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9th World Conference on Information Systems and Technologies

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Parametric Study of The Analog Ensembles Algorithm With Clustering Methods For Hindcasting With Multistations

Weather prediction for locations without or scarce meteorological data available can be attempted by taking meteorological datasets from nearby stations. This hindcasting problem can be successfully solved using the Analog Ensemble (AnEn) method. This paper presents a parametric analysis of the AnEn method, and two variations (based on K-means and fuzzy C-means clustering methods), when used to search for analog ensembles in a historical dataset. The study allowed to identify the parameter combinations that yield the best prediction accuracy, improving 13% on the systematic error and 5% on the random error the previous results obtained with the same dataset. In addition, important performance gains were achieved at the computational level.

Leonardo Araújo
Universidade Tecnológica Federal do Paraná
Brazil

Carlos Balsa
Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança (IPB)
Portugal

Carlos Veiga Rodrigues
Vestas Wind Systems A/S - Design Center Porto
Portugal

José Rufino
Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança (IPB)
Portugal

 


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