A new method for identification of fuzzy models based on evolutionary algorithms and its application to the modeling of a wind turbine

2011 ◽  
Author(s):  
Gabriel Moreno ◽  
Doris Saez ◽  
Marcos E. Orchard
Keyword(s):  
Evolutionary Algorithms ◽  
Wind Turbine ◽  
New Method ◽  
Fuzzy Models

A novel fractional discrete nonlinear grey Bernoulli model for forecasting the wind turbine capacity of China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wen-ze Wu ◽  
Wanli Xie ◽  
Chong Liu ◽  
Tao Zhang
Keyword(s):  
Wind Turbine ◽  
Nonlinear Coefficient ◽  
Predictive Performance ◽  
Prediction Performance ◽  
New Method ◽  
Grey Model ◽  
Content Type ◽  
Proposed Model ◽  
Whale Optimization ◽  
Turbine Capacity

PurposeA new method for forecasting wind turbine capacity of China is proposed through grey modelling technique.Design/methodology/approachFirst of all, the concepts of discrete grey model are introduced into the NGBM(1,1) model to reduce the discretization error from the differential equation to its discrete forms. Then incorporating the conformable fractional accumulation into the discrete NGBM(1,1) model is carried out to further improve the predictive performance. Finally, in order to effectively seek the emerging coefficients, namely, fractional order and nonlinear coefficient, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients.FindingsThe empirical results show that the newly proposed model has a better prediction performance compared to benchmark models; the wind turbine capacity from 2019 to 2021 is expected to reach 275954.42 Megawatts in 2021. According to the forecasts, policy suggestions are provided for policy-makers.Originality/valueBy combing the fractional accumulation and the concepts of discrete grey model, a new method to improve the prediction performance of the NGBM(1,1) model is proposed. The newly proposed model is firstly applied to predict wind turbine capacity of China.

scholarly journals Fault Diagnosis of a Wind Turbine Benchmark via Identified Fuzzy Models

2015 ◽  
Vol 62 (6) ◽  
pp. 3775-3782 ◽  
Author(s):  
Silvio Simani ◽  
Saverio Farsoni ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Fuzzy Models

Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms

2014 ◽  
Vol 41 (15) ◽  
pp. 6585-6595 ◽  
Author(s):  
Francisco G. Montoya ◽  
Francisco Manzano-Agugliaro ◽  
Sergio López-Márquez ◽  
Quetzalcoatl Hernández-Escobedo ◽  
Consolación Gil
Keyword(s):  
Evolutionary Algorithms ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farm Layout ◽  
Multi Objective ◽  
Selection For

scholarly journals Improving Interpretability of Fuzzy Models Using Multi-Objective Neuro-Evolutionary Algorithms

10.5772/6124 ◽  
2008 ◽  
Author(s):  
Gracia Snchez ◽  
Jos Francisco Snchez Ruiz ◽  
Jos Manuel Alcaraz Muoz ◽  
Fernando Jimnez
Keyword(s):  
Evolutionary Algorithms ◽  
Multi Objective ◽  
Fuzzy Models

scholarly journals Dynamical Modeling using Evolutionary Algorithms

2004 ◽  
Vol 220 ◽  
pp. 329-330
Author(s):  
Anne Mathieu
Keyword(s):  
Evolutionary Algorithms ◽  
Gravitational Potential ◽  
New Method ◽  
Stellar Systems ◽  
Galactic Disc ◽  
Dynamical Modeling ◽  
Non Parametric

I will present a new method of dynamical modeling of stellar systems based on evolutionary algorithms. the technique will be illustrated with an application to the problem of recovering the gravitational potential of a thin galactic disc from kinematic observables in a non-parametric way.

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