Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique

Fluctuation of wind speed affects wind energy systems since the potential wind power is proportional the cube of wind speed. Hence precise prediction of wind speed is very important to improve the performances of the systems. Due to unstable behavior of the wind speed above different terrains, in this study fractal characteristics of the wind speed series were analyzed. According to the self-similarity characteristic and the scale invariance, the fractal extrapolate interpolation prediction can be performed by extending the fractal characteristic from internal interval to external interval. Afterward neuro-fuzzy technique was applied to the fractal data because of high nonlinearity of the data. The neuro-fuzzy approach was used to detect the most important variables which affect the wind speed according to the fractal dimensions. The main goal was to investigate the influence of terrain roughness length and different heights of the wind speed on the wind speed prediction.

[1]  F. Basti,et al.  The Fractal Nature Materials Microstructure Influence on Electrochemical Energy Sources , 2015 .

[2]  Alan G. Davenport,et al.  Guidelines for the calculation of wind speed-ups in complex terrain , 1998 .

[3]  R. Calif PDF models and synthetic model for the wind speed fluctuations based on the resolution of Langevin equation , 2012 .

[4]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[5]  Ljubiša Kocić,et al.  Fractals and BaTiO3-Ceramic Microstructure Analysis , 1998 .

[6]  Rudy Calif,et al.  Classification of wind speed distributions using a mixture of Dirichlet distributions , 2011 .

[7]  Aldo Armigliato,et al.  Modern Developments and Applications in Microbeam Analysis , 1998 .

[8]  A. Bielecki,et al.  Wind speed modelling using Weierstrass function fitted by a genetic algorithm , 2012 .

[9]  Patrick Gallinari,et al.  Variable selection with neural networks , 1996, Neurocomputing.

[10]  Kit Yan Chan,et al.  A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach , 2009, Expert Syst. Appl..

[11]  Ying-Pin Chang,et al.  Fractal dimension of wind speed time series , 2012 .

[12]  Pak Wai Chan,et al.  Standardization of raw wind speed data under complex terrain conditions: A data-driven scheme , 2014 .

[13]  Ljubiša Kocić,et al.  Fractal approach to BaTiO3-ceramics micro-impedances , 2015 .

[14]  Michael F. Barnsley,et al.  Fractals everywhere , 1988 .

[15]  J. Buescu,et al.  Explicitly defined fractal interpolation functions with variable parameters , 2015 .

[16]  William David Lubitz,et al.  Wind-tunnel and field investigation of the effect of local wind direction on speed-up over hills , 2007 .

[17]  Ljubiša Kocić,et al.  Dielectric Properties of BaTiO3 Ceramics and Curie‐Weiss and Modified Curie‐Weiss Affected by Fractal Morphology , 2015 .

[18]  John Chick,et al.  Validation of a CFD model of wind turbine wakes with terrain effects , 2013 .

[19]  V. V. Miti,et al.  Fractal Corrections of BaTiO 3-ceramic Sintering Parameters , 2014 .

[20]  Pak Wai Chan,et al.  Wind characteristics over different terrains , 2013 .

[21]  Sven P. Jacobsson,et al.  Algorithmic approaches for studies of variable influence, contribution and selection in neural networks , 2000 .

[22]  Donald Sofge Using Genetic Algorithm Based Variable Selection to Improve Neural Network Models for Real-World Systems , 2002, ICMLA.

[23]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[24]  Dino Zardi,et al.  Sensitivity of Simulated Wind Speed to Spatial Resolution over Complex Terrain , 2014 .

[25]  Vladimir B. Pavlović,et al.  Fractal corrections of BaTiO3-ceramic sintering parameters , 2014 .

[26]  J. J. Sharples,et al.  Wind characteristics over complex terrain with implications for bushfire risk management , 2010, Environ. Model. Softw..

[27]  Peter L. Jackson,et al.  Comparison of wind speeds obtained using numerical weather prediction models and topographic exposure indices for predicting windthrow in mountainous terrain , 2008 .

[28]  C. Sparrow The Fractal Geometry of Nature , 1984 .

[29]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[30]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[31]  Giovanna Castellano,et al.  Variable selection using neural-network models , 2000, Neurocomputing.

[32]  Daniel S. Abdi,et al.  Wind flow simulations on idealized and real complex terrain using various turbulence models , 2014, Advances in Engineering Software.

[33]  Günter Gauglitz,et al.  Growing neural networks for a multivariate calibration and variable selection of time-resolved measurements , 2003 .

[34]  Hung T. Nguyen,et al.  Diagnosis of hypoglycemic episodes using a neural network based rule discovery system , 2011, Expert Syst. Appl..

[35]  A. Robertson,et al.  Directionality, fractals and chaos in wind-shaped forests , 1994 .

[36]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[37]  P. López,et al.  Effect of direction on wind speed estimation in complex terrain using neural networks , 2008 .

[38]  Vojislav V. Mitić,et al.  The Fractal Nature Materials Microstructure Influence on Electrochemical Energy Sources , 2015 .

[39]  Chunbo Xiu,et al.  Short-term prediction method of wind speed series based on fractal interpolation , 2014 .