A review of enhancing wind power with AI: applications, economic implications, and green innovations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F25%3A10258033" target="_blank" >RIV/61989100:27360/25:10258033 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s44265-025-00059-4" target="_blank" >https://link.springer.com/article/10.1007/s44265-025-00059-4</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
A review of enhancing wind power with AI: applications, economic implications, and green innovations
Original language description
Wind energy, a renewable resource characterized by its inexhaustibility and absence of pollutants, has garnered signifcant attention in recent years. The optimization of wind power generation for both economic and environmental benefts has emerged as a solution to contemporary energy challenges. Artifcial intelligence (AI), particularly machine learning (ML), enhances the efciency and sustainability of power generation in wind energy systems. This study employs a systematic literature review (SLR) methodology to examine the relevant literature. The fndings indicate that AI, predominantly represented by ML and hybrid AI models, contributes to wind energy systems in three primary domains: frst, the forecasting and analysis of variables, second the optimization of wind turbines (WTs) performance through advanced maintenance management and condition monitoring, and fnally wind farm layout and optimization. Subsequently, we discussed how AI facilitates optimizes employment and energy consumption structures, promotes the green transformation of wind power enterprises, and drives innovation in the wind power industry through wind variable forecasting and turbine maintenance. The application of AI in the wind energy domain presents opportunities for restructuring the energy landscape. Eforts could be made to accelerate AI-driven innovation in the renewable energy sector and promote transformative reorganization of the energy industry.
Czech name
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Czech description
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Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Digital Economy and Sustainable Development
ISSN
2731-9423
e-ISSN
2731-9423
Volume of the periodical
2025
Issue of the periodical within the volume
3:11
Country of publishing house
SG - SINGAPORE
Number of pages
29
Pages from-to
1-29
UT code for WoS article
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EID of the result in the Scopus database
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