Stability and Reliability Analysis for Multiple WT Using Deep Reinforcement Learning

Padmaja, S. M.; Kumar, Akhilesh; Dhanesh, Lavanya; Kamesh, Vinjamuri Venkata; Geetha, H.; Ramya, D.; Sekhar, G. G. Raja; Saha, Nutan

doi:10.1080/15325008.2023.2220313

Cited by 8 publications

(1 citation statement)

References 35 publications

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“…This will result in a reduction in dependency on fossil fuels and will help mitigate climate change. The role that machine learning technologies play in enhancing the efficiency and dependability of solar energy systems will likely expand as these technologies continue to advance [194], [195]. This holds the promise of a more positive and environmentally friendly future powered by renewable energy [196]- [198].…”

Section: Use Of Pearson Correlation For Solar Energy Forecastingmentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

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Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

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