Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach
暂无分享,去创建一个
[1] Jianzhou Wang,et al. Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China , 2019, Journal of Cleaner Production.
[2] Fernando Porté-Agel,et al. Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms , 2011 .
[3] B. Lange,et al. Comparison of Wake Model Simulations with Offshore Wind Turbine Wake Profiles Measured by Sodar , 2006 .
[4] Torben J. Larsen,et al. Wake meandering: a pragmatic approach , 2008 .
[5] Julio Hernández,et al. Survey of modelling methods for wind turbine wakes and wind farms , 1999 .
[6] Fernando Porté-Agel,et al. Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm , 2015 .
[7] Mohammad Yusri Hassan,et al. Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model , 2016 .
[8] Li Guanglei,et al. Security and Stability Analysis of Wind Farms Integration into Distribution Network , 2017 .
[9] Dipankar Deb,et al. Decision and Control in Hybrid Wind Farms , 2020, Studies in Systems, Decision and Control.
[10] Vladimir Terzija,et al. Wake effect in wind farm performance: Steady-state and dynamic behavior , 2012 .
[11] Hamidreza Zareipour,et al. A review and discussion of decomposition-based hybrid models for wind energy forecasting applications , 2019, Applied Energy.
[12] J. Murillo-Escobar,et al. Forecasting concentrations of air pollutants using support vector regression improved with particle swarm optimization: Case study in Aburrá Valley, Colombia , 2019, Urban Climate.
[13] Jie Zhang,et al. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting , 2017 .
[14] Goran Nenadic,et al. Machine learning methods for wind turbine condition monitoring: A review , 2019, Renewable Energy.
[15] E. S. Politis,et al. Modelling and Measuring Flow and Wind Turbine Wakes in Large Wind Farms Offshore , 2009, Renewable Energy.
[16] D. Deb,et al. Wavelet Transform and Variants of SVR with Application in Wind Forecasting , 2018, Advances in Intelligent Systems and Computing.
[17] H. Hangan,et al. Wind Tunnel Investigation of the Near-wake Flow Dynamics of a Horizontal Axis Wind Turbine , 2014 .
[18] Rebecca J. Barthelmie,et al. Analytical modelling of wind speed deficit in large offshore wind farms , 2006 .
[19] C. Lacor,et al. CFD modelling approaches against single wind turbine wake measurements using RANS , 2016 .
[20] Xiaobing Kong,et al. Wind speed prediction using reduced support vector machines with feature selection , 2015, Neurocomputing.
[21] Dipankar Deb,et al. Optimized hybrid wind power generation with forecasting algorithms and battery life considerations , 2017, 2017 IEEE Power and Energy Conference at Illinois (PECI).
[22] F. Porté-Agel,et al. A new analytical model for wind-turbine wakes , 2013 .
[23] A. Foley,et al. Lidar assisted wake redirection in wind farms: A data driven approach , 2020 .
[24] Rosa Espínola,et al. The effect of wind generation and weekday on Spanish electricity spot price forecasting , 2011 .
[25] D. Deb,et al. Modeling of a wind turbine farm in presence of wake interactions , 2011, 2011 International Conference on Energy, Automation and Signal.
[26] Geoffrey E. Hinton,et al. Neighbourhood Components Analysis , 2004, NIPS.
[27] Josep M. Guerrero,et al. Hybrid machine intelligent SVR variants for wind forecasting and ramp events , 2019, Renewable and Sustainable Energy Reviews.
[28] Vlad Muresan,et al. Multi-Criteria Decision Making Approach for Hybrid Operation of Wind Farms , 2019, Symmetry.
[29] Fang Youtong,et al. Analysis of the Jensen's model, the Frandsen's model and their Gaussian variations , 2014, 2014 17th International Conference on Electrical Machines and Systems (ICEMS).
[30] T. Ishihara,et al. A new Gaussian-based analytical wake model for wind turbines considering ambient turbulence intensities and thrust coefficient effects , 2018, Journal of Wind Engineering and Industrial Aerodynamics.
[31] Nilay Shah,et al. Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression , 2019, Renewable and Sustainable Energy Reviews.
[32] Sven F. Crone,et al. Automatic time series analysis for electric load forecasting via support vector regression , 2019, Appl. Soft Comput..
[33] J. F. Ainslie,et al. CALCULATING THE FLOWFIELD IN THE WAKE OF WIND TURBINES , 1988 .
[34] P. Argyle,et al. Modelling turbulence intensity within a large offshore wind farm , 2018, Wind Energy.
[35] Hongxing Yang,et al. Study on an innovative three-dimensional wind turbine wake model , 2018, Applied Energy.
[36] A. S. Dokuz,et al. Wind power forecasting based on daily wind speed data using machine learning algorithms , 2019, Energy Conversion and Management.
[37] F. Porté-Agel,et al. Turbulent flow and scalar transport through and over aligned and staggered wind farms , 2012 .
[38] G.J.W. Van Bussel,et al. Influence of atmospheric stability on wind turbine loads , 2013 .
[39] W. Shen,et al. Development and validation of a new two-dimensional wake model for wind turbine wakes , 2015 .
[40] Paul van der Laan,et al. Wind turbine wake models developed at the Technical University of Denmark: A review , 2016 .
[41] Peng Lu,et al. A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy , 2020 .
[42] N. Jensen. A note on wind generator interaction , 1983 .
[43] A. E. Maguire,et al. Review and evaluation of wake loss models for wind energy applications , 2018, Applied Energy.
[44] Vanita Jain,et al. Sentiment classification of twitter data belonging to renewable energy using machine learning , 2019, Journal of Information and Optimization Sciences.
[45] Lei Wu,et al. Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method , 2016 .
[46] Xiao-jing Wang,et al. Grey Correlation Analysis on the Influential Factors the Hospital Medical Expenditure , 2010, ICICA.
[47] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[48] İnci Okumuş,et al. Current status of wind energy forecasting and a hybrid method for hourly predictions , 2016 .
[49] Zezhou Ye,et al. Hydrogen fuel cell diagnostics using random forest and enhanced feature selection , 2020 .
[50] Shiru Sharma,et al. Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals , 2019, Comput. Biol. Medicine.
[51] Vlad Muresan,et al. Wake Management in Wind Farms: An Adaptive Control Approach , 2019 .
[52] Jordi Cusidó,et al. Feature Selection Algorithms for Wind Turbine Failure Prediction , 2019, Energies.
[53] Sancho Salcedo-Sanz,et al. Feature selection in machine learning prediction systems for renewable energy applications , 2018, Renewable and Sustainable Energy Reviews.
[54] Paras Mandal,et al. A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.
[55] Luis A. Martínez-Tossas,et al. Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments , 2018 .
[56] F. Pierella,et al. Experimental Investigation of Wind Turbine Wakes in the Wind Tunnel , 2013 .