A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/20/6782 |
| _version_ | 1797514697504718848 |
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| author | Meftah Elsaraiti Adel Merabet |
| author_facet | Meftah Elsaraiti Adel Merabet |
| author_sort | Meftah Elsaraiti |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:35:16Z |
| format | Article |
| id | doaj.art-06cc20663afb4d5cb73cbae48cc2ef05 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:35:16Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-06cc20663afb4d5cb73cbae48cc2ef052023-11-22T18:08:42ZengMDPI AGEnergies1996-10732021-10-011420678210.3390/en14206782A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind SpeedMeftah Elsaraiti0Adel Merabet1Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, CanadaDivision of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, CanadaForecasting 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.https://www.mdpi.com/1996-1073/14/20/6782ARIMAforecastingLSTMwind speed |
| spellingShingle | Meftah Elsaraiti Adel Merabet A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed Energies ARIMA forecasting LSTM wind speed |
| title | A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed |
| title_full | A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed |
| title_fullStr | A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed |
| title_full_unstemmed | A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed |
| title_short | A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed |
| title_sort | comparative analysis of the arima and lstm predictive models and their effectiveness for predicting wind speed |
| topic | ARIMA forecasting LSTM wind speed |
| url | https://www.mdpi.com/1996-1073/14/20/6782 |
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