Review integration of artificial intelligence in solar energy system

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Purva Trivedi; Dr. Arun Parakh; Shurbhit Surage

Review integration of artificial intelligence in solar energy system

Authors: Purva Trivedi, Dr. Arun Parakh, Shurbhit Surage

Unique Paper ID: 181813
Volume: 12
Issue: 1
PageNo: 5596-5600

Keywords: Solar energy machine learning deep learning energy efficiency

Abstract:
In the current energy scenario to meet the global energy demand has become challenging task. The energy demand is increasing day by day. The main hindrance in meeting up the current energy demand is the dependencies on nonrenewable source, limited reservoir of nonrenewable resources for generation of energy. The alteration from nonrenewable to renewable energy sources is pivotal to grapple with the increasing demand and handle handling various environmental issues arises with the burning of non-renewable energy sources. This review paper emphasis on the implementation of machine learning and deep learning methods with conventional solar energy system and analyze performance of solar energy system and optimization after implementing machine learning and deep learning methods of artificial intelligence with conventional solar energy system. The comprehensive analysis in this paper presents that that in spite of marvelous potential exhibit by implementation of machine learning and deep learning method with solar energy system some challenges such as data complexity, system integration and data interpretability are associated with the integration with solar energy system. The aim behind the whole study is to lay a foundation for future research and ensure continuous innovation, implementation of Artificial intelligence technology in solar energy system for gaining energy efficiency and leads future towards sustainable energy generation.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 1
PageNo: 5596-5600

Review integration of artificial intelligence in solar energy system

Available:https://ijirt.org/article?manuscript=181813

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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