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All Journal International Journal of Renewable Energy Development International Journal of Renewable Energy Development
Khalifa Mansouri
ENSET Mohammedia, EEIS Laboratory, Hassan II University of Casablanca
Chemistry Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Wind Speed Prediction Based on Statistical and Deep Learning Models Ilham Tyass; Tajeddine Khalili; Mohamed Rafik; Bellat Abdelouahed; Abdelhadi Raihani; Khalifa Mansouri
International Journal of Renewable Energy Development Vol 12, No 2 (2023): March 2023
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.48672

Abstract

Wind is a dominant source of renewable energy with a high sustainability potential. However, the intermittence and unstable nature of wind source affect the efficiency and reliability of wind energy conversion systems. The prediction of the available wind potential is also heavily flawed by its unstable nature. Thus, evaluating the wind energy trough wind speed prevision, is crucial for adapting energy production to load shifting and user demand rates. This work aims to forecast the wind speed using the statistical Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Deep Neural Network model of Long Short-Term Memory (LSTM). In order to shed light on these methods, a comparative analysis is conducted to select the most appropriate model for wind speed prediction. The errors metrics, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the effectiveness of each model and are used to select the best prediction model. Overall, the obtained results showed that LSTM model, compared to SARIMA, has shown leading performance with an average of absolute percentage error (MAPE) of 14.05%.
Wind Speed Prediction Based on Statistical and Deep Learning Models Ilham Tyass; Tajeddine Khalili; Mohamed Rafik; Bellat Abdelouahed; Abdelhadi Raihani; Khalifa Mansouri
International Journal of Renewable Energy Development Vol 12, No 2 (2023): March 2023
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.48672

Abstract

Wind is a dominant source of renewable energy with a high sustainability potential. However, the intermittence and unstable nature of wind source affect the efficiency and reliability of wind energy conversion systems. The prediction of the available wind potential is also heavily flawed by its unstable nature. Thus, evaluating the wind energy trough wind speed prevision, is crucial for adapting energy production to load shifting and user demand rates. This work aims to forecast the wind speed using the statistical Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Deep Neural Network model of Long Short-Term Memory (LSTM). In order to shed light on these methods, a comparative analysis is conducted to select the most appropriate model for wind speed prediction. The errors metrics, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the effectiveness of each model and are used to select the best prediction model. Overall, the obtained results showed that LSTM model, compared to SARIMA, has shown leading performance with an average of absolute percentage error (MAPE) of 14.05%.
Co-Authors Abdelhadi Raihani Bellat Abdelouahed Ilham Tyass Mohamed Rafik Tajeddine Khalili