Applications of artificial intelligence methods in renewable energy systems

Home Page
About
Submit A Journal
Submit A Conference
Submit Paper/Book
- Submit a Preprint
- Submit a Book
Contact

International Journal of Energy Applications and Technologies
Cilt: 11 Sayı: 1
Applications of artificial intelligence methods in renewable energy systems

Applications of artificial intelligence methods in renewable energy systems

Pages : 86-99

View : 108 | Download : 342

Publication Date : 2025-12-22

Article Type : Review Paper

Abstract :In order to reduce environmental problems caused by fossil fuels and to build a sustainable future, radical transformations are required in energy systems. In this transition process, energy sources characterized by environmental sustainability and renewability play a critical role. Renewable energy systems such as solar, wind and hydroelectric power are strategically important for reducing carbon emissions and ensuring energy security. This study comprehensively examines current applications of artificial intelligence (AI) techniques in these energy systems. In particular, it analyzes the contributions of AI-based solutions in key areas such as production forecasting, predictive maintenance strategies, system performance optimization and smart grid integration. Numerous studies have shown that machine learning methods, deep learning approaches and optimization algorithms enable accurate predictions and effective decision-making to address challenges such as intermittency, variability and uncertainty inherent in renewable energy sources. Moreover, the advantages offered by these technologies in enhancing operational efficiency, minimizing energy losses and supporting long-term environmental sustainability are emphasized. The findings suggest that AI-driven systems will significantly contribute to the digital transformation of the energy sector and play a decisive role in shaping the sustainable, flexible and intelligent energy infrastructures of the future.
Keywords : Artificial intelligence, Forecasting, Machine learning, Renewable energy systems, Smart grid

ORIGINAL ARTICLE URL

* There may have been changes in the journal, article,conference, book, preprint etc. informations. Therefore, it would be appropriate to follow the information on the official page of the source. The information here is shared for informational purposes. IAD is not responsible for incorrect or missing information.