Artificial Intelligence-Based Deep Learning Model for the Performance Enhancement of Photovoltaic Panels in Solar Energy Systems

Meena, Radhey Shyam; Singh, Anoop; Urhekar, Shilpa; RohitBhakar,; Garg, Neeraj; Israr, Mohammad; Kothari, D. P.; Chiranjeevi, C.; Srinivasan, Prasath

doi:10.1155/2022/3437364

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…Available groundwater and solar energy access in sensitive watersheds may jeopardize water security through excessive abstraction. Solar power generation is inherently unreliable due to its reliance on weather deviations; to estimate how well solar power generation can generate energy at a specific location at a particular moment, weather predictions must be prepared in advance [34]. One of the limitations of the research is that solar cells have a lower power conversion efficiency (PCE) than conventional PV cells.…”

Section: Challenges and Future Scopementioning

confidence: 99%

Review on green resources and AI for biogenic solar power

Bhattacharjee,

Roy

2024

Energ. Stor. Conv.

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The need for clean and renewable energy has grown dramatically during the past few years. As potential candidates for producing green energy in this region, photovoltaic and bio-solar energy technologies have arisen. This review presents a novel approach for designing and developing photovoltaics and bio-solar cells using eco-friendly materials and artificial intelligence (AI) techniques. An intriguing architecture is outlined for a bio-solar cell that fuses photovoltaic electronics with photosynthetic organisms. A recyclable thin-film solar cell serves as the basis of our photovoltaic system. To further maximize the effectiveness of the device, we use AI algorithms. According to statistical calculations, the proposed bio-solar cell can produce a sizable amount of electricity while being ecologically sound. This paper outlines significant advances in developing solar cells and photovoltaics using green nanomaterials and AI, which provide exciting potential for improving energy harvesting capacity. This review also presents an overview of the effects of the potential commercialization of our strategy, its social and environmental benefits, and its pitfalls.

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Section: Challenges and Future Scopementioning

confidence: 99%

Review on green resources and AI for biogenic solar power

Bhattacharjee,

Roy

2024

Energ. Stor. Conv.

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“…Their findings demonstrated that optimizing artificial intelligence could improve solar tracking systems. In a related study, Meena et al (2022) utilized AI algorithms to improve the performance and reduce the cost of photovoltaic (PV) systems, indicating that AI‐based solutions may be able to reduce the environmental impact of PV power systems.…”

Section: Applications Of Artificial Intelligence In Solar and Wind En...mentioning

confidence: 99%

Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage

Bin Abu Sofian,

Lim,

Siti Halimatul Munawaroh

et al. 2024

Sustainable Development

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This article evaluates the present global condition of solar and wind energy adoption and explores their benefits and limitations in meeting energy needs. It examines the historical and evolutionary growth of solar and wind energy, global trends in the usage of renewable energy, and upcoming technologies, including floating solar and vertical‐axis wind turbines. The importance of smart grid technology and energy storage alternatives for enhancing the effectiveness and dependability of renewable energy is explored. In addition, the role of Electric Vehicles (EVs) in a modern smart grid has been assessed. Furthermore, the economic benefits, and most recent technological developments of solar and wind energy and their environmental and social ramifications. The potential of solar and wind energy to meet the increasing global energy demand and the problems and opportunities facing the renewable energy industry have shown excellent promise. Machine learning applications for solar and wind energy generation are vital for sustainable energy production. Machine learning can help in design, optimization, cost reduction, and, most importantly, in improving the efficacy of solar and wind energy, including advancing energy storage. This assessment is a crucial resource for policymakers, industry leaders, and researchers who aim to make the world cleaner and more sustainable. Ultimately, this review has shown the great potential of solar and wind energy in meeting global energy demands and sustainable goals.

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“…This confirms the efficacy and viability of employing a "Convolutional neural network model" like "SolarNet" for forecasting solar radiation and emphasizes the possibility for new research areas in forecasting. Meena et al, 2022 investigate how "Solar power systems," particularly "Standalone, grid-connected," and hybrid systems, can be scaled using artificial intelligence (AI) techniques to lessen their environmental impact. The paper presents a hybrid AI sizing model built on multilayered perceptrons that combines the advantages of both AI and traditional sizing techniques.…”

Section: Studies Using Ai For Enhancement Of Pv In Solar Energy Systemsmentioning

confidence: 99%

A comparative study of AI-driven techno-economic analysis for grid-tied solar PV-fuel cell hybrid power systems

Soni,

Vikramaditya Dave,

Sujit Kumar

et al. 2024

The Scientific Temper

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The increasing global demand for clean and sustainable energy solutions has led to the integration of “renewable energy sources” and innovative technologies in power generation systems. In this context, “grid-tied solar photovoltaic (PV)-fuel cell hybrid power systems” have drawn considerable interest because of their potential to provide reliable and efficient energy generation. However, the complex interaction between solar PV and fuel cell components and the fluctuating nature of renewable energy sources necessitates a comprehensive techno-economic analysis. This research paper presents a comparative study of AI-driven approaches for conducting techno-economic analysis of “grid-tied solar PV-fuel cell hybrid power systems.” The study uses various AI techniques to analyze these hybrid systems' performance, economic viability, and environmental impact.

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Artificial Intelligence-Based Deep Learning Model for the Performance Enhancement of Photovoltaic Panels in Solar Energy Systems

Cited by 4 publications

References 36 publications

Review on green resources and AI for biogenic solar power

Review on green resources and AI for biogenic solar power

Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage

A comparative study of AI-driven techno-economic analysis for grid-tied solar PV-fuel cell hybrid power systems

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