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Wind Speed Prediction Based on Statistical and Deep Learning Models

1ENSET Mohammedia, EEIS Laboratory, Hassan II University of Casablanca, Morocco

2LICPM Laboratory, ENSA Béni Mellal, Sultan Moulay Sliman University, Béni Mellal, Morocco

Received: 1 Sep 2022; Revised: 16 Dec 2022; Accepted: 10 Jan 2023; Available online: 23 Jan 2023; Published: 15 Mar 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
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%.
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Keywords: Wind power; Wind speed; Time series; Forecasting models; ARIMA; Deep learning; LSTM

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