Exogenous Measurements from Basic Meteorological Stations forWind Speed Forecasting
Autor
Sierra-Fernández, José María
Moreno-Muñoz, A.
Palomares-Salas, José Carlos
González de la Rosa, Juan José
Agüera Pérez, Agustín
Editor
MDPIFecha
2013Materia
Wind speed predictionTime series forecasting
Artificial neural network
On-site measurement
Exogenous information
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This research presents a comparative analysis of wind speed forecasting methods
applied to perform 1 h-ahead forecasting. The main significant development has been
the introduction of low-quality measurements as exogenous information to improve these
predictions. Eight prediction models have been assessed; three of these models [persistence,
autoregressive integrated moving average (ARIMA) and multiple linear regression] are used
as references, and the remaining five, based on neural networks, are evaluated on the basis
of two procedures. Firstly, four quality indices are assessed (the Pearson’s correlation
coefficient, the index of agreement, the mean absolute error and the mean squared error).
Secondly, an analysis of variance test and multiple comparison procedure are conducted.
The findings indicate that a backpropagation network with five neurons in the hidden layer is
the best model obtained with respect to the reference models. The pair of improvements
(mean absolute-mean squared error) obtained are 29.10%–56.54%, 28.15%–53.99% and
4.93%–14.38%, for the persistence, ARIMA and multiple linear regression models,
respectively. The experimental results reported in this paper show that traditional agricultural
measurements enhance the predictions.