Forecasting Wind Energy Production Using Machine Learning Techniques

Abstract

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.

Application of Ordinary Least Squares Regression and Neural Networks in Predicting Employee Turnover in the Industry

“…Utilize the fitted model to make predictions of staff turnover on new data. Apply the predictor variables to the model and obtain corresponding predictions (Margarat et al, 2023).…”