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Energies 2013, 6(11), 5807-5825; doi:10.3390/en6115807

Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting

Computational Instrumentation and Industrial Electronics Group-Andalusian Plan of Research, Development and Innovation-Information and Communication Technologies-168, Algeciras, Cádiz E-11202, Spain
Department of Automatic Engineering, Electronics, Architecture and Computer Networks, University of Cádiz, Avda. Ramón Puyol, S/N, Algeciras, Cádiz E-11202, Spain
Computer Architecture, Electronics and Electronic Technology Department, University of Córdoba, Campus de Rabanales, Leonardo da Vinci Building, Córdoba E-14071, Spain
Author to whom correspondence should be addressed.
Received: 5 September 2013 / Revised: 22 October 2013 / Accepted: 28 October 2013 / Published: 7 November 2013
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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. View Full-Text
Keywords: wind speed prediction; time series forecasting; artificial neural network; on-site measurement; exogenous information wind speed prediction; time series forecasting; artificial neural network; on-site measurement; exogenous information

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Palomares-Salas, J.C.; Agüera-Pérez, A.; Rosa, J.J.G.; Sierra-Fernández, J.M.; Moreno-Muñoz, A. Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting. Energies 2013, 6, 5807-5825.

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