)/Editor(s): Abdelkader Mohamed Sghaier Derbali (Taibah University, Saudi Arabia)
DOI: 10.4018/979-8-3693-7112-1.ch007
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Abstract
This article examines the use of artificial intelligence (AI) into the optimization of renewable energy production systems, primarily focusing on solar energy. In light of the growing demand for renewable energy sources and the difficulties associated with their intermittent production, IA appears to be a promising approach to increase these systems' reliability and efficiency. A thorough analysis of the effect of the IA on the effectiveness of renewable energy generation systems is presented. It investigates the machine learning approaches used to forecast energy output based on historical and meteorological data and suggests intelligent storage and management strategies. In order to determine the most effective solutions, a comparison of the performance of several optimization algorithms is conducted. The anticipated studies show a significant increase in the efficiency of renewable energy production systems, a decrease in production costs, and an improvement in the accuracy of energy output forecasts.
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