2.04 – Wind Energy Potential

The current chapter analyses one of the most critical aspects for the success of a wind power investment, that is, obtaining long-term, accurate wind field data in order to estimate the amount of future electricity production from wind turbines and thus the economic viability of the project. But, such large quantities of reliable wind speed measurements are not always available. Therefore, an analysis is also made of a wide variety of statistical tools, like Weibull distribution, topographical models, computational fluid dynamics, and forecasting methods that have been developed for estimating wind characteristics in different conditions of atmospheric stability and on complex terrain sites.

[1]  Jing Shi,et al.  Bayesian adaptive combination of short-term wind speed forecasts from neural network models , 2011 .

[2]  Martin Otto Laver Hansen,et al.  Aerodynamics of Wind Turbines , 2001 .

[3]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[4]  Oluseyi O. Ajayi,et al.  Assessment of wind energy potential of two sites in North-East, Nigeria , 2011 .

[5]  Erich Hau,et al.  Wind Turbines: Fundamentals, Technologies, Application, Economics , 1999 .

[6]  B. Ozerdem,et al.  An investigation of wind characteristics on the campus of Izmir Institute of Technology, Turkey , 2003 .

[7]  Désiré Le Gouriérès.  Wind Power Plants: Theory and Design , 1982 .

[8]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[9]  L. Freris.  Wind energy conversion systems , 1990 .

[10]  David R. Anderson,et al.  Model Selection and Multimodel Inference , 2003 .

[11]  Nicholas Jenkins,et al.  Wind Energy Technology , 1997 .

[12]  R. Gasch,et al.  Introduction to Wind Energy , 2012 .

[13]  A. Sayigh,et al.  Wind characteristics and wind energy potential in Morocco , 1998 .

[14]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .

[15]  S. Mathew.  Wind Energy: Fundamentals, Resource Analysis and Economics , 2006 .

[16]  H. Nfaoui,et al.  Stochastic simulation of hourly average wind speed sequences in Tangiers (Morocco) , 1996 .

[17]  D. Mackay,et al.  Bayesian methods for adaptive models , 1992 .

[18]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[19]  M. Stiebler.  Wind Energy Systems for Electric Power Generation , 2008 .

[20]  Thomas Ackermann,et al.  Wind Power in Power Systems , 2005 .

[21]  P. Dunn.  Renewable energies : sources, conversion, and application , 1986 .

[22]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[23]  G. L. Johnson,et al.  Wind energy systems , 1985 .

[24]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[25]  Muyiwa S. Adaramola,et al.  Analysis of wind speed data and wind energy potential in three selected locations in south-east Nigeria , 2012, International Journal of Energy and Environmental Engineering.

[26]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[27]  J. A. Carta,et al.  A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands , 2009 .

[28]  W. Weibull.  A Statistical Distribution Function of Wide Applicability , 1951 .

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

[30]  H. Nfaoui,et al.  A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco , 2004 .

[31]  Erik Lundtang Petersen,et al.  The European Wind Atlas , 1985 .

[32]  Dean Isaacson,et al.  Markov Chains: Theory and Applications , 1976 .