Modelling Wind Energy Potential in Different Regions with Different Methods

Daş, Mehmet; Akpınar, Ebru Kavak; Akpınar, Sinan

doi:10.35378/gujs.795265

Gazi University Journal of Science

2021

DOI: 10.35378/gujs.795265

|View full text |Cite

Modelling Wind Energy Potential in Different Regions with Different Methods

Mehmet Daş

Ebru Kavak Akpınar

Sinan Akpınar

Abstract: Highlights• Modeling of wind power density values with machine learning algorithms.• Wind energy statistical analysis with Weibull cumulative distribution function.• Generating mathematical equations for wind power distribution.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 39 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Unlocking the potential: A review of artificial intelligence applications in wind energy

Dörterler,

Arslan,

Özdemir

2024

Expert Systems

View full text Add to dashboard Cite

This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field.

show abstract

Unlocking the potential: A review of artificial intelligence applications in wind energy

Dörterler,

Arslan,

Özdemir

2024

Expert Systems

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Modelling Wind Energy Potential in Different Regions with Different Methods

Abstract: Highlights• Modeling of wind power density values with machine learning algorithms.• Wind energy statistical analysis with Weibull cumulative distribution function.• Generating mathematical equations for wind power distribution.

Cited by 1 publication

References 39 publications

Unlocking the potential: A review of artificial intelligence applications in wind energy

Unlocking the potential: A review of artificial intelligence applications in wind energy

Contact Info

Product

Resources

About