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General overview of artificial neural network applications in renewable energy systems

Berk Erbil Yağcı, Gözde Demirsoy, Alper Nabi Akpolat

Abstract


A country's primary aim is to ensure sustainable development, with clean and accessible energy vital to this goal. Achieving clean and accessible energy requires maximizing the use of renewable energy resources. Significant steps have been taken globally to increase the share of renewable energy in production and consumption to support this objective. This includes establishing national and international policies, setting strategic targets, and implementing necessary measures. Furthermore, the advancement of technology has led to rapid growth in renewable energy-based electricity generation in recent years. This growth has created a need for predictability in electricity generation, distribution, transmission, and financing to balance supply and demand effectively. As a result, integrating artificial intelligence (AI) applications has become essential for digital transformation in the energy sector. This study aims to explore the use of artificial neural networks (ANNs) for predictability in the renewable energy sector by reviewing literature from 2017 to 2024. The focus is primarily on studies published in academic journals indexed in the Science Citation Index (SCI). The research examines the relationship between algorithms, prediction levels, and renewable energy. The study concludes with findings and recommendations intended to contribute to the digital transformation of the renewable energy industry. As a country with significant renewable energy potential such as solar and wind, Türkiye can manage the supply-demand balance more effectively by integrating technologies such as AI and ANNs into the energy sector.


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References


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URN: https://sloi.org/urn:sl:tjoee93328



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