Publication | Open Access
Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage
172
Citations
108
References
2024
Year
Electrical EngineeringRenewable Energy RevolutionEngineeringMachine LearningData ScienceSmart GridEnergy ConversionSustainable EnergyWind TurbinesEnergy StorageSolar WindWind EnergyAlternative Energy SolutionRenewable Energy SystemsEnergy PredictionClean EnergyRenewable Energy Manufacturing
The article evaluates the current global adoption of solar and wind energy, their benefits and limitations, and highlights the critical role of machine learning in advancing sustainable energy production. It reviews the historical growth, emerging technologies such as floating solar and vertical‑axis turbines, smart grid and storage solutions, and how machine learning can optimize design, reduce costs, and improve performance of solar and wind systems. The review finds that electric vehicles, economic gains, technological advances, and environmental/social impacts collectively demonstrate solar and wind energy’s strong potential to meet global demand, offering policymakers and industry leaders a valuable resource for sustainable development.
Abstract 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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