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A novel inference method for local wind conditions using genetic fuzzy systems

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  • de la Rosa, Juan José González
  • Pérez, Agustín Agüera
  • Palomares Salas, José Carlos
  • Ramiro Leo, José Gabriel
  • Muñoz, Antonio Moreno

Abstract

Local wind climate is usually measured and described as the result of a regional wind climate modulated by local topography effects, roughness and obstacles in the surrounding area. This paper renders a fuzzy-logic-based method designed to generate the local wind conditions originated by these geographic elements. The proposed fuzzy systems are specifically conceived to modify a regional wind frequency rose attending to the terrain slopes in all directions. In order to optimize these fuzzy systems, the genetic algorithm improves an initial population and, eventually, selects the one which produces the best approximation to the real measurements. The described process coveys a method to train fuzzy systems in wind parameters down-scaling. It is clearly visible the improvement of the obtained wind frequency distribution with regard to the regional one. This fact implies that the optimized fuzzy system contains information about how to correct the wind direction over a zone using the terrain slopes. This acquired knowledge is the best statistical solution found through Genetic Fuzzy Learning according to the variables and conditions imposed to solve this particular problem in this location.

Suggested Citation

  • de la Rosa, Juan José González & Pérez, Agustín Agüera & Palomares Salas, José Carlos & Ramiro Leo, José Gabriel & Muñoz, Antonio Moreno, 2011. "A novel inference method for local wind conditions using genetic fuzzy systems," Renewable Energy, Elsevier, vol. 36(6), pages 1747-1753.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:6:p:1747-1753
    DOI: 10.1016/j.renene.2010.12.017
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    1. José Carlos Palomares-Salas & Agustín Agüera-Pérez & Juan José González de la Rosa & José María Sierra-Fernández & Antonio Moreno-Muñoz, 2013. "Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting," Energies, MDPI, vol. 6(11), pages 1-19, November.

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