Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting

José Carlos Palomares-Salas, Agustín Agüera-Pérez, Juan José González de la Rosa, José María Sierra-Fernández and Antonio Moreno-Muñoz
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José Carlos Palomares-Salas: Computational Instrumentation and Industrial Electronics Group-Andalusian Plan of Research, Development and Innovation-Information and Communication Technologies-168, Algeciras, Cádiz E-11202, Spain
Agustín Agüera-Pérez: Computational Instrumentation and Industrial Electronics Group-Andalusian Plan of Research, Development and Innovation-Information and Communication Technologies-168, Algeciras, Cádiz E-11202, Spain
Juan José González de la Rosa: Computational Instrumentation and Industrial Electronics Group-Andalusian Plan of Research, Development and Innovation-Information and Communication Technologies-168, Algeciras, Cádiz E-11202, Spain
José María Sierra-Fernández: Computational Instrumentation and Industrial Electronics Group-Andalusian Plan of Research, Development and Innovation-Information and Communication Technologies-168, Algeciras, Cádiz E-11202, Spain
Antonio Moreno-Muñoz: Computational Instrumentation and Industrial Electronics Group-Andalusian Plan of Research, Development and Innovation-Information and Communication Technologies-168, Algeciras, Cádiz E-11202, Spain

Energies, 2013, vol. 6, issue 11, 1-19

Abstract: 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.

Keywords: wind speed prediction; time series forecasting; artificial neural network; on-site measurement; exogenous information (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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