Wind speed reconstruction from synoptic pressure patterns using an evolutionary algorithm

This paper presents an evolutionary algorithm for wind speed reconstruction from synoptic pressure patterns. The algorithm operates in a search space formed by grids of pressure measures, and must classify the different situations into classes, in such a way that a measure of wind speed in a given point is minimized among patterns assigned to the same class. Then, each class is assigned a mean wind speed and direction, so the wind speed reconstruction is possible for a new grid of synoptic pressures. In this paper we present the problem model and the specific description of the evolutionary algorithm proposed to solve the problem. We also show the good performance of the proposed method in the reconstruction of the average wind speed in six wind towers in Spain. The proposed method is applicable to wind speed reconstruction or reconstruction of wind missing data of wind series, specially when there is no other variable or related measure available.

[1]  Gabriel Garcera,et al.  Comparative study of speed estimators with highly noisy measurement signals for Wind Energy Generation Systems , 2011 .

[2]  Yoonseon Song,et al.  Evolutionary programming using the Levy probability distribution , 1999 .

[3]  K. Vaisakh,et al.  A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis , 2010 .

[4]  R. Trigo,et al.  Understanding Precipitation Changes in Iberia in Early Spring: Weather Typing and Storm-Tracking Approaches , 2006 .

[5]  Heping Liu,et al.  Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed , 2011 .

[6]  Gong Li,et al.  Application of Bayesian model averaging in modeling long-term wind speed distributions , 2010 .

[7]  Ioannis B. Theocharis,et al.  Locally recurrent neural networks for long-term wind speed and power prediction , 2006, Neurocomputing.

[8]  Xin Yao,et al.  A new evolutionary approach to cutting stock problems with and without contiguity , 2002, Comput. Oper. Res..

[9]  Soteris A. Kalogirou,et al.  Optimization of solar systems using artificial neural-networks and genetic algorithms , 2004 .

[10]  Nabil Benoudjit,et al.  Multiple architecture system for wind speed prediction , 2011 .

[11]  D. Fadare.  The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria , 2010 .

[12]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for photovoltaic applications: A review , 2008 .

[13]  Chiung-Chou Liao,et al.  Genetic k-means algorithm based RBF network for photovoltaic MPP prediction , 2010 .

[14]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[15]  Jing Shi,et al.  On comparing three artificial neural networks for wind speed forecasting , 2010 .

[16]  Z. H. Chen,et al.  Relationship between atmospheric pollution processes and synoptic pressure patterns in northern China , 2008 .

[17]  Mehdi Khashei,et al.  Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) , 2009, Neurocomputing.

[18]  K. Vaisakh,et al.  Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis , 2011, Expert Syst. Appl..

[19]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[20]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for sizing photovoltaic systems: A review , 2009 .

[21]  René Jursa,et al.  Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models , 2008 .

[22]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .

[23]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[24]  Andrew Kusiak,et al.  Optimization of wind turbine energy and power factor with an evolutionary computation algorithm , 2010 .

[25]  Mohamed Mohandes,et al.  Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS) , 2011 .

[26]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[27]  R. Trigo,et al.  Circulation weather types and their influence on the precipitation regime in Portugal. , 2000 .

[28]  Antonio J. Conejo,et al.  A methodology to generate statistically dependent wind speed scenarios , 2010 .