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A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

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  • Meftah Elsaraiti

    (Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, Canada)

  • Adel Merabet

    (Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, Canada)

Abstract

Forecasting wind speed has become one of the most attractive topics to researchers in the field of renewable energy due to its use in generating clean energy, and the capacity for integrating it into the electric grid. There are several methods and models for time series forecasting at the present time. Advancements in deep learning methods characterize the possibility of establishing a more developed multistep prediction model than shallow neural networks (SNNs). However, the accuracy and adequacy of long-term wind speed prediction is not yet well resolved. This study aims to find the most effective predictive model for time series, with less errors and higher accuracy in the predictions, using artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM), which is a special type of RNN model, compared to the common autoregressive integrated moving average (ARIMA). The results are measured by the root mean square error (RMSE) method. The comparison result shows that the LSTM method is more accurate than ARIMA.

Suggested Citation

  • Meftah Elsaraiti & Adel Merabet, 2021. "A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed," Energies, MDPI, vol. 14(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6782-:d:658694
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    References listed on IDEAS

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    Cited by:

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    8. Longnv Huang & Qingyuan Wang & Jiehui Huang & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction," Energies, MDPI, vol. 15(13), pages 1-17, July.
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    Keywords

    ARIMA; forecasting; LSTM; wind speed;
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