Generative Design in Development of Mechanical Components for Magnus Effect-Based Wind Turbine

Lukin, Aleksandr; Perepelkina, Svetlana; Demidova, Galina L.; Lukichev, Dmitry V.; Rassõlkin, Anton; Vaimann, Toomas; Kallaste, Ants

doi:10.1109/icepds47235.2020.9249344

Cited by 3 publications

(1 citation statement)

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“…The significant mass reduction achieved in the components of the wind turbine underscores the potential of AI-driven design approaches in enhancing the efficiency and performance of wind energy systems. This study provides valuable insights into the application of AI in the development of more efficient and sustainable wind energy technologies (Lukin et al, 2020).…”

Section: Ai-driven Innovations In Wind Energy Optimizationmentioning

confidence: 99%

AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization

Nzubechukwu Chukwudum Ohalete,

Adebayo Olusegun Aderibigbe,

Emmanuel Chigozie Ani

et al. 2023

World J. Adv. Res. Rev.

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This comprehensive review explores the transformative role of artificial intelligence (AI) and data science in the renewable energy sector, with a particular focus on solar and wind energy optimization. The study systematically examines the intersection of AI and renewable energy, highlighting the emergence of AI-driven solutions and their impact on enhancing the efficiency, reliability, and sustainability of renewable energy systems. The review begins with an overview of renewable energy and its growing importance in the global energy mix, emphasizing the critical role of AI in this sector. It then delves into the methodological approach, outlining the research strategy and criteria for selecting relevant AI and data science studies in renewable energy. This includes a detailed analysis of data collection and synthesis techniques used to identify key AI innovations and trends in solar and wind energy optimization. The core of the review comprises an extensive literature survey on AI applications in solar and wind energy systems. It covers fundamental principles of AI in renewable energy, state-of-the-art data science techniques, and emerging trends such as novel AI algorithms and their integration into renewable energy grids. The study evaluates the technological, economic, and environmental impacts of AI in renewable energy, addressing challenges and proposing solutions. Furthermore, the review discusses the role of standards and regulatory frameworks in AI-driven renewable energy and the implications for stakeholders. It concludes with a summary of AI's role in enhancing renewable energy, future prospects, and recommendations for industry leaders and policymakers. This review provides a thorough understanding of the current state and future potential of AI in renewable energy, offering valuable insights for researchers, industry professionals, and policymakers engaged in the field of sustainable energy.

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Section: Ai-driven Innovations In Wind Energy Optimizationmentioning

confidence: 99%