Genetic algorithms-based optimal site selection of solar PV in the north of Afghanistan

Qasimi, Abdul Baser; Toomanian, Ara; Nasri, Farhad; Samany, Najmeh Neysani

doi:10.1080/14786451.2023.2246081

Cited by 2 publications

(1 citation statement)

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“…To generate offspring chromosomes, it is necessary to carry out crossover or recombination procedures on pairs of parent chromosomes chosen separately. To develop new candidate solutions, this includes the exchange of genetic information between the parents as mentioned earlier [225], [226].…”

Section: E Genetic Algorithmsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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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.

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Section: E Genetic Algorithmsmentioning

confidence: 99%