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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1007/bf03305378

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1433-3058

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Wind speed forecasting using the NARX model case

Authors: Erasmo Cadenas Wilfrido Rivera Rafael CamposAmezcua Roberto Cadenas
Publish Date: 2015/08/23
Volume: 27, Issue: 8, Pages: 2417-2428
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Abstract

This study presents the generation of a nonlinear autoregressive exogenous model NARX for wind speed forecasting in a 1 h in advance horizon A sample of meteorological data of hourly measurements taken during a year was used for the generation of the model The variables measured were as follows wind speed wind direction solar radiation pressure and temperature All measurements were taken by the Comision Federal de Electricidad CFE at La Mata in the state of Oaxaca Mexico Using the Mahalanobis distance the sample of data was treated in order to detect deviated values in multivariable samples Later on the statistical Granger test was conducted to establish the entry variables that would be incorporated into the model Since solar radiation was the only one determined as the cause for wind speed it was the variable used in the configuration of the model To compare the NARX model a onevariable nonlinear autoregressive model NAR was also generated Both models the NARX and the NAR were compared against the persistence model by means of applying the statistical error forecast measurements of mean absolute error mean squared error and mean absolute percentage error to the test data The results showed the NARX model as the most precise of the three reflecting the importance of the inclusion of additional meteorological variables in the wind speed forecasting models

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