Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, México. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Cadenas, Erasmo; Rivera, Wilfrido; Campos-Amezcua, Rafael; Cadenas, Roberto
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2417
• Page End: 2428
• Publication Date: 2016-11
• Subjects: Wind speed forecasting -- NARX -- Mahalanobis distance -- Granger test; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2012-y
• Languages: English
• ISSNs: 0941-0643

Abstract

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 one-variable, 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.