Abstract
Major dependency on fossil energy resources and emission of greenhouse gases are common problems that have a very harmful impact on human communities. Thus, the use of renewable energy resources, such as wind power, has become a strong alternative to solve this problem. Nevertheless, because of the intermittence and unpredictability of the wind energy, an accurate wind speed forecasting is a very challenging research subject. This paper addresses a short-term wind speed forecasting based on Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The forecasting performances of the model were conducted using the same dataset under different evaluation metrics in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) performance evaluation metrics. The obtained results denote that the used model achieves excellent forecasting accuracy.
Highlights
Renewable energies (RE) provide sustainable solutions, to the energy challenges of the 21st century, climate change, air pollution, depletion of resources, and fast demographic evolution
The proposed method performance is analyzed based on statistical error metrics: The mean square error (MSE), the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) which are defined as follows:
Following the interpretation for the typical MAPE value which was explained in [15], the error measures confirm the satisfactory performance of Seasonal Autoregressive Integrated Moving Average (SARIMA) models, MAPE values are between 11% and 20% means that the model offers good accuracy forecast
Summary
Renewable energies (RE) provide sustainable solutions, to the energy challenges of the 21st century, climate change, air pollution, depletion of resources, and fast demographic evolution. The meteorological data used in this study are collected on the site "Abdelkhalak Torres" in Koudia Al Baida Tetouen located at (Latitude: 35° 45' 35.1, Longitude: -5° 41' 19.9’’) This wind farm, ordered by the national office of electricity and drinking water (ONEE) in 2000, has a capacity of 50.4 MW. The wind speed data used in the present study covers three different months in 2018, March, October and July which correspond respectively to the windiest, moderately windy and less windy months. These data are recorded daily at 10-minute intervals at a height of 100 m [8].The average wind speed in this site is about 10.3 m/s. We observe that there is a predominance of wind in the East-SouthEast (ESE) direction
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