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Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting

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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
    Department of Automatic Engineering, Electronics, Architecture and Computer Networks, University of Cádiz, Avda. Ramón Puyol, S/N, 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
    Department of Automatic Engineering, Electronics, Architecture and Computer Networks, University of Cádiz, Avda. Ramón Puyol, S/N, 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
    Department of Automatic Engineering, Electronics, Architecture and Computer Networks, University of Cádiz, Avda. Ramón Puyol, S/N, 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
    Department of Automatic Engineering, Electronics, Architecture and Computer Networks, University of Cádiz, Avda. Ramón Puyol, S/N, 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
    Computer Architecture, Electronics and Electronic Technology Department, University of Córdoba, Campus de Rabanales, Leonardo da Vinci Building, Córdoba E-14071, Spain)

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.

Suggested Citation

  • José Carlos Palomares-Salas & Agustín Agüera-Pérez & Juan José González de la Rosa & José María Sierra-Fernández & Antonio Moreno-Muñoz, 2013. "Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting," Energies, MDPI, vol. 6(11), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:11:p:5807-5825:d:30240
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    References listed on IDEAS

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    Cited by:

    1. Wei Sun & Mohan Liu & Yi Liang, 2015. "Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm," Energies, MDPI, vol. 8(7), pages 1-23, June.
    2. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    3. Maria Grazia De Giorgi & Stefano Campilongo & Antonio Ficarella & Paolo Maria Congedo, 2014. "Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)," Energies, MDPI, vol. 7(8), pages 1-22, August.
    4. Liu, Hui & Tian, Hongqi & Liang, Xifeng & Li, Yanfei, 2015. "New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 83(C), pages 1066-1075.

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