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Journal of Intelligent Computing
 

Using Analog Ensembles Algorithms for Multistations
Alexandre Chesneau, Carlos Balsa, Carlos Veiga Rodrigues, Isabel Lopes
Universit–e de Toulouse - Institut National Polytechnique de Toulouse Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Polit–ecnico de Bragan‘ca, Campus de Santa Apol–onia, 5300-253 Bragan‘ca, Portugal Vestas Wind Sy
Abstract: In this work, many analog ensembles algorithms were used with the performance of multiple stations. We have deployed many techniques to analyse and benchmark inorder to change the prediction. This issue consists in leading the weather predictions for a location where no data is available, using meteorological time series from nearby stations. Many models are verified and explored. The preliminary one is described by Monache and co-workers, to methods using cosine similarity, normalization, and K-means clustering. Best results were obtained with the K-means metric, wielding between 3% and 30% of lower quadratic error when compared against the Monache metric. Increasing the predictors to two stations improved the performance of the hindcast, leading up to 16% of lower error, depending on the correlation between the predictor stations.
Keywords: Analog Ensembles, Hindcasting, Time Series, Meteorological Data Using Analog Ensembles Algorithms for Multistations
DOI:https://doi.org/10.6025/jic/2020/11/3/102-113
Full_Text   PDF 779 KB   Download:   171  times
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