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Authors: A. Agüera ; J. J. G. de la Rosa ; J. G. Ramiro and J. C. Palomares

Affiliation: University of Cadiz, Spain

Keyword(s): Wind Climate, Fuzzy Systems, Genetic Algorithm, Topography.

Related Ontology Subjects/Areas/Topics: Advanced Applications of Fuzzy Logic ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Evolutionary Programming ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: The wind climate measured in a point is usually described as the result of a regional wind climate forced by local effects derived from topography, roughness and obstacles in the surrounding area. This paper presents a method that allows to use fuzzy logic to generate the local wind conditions caused by these geographic elements. The fuzzy systems proposed in this work are specifically designed to modify a regional wind frequency rose attending to the terrain slopes in each direction. In order to optimize these fuzzy systems, Genetic Algorithms will act improving an initial population and, eventually, selecting the one which produce the best aproximation to the real measurements.

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Paper citation in several formats:
Agüera, A.; J. G. de la Rosa, J.; G. Ramiro, J. and C. Palomares, J. (2010). TRAINING A FUZZY SYSTEM IN WIND CLIMATOLOGIES DOWNSCALING. In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 5: ICEIS; ISBN 978-989-8425-05-8; ISSN 2184-4992, SciTePress, pages 238-243. DOI: 10.5220/0002899402380243

@conference{iceis10,
author={A. Agüera. and J. {J. G. de la Rosa}. and J. {G. Ramiro}. and J. {C. Palomares}.},
title={TRAINING A FUZZY SYSTEM IN WIND CLIMATOLOGIES DOWNSCALING},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 5: ICEIS},
year={2010},
pages={238-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002899402380243},
isbn={978-989-8425-05-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 5: ICEIS
TI - TRAINING A FUZZY SYSTEM IN WIND CLIMATOLOGIES DOWNSCALING
SN - 978-989-8425-05-8
IS - 2184-4992
AU - Agüera, A.
AU - J. G. de la Rosa, J.
AU - G. Ramiro, J.
AU - C. Palomares, J.
PY - 2010
SP - 238
EP - 243
DO - 10.5220/0002899402380243
PB - SciTePress

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