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Wind Farm Layout Optimization: Genetic Algorithms, Machine Learning, and Bibliometric Insights
Asfour, Rawan Fathy El-Amin, Mohammed
Asfour, Rawan
Fathy El-Amin, Mohammed
Type
Supervisor
Date
2026-03-28
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Abstract
The optimization of wind farm layouts (WFLO) is pivotal in maximizing energy production while reducing operational costs and environmental impact. This chapter presents a comprehensive framework that investigates Genetic Algorithms (GA) and Machine Learning (ML) techniques for WFLO. A parametric GA-based simulation explores the influence of key parameters, such as population size, hub height, surface roughness, and cost models, on layout efficiency and power output. To enrich this computational foundation, a bibliometric analysis was performed covering 2015-2025 using cleaned and merged data from Scopus and Web of Science, involving 483 unique documents. The analysis classifies WFLO research into major optimization categories such as GA, ML, PSO, and hybrid techniques, and reveals an increasing reliance on data-driven models and hybrid frameworks. A keyword heatmap confirms the growing prominence of ML algorithms like ANN, SVM, and Random Forest in WFLO. The results highlight the evolution of optimization strategies, the synergy between GA and ML, and the emergence of intelligent layout designs that combine predictive accuracy with computational efficiency.
Department
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Sponsor
Effat University
Copyright
Book title
Advances and Applications of Machine Learning in Fluid Flow Problems