Forecasting Wind Energy Production Using Machine Learning Techniques

• Published in: E3S web of conferences Vol. 387; p. 1007
• Main Authors: Margarat, G. Simi, Kumar C., Siva, Rajan, Surulivel, B., Raj Mohan
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01-01-2023
• Subjects: Data collection; Energy distribution; Energy management; Forecasting; Learning algorithms; Machine learning; Mathematical models; Optimization; random forest regression; Renewable energy sources; Root-mean-square errors; Support vector machines; support vector regression; wind energy production; Wind power
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202338701007

Wind energy is an essential source of renewable energy that has gained popularity in recent years. Accurately forecasting wind energy production is crucial for efficient energy management and distribution. This paper proposes a machine learning-based approach using Support Vector Regression (SVR) and Random Forest Regression (RFR) to forecast wind energy production. The proposed methodology involves data collection, preprocessing, feature selection, model training, optimization, and evaluation. The performance of the models is assessed using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R-squared) metrics. The results indicate that the proposed SVR-RFR model outperforms individual models, achieving a higher accuracy in forecasting wind energy production.