Artificial intelligence applications in solar energy

Thanh Tuan Le - HUTECH University, Ho Chi Minh
Thi Thai Le - Hanoi University of Science and Technology, Hanoi
Huu Cuong Le - Ho Chi Minh City University of Transport, Ho Chi Minh
Van Huong Dong - Ho Chi Minh City University of Transport, Ho Chi Minh
Prabhu Paramasivam - SIMATS, Chennai, Tamilnadu
Nghia Chung - Ho Chi Minh City University of Transport, Ho Chi Minh

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.


Solar energy; Machine learning; Soft computing; Neural networks; Genetic algorithm

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