Wind Speed Prediction Based on Seasonal ARIMA model

• Published in: E3S web of conferences Vol. 336; p. 34
• Main Authors: Tyass, Ilham, Bellat, Abdelouahad, Raihani, Abdelhadi, Mansouri, Khalifa, Khalili, Tajeddine
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2022
• Subjects: Alternative energy sources; Autoregressive moving-average models; Decomposition; Electricity; Emissions; Energy resources; Energy sources; Forecasting; Greenhouse gases; Kalman filters; Mean square errors; Performance evaluation; Renewable energy; Renewable resources; Root-mean-square errors; Seasonal variations; Systems stability; Time series; Wind farms; Wind power; Wind speed; Morocco
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202233600034

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.