116 citations as recorded by crossref.

1. Improving site-dependent power curve prediction accuracy using regression trees S. Barber & H. Nordborg https://doi.org/10.1088/1742-6596/1618/6/062003
2. Virtual sensors for wind turbines with machine learning‐based time series models N. Dimitrov & T. Göçmen https://doi.org/10.1002/we.2762
3. Kriging meta-models for damage equivalent load assessment of idling offshore wind turbines F. Schmidt et al. https://doi.org/10.5194/wes-10-3069-2025
4. Fatigue reliability analysis of floating offshore wind turbines under the random environmental conditions based on surrogate model G. Zhao et al. https://doi.org/10.1016/j.oceaneng.2024.119686
5. Power Enhancement of a Vertical Axis Wind Turbine Equipped with an Improved Duct M. Ranjbar et al. https://doi.org/10.3390/en14185780
6. Investigation of Sensitivity Analysis of Crucial Dynamic Characteristics of Offshore Wind Turbine Utilizing Polynomial Chaos Expansion F. Han et al. https://doi.org/10.1115/1.4067797
7. Given-data probabilistic fatigue assessment for offshore wind turbines using Bayesian quadrature E. Fekhari et al. https://doi.org/10.1017/dce.2023.27
8. On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model D. Liu et al. https://doi.org/10.1016/j.renene.2024.120238
9. Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities V. Pettas & P. Cheng https://doi.org/10.3390/en17061284
10. Data-driven wind farm flow control and challenges towards field implementation: A review T. Göçmen et al. https://doi.org/10.1016/j.rser.2025.115605
11. Data-driven surrogate model for wind turbine damage equivalent load R. Haghi & C. Crawford https://doi.org/10.5194/wes-9-2039-2024
12. Grand challenges in the design, manufacture, and operation of future wind turbine systems P. Veers et al. https://doi.org/10.5194/wes-8-1071-2023
13. Influence of wind-wave characteristics on floating wind turbine loads: A sensitivity analysis across different floating concepts L. Reddy et al. https://doi.org/10.1088/1742-6596/2767/6/062030
14. Probabilistic surrogate modeling of offshore wind-turbine loads with chained Gaussian processes D. Singh et al. https://doi.org/10.1088/1742-6596/2265/3/032070
15. Wind load assessment in marine and offshore engineering standards H. Kozmar et al. https://doi.org/10.1016/j.oceaneng.2022.110872
16. Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals D. Conti et al. https://doi.org/10.5194/wes-6-841-2021
17. Operational-based annual energy production uncertainty: are its components actually uncorrelated? N. Bodini & M. Optis https://doi.org/10.5194/wes-5-1435-2020
18. Wind field estimation for lidar-assisted control: a comparison of proper orthogonal decomposition and interpolation techniques E. Soto Sagredo et al. https://doi.org/10.5194/wes-11-1705-2026
19. Beyond the First Generation of Wind Modeling for Resource Assessment and Siting: From Meteorology to Uncertainty Quantification M. Kelly https://doi.org/10.3390/en18071589
20. Surrogate models for parameterized representation of wake‐induced loads in wind farms N. Dimitrov https://doi.org/10.1002/we.2362
21. Global trends in the performance of large wind farms based on high-fidelity simulations S. Andersen et al. https://doi.org/10.5194/wes-5-1689-2020
22. Multi-failure reliability evaluation of wind turbine blades under extreme conditions X. Li et al. https://doi.org/10.1016/j.istruc.2026.111836
23. Data-driven probabilistic surrogate model for floating wind turbine lifetime damage equivalent load prediction D. Singh et al. https://doi.org/10.5194/wes-10-2865-2025
24. An efficient approach for time-domain fatigue analysis for semi-submersible hulls of floating wind turbines S. Wang et al. https://doi.org/10.1016/j.marstruc.2025.103955
25. Data-Driven Stochastic Fatigue Load Modeling for Fatigue Failure Simulation of Onshore Wind Turbine Foundations R. Gao et al. https://doi.org/10.1061/AJRUA6.RUENG-1513
26. Bivariate statistics of floating offshore wind turbine dynamic response under operational conditions X. Xu et al. https://doi.org/10.1016/j.oceaneng.2022.111657
27. Research on dynamic response prediction of semi-submersible wind turbine platform in real sea test model based on machine learning H. Jiang et al. https://doi.org/10.1016/j.apor.2023.103808
28. A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. https://doi.org/10.1088/1742-6596/2767/3/032026
29. Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions X. Li & W. Zhang https://doi.org/10.1016/j.renene.2020.06.043
30. Real-Time Hybrid Simulation with Polynomial Chaos NARX Modeling for Seismic Response Evaluation of Structures Subjected to Stochastic Ground Motions X. Gao et al. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003451
31. Reliability‐based layout optimization in offshore wind energy systems C. Clark et al. https://doi.org/10.1002/we.2664
32. Computational modelling of hydrodynamic loads on offshore monopiles in concurrent wave and current conditions A. Hilo et al. https://doi.org/10.1016/j.oceaneng.2026.124204
33. Characterizing atmospheric stability in complex terrain N. Agarwal & J. Lundquist https://doi.org/10.5194/wes-11-883-2026
34. Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression L. Avendaño-Valencia et al. https://doi.org/10.1016/j.renene.2021.02.003
35. FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. https://doi.org/10.5194/wes-7-2181-2022
36. Improving Site-Dependent Wind Turbine Performance Prediction Accuracy Using Machine Learning S. Barber et al. https://doi.org/10.1115/1.4053513
37. Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements C. Hübler & R. Rolfes https://doi.org/10.5194/wes-7-1919-2022
38. Modelling for Digital Twins—Potential Role of Surrogate Models Á. Bárkányi et al. https://doi.org/10.3390/pr9030476
39. Uncertainty propagation and sensitivity analysis of an artificial neural network used as wind turbine load surrogate model L. Schröder et al. https://doi.org/10.1088/1742-6596/1618/4/042040
40. Estimation of probability distribution of long-term fatigue damage on wind turbine tower using residual neural network H. Bai et al. https://doi.org/10.1016/j.ymssp.2023.110101
41. Data-driven time series forecasting of offshore wind turbine loads H. Muhammad Amri et al. https://doi.org/10.1088/1742-6596/2767/5/052060
42. Multi-objective Bayesian optimisation over sparse subspaces for model predictive control of wind farms K. Hoang et al. https://doi.org/10.1016/j.renene.2025.122988
43. Probabilistic fatigue surrogate model of bimodal tension process for a semi-submersible platform Y. Zhao & S. Dong https://doi.org/10.1016/j.oceaneng.2020.108501
44. Surrogate models for the blade element momentum aerodynamic model using non-intrusive polynomial chaos expansions R. Haghi & C. Crawford https://doi.org/10.5194/wes-7-1289-2022
45. Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data C. Mylonas et al. https://doi.org/10.1002/we.2621
46. Wind farm layout optimization in complex terrain considering wind turbine fatigue load constraint W. Liu et al. https://doi.org/10.1063/5.0249777
47. Meta-model-based fatigue analysis of 25 MW offshore jackets: relevance of wind and wave conditions M. Moattari et al. https://doi.org/10.1016/j.oceaneng.2026.124475
48. Modelling of turbine power and local wind conditions in wind farm using an autoencoder neural network S. Dou & N. Dimitrov https://doi.org/10.1088/1742-6596/2265/3/032069
49. Influence of wind turbine models selection on lifetime extension estimations based on a relative assessment approach F. Galletto et al. https://doi.org/10.1088/1742-6596/3224/6/062069
50. Multi-Criteria Optimization of Wind Turbines in an Offshore Wind Farm with Monopile Foundation Considering Structural Integrity and Energy Generation S. Ali et al. https://doi.org/10.3390/jmse12122313
51. Multi-task learning long short-term memory model to emulate wind turbine blade dynamics S. Baisthakur & B. Fitzgerald https://doi.org/10.5194/wes-10-1979-2025
52. Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads A. Robertson et al. https://doi.org/10.5194/wes-4-479-2019
53. Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements D. Conti et al. https://doi.org/10.5194/wes-5-1129-2020
54. Surrogate models for predicting stall-induced vibrations on wind turbine blades C. Santhanam et al. https://doi.org/10.1088/1742-6596/2265/3/032005
55. Equi-Euler GraphNet: An equivariant, temporal-dynamics informed graph neural network for dual force and trajectory prediction in multi-body systems V. Sharma et al. https://doi.org/10.1016/j.ymssp.2025.113533
56. Data-driven models for predicting structural loads in offshore wind turbines using SCADA data I. Tsaklis et al. https://doi.org/10.1088/1742-6596/3224/6/062042
57. Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling S. Guth et al. https://doi.org/10.1002/we.2880
58. Recursive Bayesian estimation of wind load on a monopile-supported offshore wind turbine using output-only measurements A. Mehrjoo et al. https://doi.org/10.1016/j.ymssp.2024.112183
59. Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. https://doi.org/10.1088/1742-6596/2265/3/032110
60. Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P. Hulsman et al. https://doi.org/10.5194/wes-5-309-2020
61. Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks D. Singh et al. https://doi.org/10.5194/wes-9-1885-2024
62. Physics-Informed Neural Network surrogate model for bypassing Blade Element Momentum theory in wind turbine aerodynamic load estimation S. Baisthakur & B. Fitzgerald https://doi.org/10.1016/j.renene.2024.120122
63. Evaluating the role of the subgrid-scale eddy viscosity coefficient in a multiscale simulation with the WRF model S. Hamzeloo et al. https://doi.org/10.1088/1742-6596/3224/2/022012
64. Variable importance analysis of wind turbine extreme responses with Shapley value explanation X. Zhang & N. Dimitrov https://doi.org/10.1016/j.renene.2024.121049
65. Towards a load surrogate model for low-frequency fatigue cycles in wind turbines A. Shah et al. https://doi.org/10.1088/1742-6596/3224/6/062064
66. Surrogate model for fast simulation of turbine loads in wind farms E. Bossanyi https://doi.org/10.1088/1742-6596/2265/4/042038
67. A study of Stall-Induced Vibrations using Surrogate-Based Optimization C. Santhanam et al. https://doi.org/10.1016/j.renene.2023.05.054
68. From shear to veer: theory, statistics, and practical application M. Kelly & M. van der Laan https://doi.org/10.5194/wes-8-975-2023
69. Østerild: A natural laboratory for atmospheric turbulence A. Peña https://doi.org/10.1063/1.5121486
70. Digital shadows with optimized bias correction for accurate wind turbine fatigue load estimation H. Hoghooghi & C. Bottasso https://doi.org/10.1088/1742-6596/3224/6/062003
71. Incorporation of floater rotation and displacement in a static wind farm simulator R. Riva et al. https://doi.org/10.1088/1742-6596/2767/6/062019
72. Wind turbine load estimation using machine learning and transfer learning G. Xu et al. https://doi.org/10.1088/1742-6596/2265/3/032108
73. Statistical impact of wind-speed ramp events on turbines, via observations and coupled fluid-dynamic and aeroelastic simulations M. Kelly et al. https://doi.org/10.5194/wes-6-1227-2021
74. From wind conditions to operational strategy: optimal planning of wind turbine damage progression over its lifetime N. Requate et al. https://doi.org/10.5194/wes-8-1727-2023
75. Flow acceleration statistics: a new paradigm for wind-driven loads, towards probabilistic turbine design M. Kelly https://doi.org/10.5194/wes-10-535-2025
76. Comparing lidar-derived and conventional wind profile extrapolations for fatigue load calculations on large wind turbines F. Bornemann et al. https://doi.org/10.1088/1742-6596/3224/4/042003
77. Hybrid surrogate input load estimation model in offshore wind turbines using transfer learning and multitask learning A. Mehrjoo et al. https://doi.org/10.1016/j.renene.2025.123011
78. The surrogate model for short-term extreme response prediction based on ANN and Kriging algorithm G. Zhao et al. https://doi.org/10.1016/j.apor.2024.104196
79. Turbulence-aware inflow feature engineering for physics-guided surrogate modeling of wind turbine dynamics Z. Liu et al. https://doi.org/10.1016/j.renene.2026.125840
80. Atmospheric condition identification in multivariate data through a metric for total variation N. Hamilton https://doi.org/10.5194/amt-13-1019-2020
81. From SCADA to lifetime assessment and performance optimization: how to use models and machine learning to extract useful insights from limited data N. Dimitrov & A. Natarajan https://doi.org/10.1088/1742-6596/1222/1/012032
82. Retrospective de‐trending of wind site turbulence using machine learning F. Tough & E. Hart https://doi.org/10.1002/we.2720
83. Enhanced shear and veer in the Taiwan Strait during typhoon passage S. Müller et al. https://doi.org/10.1088/1742-6596/2767/9/092030
84. Efficient layout optimization of offshore wind farm based on load surrogate model and genetic algorithm X. Zhang et al. https://doi.org/10.1016/j.energy.2024.133106
85. Further development of offshore floating solar and its design requirements A. Emami & M. Karimirad https://doi.org/10.1016/j.marstruc.2024.103730
86. Probabilistic vulnerability assessment of floating offshore wind turbines under typhoon hazards considering long-term corrosion–fatigue degradation Y. Yuan et al. https://doi.org/10.1016/j.engstruct.2026.122577
87. An open-access database for meta-models for fatigue calculations of reference wind turbines F. Schmidt et al. https://doi.org/10.1088/1742-6596/3224/7/072003
88. Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations E. Branlard et al. https://doi.org/10.5194/wes-5-1155-2020
89. Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data R. Haghi et al. https://doi.org/10.3390/en17020346
90. Design-friendly wind farm control setpoint estimation via layout-agnostic graph neural networks D. Dirik et al. https://doi.org/10.1088/1742-6596/3224/3/032112
91. Adaptive ensemble of surrogates for efficient fatigue reliability analysis of offshore wind turbines J. Wang et al. https://doi.org/10.1016/j.renene.2025.124553
92. Analysis of the influence of climate change on the fatigue lifetime of offshore wind turbines using imprecise probabilities C. Hübler & R. Rolfes https://doi.org/10.1002/we.2572
93. An advanced fatigue load model and comparison of newly developed algorithms for wind farm layout optimization Y. Jiang et al. https://doi.org/10.1016/j.renene.2025.125046
94. Reaction loads analysis of floating offshore wind turbines: Methods and applications in the modal-based modeling framework C. Høeg & Z. Zhang https://doi.org/10.1016/j.oceaneng.2022.112952
95. Efficient fatigue damage estimation of offshore wind turbine foundation under wind-wave actions T. Li et al. https://doi.org/10.1016/j.jcsr.2024.108903
96. A wind turbine digital shadow for complex inflow conditions H. Hoghooghi & C. Bottasso https://doi.org/10.5194/wes-11-373-2026
97. A surrogate model approach for associating wind farm load variations with turbine failures L. Schröder et al. https://doi.org/10.5194/wes-5-1007-2020
98. A multi-fidelity model intercomparison for wake steering of a large turbine in a conventionally neutral atmospheric boundary layer J. Steiner et al. https://doi.org/10.5194/wes-11-1679-2026
99. Surrogate model uncertainty in wind turbine reliability assessment R. Slot et al. https://doi.org/10.1016/j.renene.2019.11.101
100. On the extension of streamwise turbulence intensity profile beyond the atmospheric surface layer under neutral to unstable stratifications M. Mataji https://doi.org/10.1016/j.jweia.2022.105100
101. Estimation of blade loads from nacelle acceleration to detect stall-induced vibrations in standstill wind turbines C. Santhanam et al. https://doi.org/10.1016/j.weer.2025.100014
102. Optimal design of experiments for computing the fatigue life of an offshore wind turbine based on stepwise uncertainty reduction A. Cousin et al. https://doi.org/10.1016/j.strusafe.2024.102483
103. Surrogate Models for Wind Turbine Load Estimation – Validation with Field Data S. Kottakat et al. https://doi.org/10.1088/1742-6596/3224/6/062057
104. FastLE: a new load extrapolation method for site-specific wind turbines using the load distribution meta-model P. Zhang et al. https://doi.org/10.5194/wes-10-2577-2025
105. Wind farm layout optimization with load constraints using surrogate modelling R. Riva et al. https://doi.org/10.1088/1742-6596/1618/4/042035
106. Data‐driven modeling for fatigue loads of large‐scale wind turbines under active power regulation J. Yang et al. https://doi.org/10.1002/we.2589
107. Efficient reliability analysis for offshore wind turbines: Leveraging SVM and augmented oversampling technique X. Zhang & A. Noshadravan https://doi.org/10.1016/j.strusafe.2025.102597
108. A review of aeroelastic instabilities and resonance effects in wind turbine blade dynamics M. Saram & J. Yang https://doi.org/10.1177/0309524X251405726
109. A digital twin solution for floating offshore wind turbines validated using a full-scale prototype E. Branlard et al. https://doi.org/10.5194/wes-9-1-2024
110. Efficient Loads Surrogates for Waked Turbines in an Array K. Shaler et al. https://doi.org/10.1088/1742-6596/2265/3/032095
111. Sensitivity of fatigue reliability in wind turbines: effects of design turbulence and the Wöhler exponent S. Mozafari et al. https://doi.org/10.5194/wes-9-799-2024
112. Polymorphic uncertainty in met-ocean conditions and the influence on fatigue loads C. Hübler et al. https://doi.org/10.1088/1742-6596/1669/1/012005
113. A Machine Learning Method for Modeling Wind Farm Fatigue Load Y. Miao et al. https://doi.org/10.3390/app12157392
114. Predicting Wind Turbine Blade Tip Deformation With Long Short‐Term Memory (LSTM) Models S. Baisthakur & B. Fitzgerald https://doi.org/10.1002/we.70027
115. A control-oriented load surrogate model based on sector-averaged inflow quantities: capturing damage for unwaked, waked, wake-steering and curtailed wind turbines A. Guilloré et al. https://doi.org/10.1088/1742-6596/2767/3/032019
116. System-level design studies for large rotors D. Zalkind et al. https://doi.org/10.5194/wes-4-595-2019