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AI Approach Towards Optimal Finding of Renewable Sources of Energy and Their Classification

AI Approach Towards Optimal Finding of Renewable Sources of Energy and Their Classification

Pampa Saha, Anshit Mukherjee, Gunjan Mukherjee
Copyright: © 2024 |Pages: 22
ISBN13: 9798369323557|ISBN13 Softcover: 9798369347041|EISBN13: 9798369323564
DOI: 10.4018/979-8-3693-2355-7.ch005
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MLA

Saha, Pampa, et al. "AI Approach Towards Optimal Finding of Renewable Sources of Energy and Their Classification." Machine Learning and Computer Vision for Renewable Energy, edited by Pinaki Pratim Acharjya, et al., IGI Global, 2024, pp. 85-106. https://doi.org/10.4018/979-8-3693-2355-7.ch005

APA

Saha, P., Mukherjee, A., & Mukherjee, G. (2024). AI Approach Towards Optimal Finding of Renewable Sources of Energy and Their Classification. In P. Acharjya, S. Koley, & S. Barman (Eds.), Machine Learning and Computer Vision for Renewable Energy (pp. 85-106). IGI Global. https://doi.org/10.4018/979-8-3693-2355-7.ch005

Chicago

Saha, Pampa, Anshit Mukherjee, and Gunjan Mukherjee. "AI Approach Towards Optimal Finding of Renewable Sources of Energy and Their Classification." In Machine Learning and Computer Vision for Renewable Energy, edited by Pinaki Pratim Acharjya, Santanu Koley, and Subhabrata Barman, 85-106. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-2355-7.ch005

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Abstract

The smart energy domain poses substantial challenges in future research, necessitating advanced investigation into optimizing smart customizable networks using artificial intelligence (AI) and machine learning (ML). With renewable energy (RE) being pivotal for global development amid climate change, AI introduces new paradigms to reshape activities, demanding revamped energy infrastructure and RE deployment strategies. The chapter explores the adoption of AI in future smart cities research with considerable economic benefits. Moreover, in the field of environmental science and engineering (ESE), the ML's potential in revolutionizing ESE by addressing complex problems and outlining crucial components for successful ML implementation in ESE, correct model development, proper interpretation, and applicability analysis has been done. The renewable source of energy like solar and wind energy can be generated in place where they are found in plenty. The ML application of energy source tracing and the prediction of energy type with multiple models of efficiencies has been highlighted.

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