67 citations as recorded by crossref.

1. Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study E. Gonzalez et al. 10.1016/j.renene.2018.07.068
2. Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast S. Tai et al. 10.5194/wes-8-433-2023
3. Wind turbine power curve modeling using radial basis function neural networks and tabu search D. Karamichailidou et al. 10.1016/j.renene.2020.10.020
4. On the measurement of stability parameter over complex mountainous terrain E. Cantero et al. 10.5194/wes-7-221-2022
5. Turbulence characteristics of a thermally stratified wind turbine array boundary layer via proper orthogonal decomposition N. Ali et al. 10.1017/jfm.2017.492
6. Capturing Day-to-day Diurnal Variations in Stability in the Convective Atmospheric Boundary Layer Using Large Eddy Simulation J. Nielson & K. Bhaganagar 10.2174/1874282301812010107
7. Using wind speed from a blade-mounted flow sensor for power and load assessment on modern wind turbines M. Pedersen et al. 10.5194/wes-2-547-2017
8. Sensitivity study of a wind farm maintenance decision - A performance and revenue analysis J. Tautz-Weinert et al. 10.1016/j.renene.2018.07.110
9. Turbulence modeling to aid tidal energy resource characterization in the Western Passage, Maine, USA M. Deb et al. 10.1016/j.renene.2023.04.100
10. Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing R. Worsnop et al. 10.5194/wes-3-371-2018
11. Wind turbine wake influence on the mixing of relative humidity quantified through wind tunnel experiments M. Obligado et al. 10.1063/5.0039090
12. Free-flow wind speed from a blade-mounted flow sensor M. Pedersen et al. 10.5194/wes-3-121-2018
13. Characteristics of Wind Environment in Dongbok･Bukchon Wind Farm on Jeju H. Jeong et al. 10.7849/ksnre.2022.2027
14. Review of atmospheric stability estimations for wind power applications C. Pérez Albornoz et al. 10.1016/j.rser.2022.112505
15. Wind power variation by wind veer characteristics with two wind farms U. Tumenbayar & K. Ko 10.1038/s41598-023-37957-6
16. Modeling of the atmospheric boundary layer under stability stratification for wind turbine wake production M. Ichenial et al. 10.1177/0309524X19880929
17. Decision model for cross-border electricity trade considering renewable energy sources M. Eghlimi et al. 10.1016/j.egyr.2022.08.220
18. The effect of wind direction shear on turbine performance in a wind farm in central Iowa M. Sanchez Gomez & J. Lundquist 10.5194/wes-5-125-2020
19. A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset S. Kartal et al. 10.5194/wes-8-1533-2023
20. Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production S. Mata et al. 10.1002/we.2917
21. Recent advances in data-driven prediction for wind power Y. Liu et al. 10.3389/fenrg.2023.1204343
22. A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management L. Zhao et al. 10.1007/s11356-022-19902-8
23. Analysis of Vertical Wind Shear Effects on Offshore Wind Energy Prediction Accuracy Applying Rotor Equivalent Wind Speed and the Relationship with Atmospheric Stability G. Ryu et al. 10.3390/app12146949
24. Investigation of wind veer characteristics on complex terrain using ground-based lidar U. Tumenbayar & K. Ko 10.14710/ijred.2023.56352
25. Influence of atmospheric stability on wind farm performance in complex terrain W. Radünz et al. 10.1016/j.apenergy.2020.116149
26. Improvement of wind power prediction from meteorological characterization with machine learning models C. Sasser et al. 10.1016/j.renene.2021.10.034
27. Understanding the Characteristics of Vertical Structures for Wind Speed Observations via Wind-LIDAR on Jeju Island D. Yi et al. 10.3390/atmos14081260
28. Benchmarks for Model Validation based on LiDAR Wake Measurements P. Doubrawa et al. 10.1088/1742-6596/1256/1/012024
29. Influence of turbulence intensity on wind turbine power curves L. Bardal & L. Sætran 10.1016/j.egypro.2017.10.384
30. Comparing the impact of uncertainties on technical and meteorological parameters in wind power time series modelling in the European Union F. Monforti & I. Gonzalez-Aparicio 10.1016/j.apenergy.2017.08.217
31. Influence of Atmospheric Stability on Wind Turbine Energy Production: A Case Study of the Coastal Region of Yucatan C. Pérez et al. 10.3390/en16104134
32. Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction J. Nielson et al. 10.1016/j.energy.2019.116273
33. Wind farm site selection considering turbulence intensity M. Asadi & K. Pourhossein 10.1016/j.energy.2021.121480
34. Extracting Hidden Features of an Atmospheric Turbulent Flow Through Implementing Discrete Wavelet Analysis on Sodar’s Signal M. Saberivahidaval et al. 10.1007/s13538-021-01039-7
35. Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data S. El‐Asha et al. 10.1002/we.2123
36. Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations G. Valerio Iungo et al. 10.1088/1742-6596/1037/7/072023
37. The Detection of Wind‐Turbine Noise in Seismic Records O. Marcillo & J. Carmichael 10.1785/0220170271
38. Can synoptic drivers explain rotor‐area wind conditions and predict energy output? J. Coburn & K. Klink 10.1002/we.2807
39. Extracting Atmospheric Stability Information from Dual-Doppler Radar Scans in the AWAKEN Campaign J. Nadolsky et al. 10.1088/1742-6596/2767/4/042012
40. Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis D. Astolfi et al. 10.3390/en14040915
41. Contribution of meteorological factors based on explainable artificial intelligence in predicting wind farm power production using machine learning algorithms D. Kim & B. Kim 10.1063/5.0127519
42. Statistical characterization of marine wind structure over wind-turbine-relevant heights in tropical cyclones J. He et al. 10.1016/j.jweia.2024.105874
43. Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements V. Kumer et al. 10.3390/rs9030242
44. Fault-tolerant sensorless control of wind turbines achieving efficiency maximization in the presence of electrical faults M. Corradini et al. 10.1016/j.jfranklin.2018.01.003
45. On the Wind Energy Resource above High-Rise Buildings G. Vita et al. 10.3390/en13143641
46. Atmospheric turbulence affects wind turbine nacelle transfer functions C. St. Martin et al. 10.5194/wes-2-295-2017
47. Wind farm power curve characterization under different atmospheric stability regimes J. Gonzalez et al. 10.1177/0309524X241254473
48. Assessing the impact of waves and platform dynamics on floating wind-turbine energy production A. Fontanella et al. 10.5194/wes-9-1393-2024
49. Simulation of Wind Speeds with Spatio-Temporal Correlation M. Cordeiro-Costas et al. 10.3390/app11083355
50. The Effects of Wind Veer During the Morning and Evening Transitions M. Sanchez Gomez & J. Lundquist 10.1088/1742-6596/1452/1/012075
51. Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences T. Malik & C. Bak 10.5194/wes-9-2017-2024
52. More accurate aeroelastic wind-turbine load simulations using detailed inflow information M. Pedersen et al. 10.5194/wes-4-303-2019
53. A workflow for including atmospheric stability effects in wind resource and yield assessment and its evaluation against wind measurements and SCADA M. Diallo et al. 10.1088/1742-6596/2507/1/012018
54. Augmenting insights from wind turbine data through data-driven approaches C. Moss et al. 10.1016/j.apenergy.2024.124116
55. Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines J. Nielson & K. Bhaganagar 10.1177/0309524X18824540
56. Power curve performance of coastal turbines subject to low turbulence intensity offshore winds Y. Sakagami et al. 10.1007/s40430-022-03942-9
57. The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production M. Optis & J. Perr-Sauer 10.1016/j.rser.2019.05.031
58. A novel probabilistic power curve model to predict the power production and its uncertainty for a wind farm over complex terrain G. Qian & T. Ishihara 10.1016/j.energy.2022.125171
59. Pitch angle control of a wind turbine operating above the rated wind speed: A sliding mode control approach L. Colombo et al. 10.1016/j.isatra.2019.07.002
60. Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear D. Kim et al. 10.1016/j.energy.2020.119051
61. Modified Power Curves for Prediction of Power Output of Wind Farms M. Vahidzadeh & C. Markfort 10.3390/en12091805
62. Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm T. Li et al. 10.1016/j.energy.2021.122340
63. Atmospheric Stability Effects on Offshore and Coastal Wind Resource Characteristics in South Korea for Developing Offshore Wind Farms G. Ryu et al. 10.3390/en15041305
64. How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al. 10.5194/wes-5-1169-2020
65. Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation F. Carbajo Fuertes et al. 10.3390/rs10050668
66. Performance Assessment of Dynamic Downscaling of WRF to Simulate Convective Conditions during Sagebrush Phase 1 Tracer Experiments S. Bhimireddy & K. Bhaganagar 10.3390/atmos9120505
67. An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions M. Vahidzadeh & C. Markfort 10.3390/en13040891