Bayesian infinite mixture models for wind speed distribution estimation

[1]  Han Li,et al.  Determining suitable region wind speed probability distribution using optimal score-radar map , 2019, Energy Conversion and Management.

[2]  J. Sethuraman A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS , 1991 .

[3]  O. A. Jaramillo,et al.  Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case , 2004 .

[4]  Jianzhou Wang,et al.  Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources , 2015 .

[5]  D. Schindler,et al.  Wind speed distribution selection – A review of recent development and progress , 2019, Renewable and Sustainable Energy Reviews.

[6]  Q. Han,et al.  Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China , 2019, Renewable and Sustainable Energy Reviews.

[7]  Qinghua Hu,et al.  Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data , 2019, IEEE Transactions on Sustainable Energy.

[8]  A. Teimourian,et al.  Assessment of wind energy potential in the southeastern province of Iran , 2019, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[9]  D. Schindler,et al.  Global comparison of the goodness-of-fit of wind speed distributions , 2017 .

[10]  Fulei Chu,et al.  Non-parametric models for joint probabilistic distributions of wind speed and direction data , 2018, Renewable Energy.

[11]  Matthew A. Lackner,et al.  Probability distributions for offshore wind speeds , 2009 .

[12]  Nurulkamal Masseran,et al.  Evaluating wind power density models and their statistical properties , 2015 .

[13]  Lei Zhang,et al.  Robust Principal Component Analysis with Complex Noise , 2014, ICML.

[14]  Fateh Chebana,et al.  Review of criteria for the selection of probability distributions for wind speed data and introduction of the moment and L-moment ratio diagram methods, with a case study , 2016 .

[15]  Samuel F. Feng,et al.  Wind speed probability density estimation using root-transformed local linear regression , 2019, Energy Conversion and Management.

[16]  N. Cook The OEN mixture model for the joint distribution of wind speed and direction: A globally applicable model with physical justification , 2019, Energy Conversion and Management.

[17]  John Paisley A Simple Proof of the Stick-Breaking Construction of the Dirichlet Process , 2010 .

[18]  Pasquale De Falco,et al.  Inverse Burr distribution for extreme wind speed prediction: Genesis, identification and estimation , 2016 .

[19]  Wenyuan Li,et al.  Generation system reliability evaluation incorporating correlations of wind speeds with different distributions , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[20]  E. Assareh,et al.  A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods , 2018, Renewable Energy.

[21]  Takvor H. Soukissian,et al.  On the selection of bivariate parametric models for wind data , 2017 .

[22]  Muhammad Ahsan ul Haq,et al.  Marshall–Olkin Power Lomax distribution for modeling of wind speed data , 2020 .

[23]  R. Everson,et al.  Robust Autoregression: Student-t Innovations Using Variational Bayes , 2011, IEEE Transactions on Signal Processing.

[24]  Hien D. Nguyen,et al.  On approximations via convolution-defined mixture models , 2016, Communications in Statistics - Theory and Methods.

[25]  Tao Chen,et al.  A mixture kernel density model for wind speed probability distribution estimation , 2016 .

[26]  Xiaofu Xiong,et al.  Estimating wind speed probability distribution using kernel density method , 2011 .

[27]  J. A. Carta,et al.  Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions , 2007 .

[28]  Adel Belouchrani,et al.  A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria , 2012 .

[29]  E. Erdem,et al.  Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites , 2010 .

[30]  I. Jánosi,et al.  Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe , 2008 .

[31]  O. A. Jaramillo,et al.  Bimodal versus Weibull Wind Speed Distributions: An Analysis of Wind Energy Potential in La Venta, Mexico , 2004 .

[32]  E. Akpinar,et al.  ESTIMATION OF WIND ENERGY POTENTIAL USING FINITE MIXTURE DISTRIBUTION MODELS , 2009 .

[33]  C. O. Okoye,et al.  Assessing the feasibility of wind energy as a power source in Turkmenistan; a major opportunity for Central Asia's energy market , 2019, Energy.

[34]  Mohd Talib Latif,et al.  Fitting a mixture of von Mises distributions in order to model data on wind direction in Peninsular Malaysia , 2013 .

[35]  Hongbin Sun,et al.  A Distributionally Robust Optimization Model for Unit Commitment Based on Kullback–Leibler Divergence , 2018, IEEE Transactions on Power Systems.

[36]  Y. Kantar,et al.  Analysis of wind speed distributions: Wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function , 2008 .

[37]  Valerio Lo Brano,et al.  Quality of wind speed fitting distributions for the urban area of Palermo, Italy , 2011 .

[38]  Önder Güler,et al.  A novel energy pattern factor method for wind speed distribution parameter estimation , 2015 .

[39]  Srinivasa Rao Rayapudi,et al.  Mixture probability distribution functions to model wind speed distributions , 2012 .

[40]  J. A. Carta,et al.  Use of finite mixture distribution models in the analysis of wind energy in the Canarian Archipelago , 2007 .

[41]  Sinan Akpinar,et al.  Wind energy analysis based on maximum entropy principle (MEP)-type distribution function , 2007 .

[42]  Guillermo Valencia Ochoa,et al.  Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia , 2019, Data in brief.

[43]  Jon G. McGowan,et al.  Use of Birnbaum-Saunders distribution for estimating wind speed and wind power probability distributions: A review , 2017 .

[44]  Jun-mei Jia,et al.  A new distribution for modeling the wind speed data in Inner Mongolia of China , 2020 .

[45]  Christopher Jung,et al.  High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series , 2016 .

[46]  S. Deep,et al.  Estimation of the wind energy potential for coastal locations in India using the Weibull model , 2020 .

[47]  Xianguo Li,et al.  MEP-type distribution function: a better alternative to Weibull function for wind speed distributions , 2005 .

[48]  Yun Wang,et al.  Approaches to wind power curve modeling: A review and discussion , 2019 .

[49]  Zheng Yan,et al.  Estimating wind speed probability distribution by diffusion-based kernel density method , 2015 .

[50]  D. Schindler,et al.  Introducing a system of wind speed distributions for modeling properties of wind speed regimes around the world , 2017 .

[51]  Jianzhou Wang,et al.  Wind speed probability distribution estimation and wind energy assessment , 2016 .

[52]  T. Ouarda,et al.  On the mixture of wind speed distribution in a Nordic region , 2018, Energy Conversion and Management.

[53]  C. O. Okoye,et al.  Technical and economic analysis of wind energy potential in Uzbekistan , 2019, Journal of Cleaner Production.

[54]  H. Çelik,et al.  Inverted Kumarswamy distribution for modeling the wind speed data: Lake Van, Turkey , 2021 .

[55]  J. A. Carta,et al.  The use of wind probability distributions derived from the maximum entropy principle in the analysis of wind energy. A case study , 2006 .

[56]  Tian Pau Chang,et al.  Estimation of wind energy potential using different probability density functions , 2011 .

[57]  Y. Kantar,et al.  Wind speed analysis using the Extended Generalized Lindley Distribution , 2018 .

[58]  Manisa Pipattanasomporn,et al.  Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds , 2020, Renewable Energy.

[59]  Xianguo Li,et al.  Investigation of wind characteristics and assessment of wind energy potential for Waterloo region, Canada. , 2005 .

[60]  Jianzhou Wang,et al.  Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models , 2016 .

[61]  Hua Zhang,et al.  Study on the Maximum Entropy Principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea , 2014 .

[62]  Birdal Senoglu,et al.  Generalized Lindley and Power Lindley distributions for modeling the wind speed data , 2017 .

[63]  Qinghua Hu,et al.  On estimating uncertainty of wind energy with mixture of distributions , 2016 .

[64]  T. Ouarda,et al.  Heterogeneous mixture distributions for modeling wind speed, application to the UAE , 2016 .

[65]  S. Campisi-Pinto,et al.  Statistical tests for the distribution of surface wind and current speeds across the globe , 2020 .

[66]  Taha B. M. J. Ouarda,et al.  Probability distributions of wind speed in the UAE , 2015 .