Meta model approach for real time and short-term forecasting of wind turbine power generation with interactive 24 h dashboard

Abstract This study evaluates the performance of various machine learning algorithms (Linear Regression, SVR, AdaBoost, XGBoost, Gradient Boosting, Decision Tree, Random Forest, Extra Trees, CatBoost) for predicting wind power generation. We investigate their strengths and weaknesses through extensive experimentation using data from Kaggle and ENTSO-E. To enhance accuracy, we employ a meta-model approach and incorporate data cleaning techniques. We integrate statistical methods, artificial neural networks, and deep learning for improved short-term forecasting. A key outcome is the development of a real-time GUI dashboard that utilizes the OpenWeather API to fetch wind data and display predictions. This user-friendly interface features visualizations, alerts, and real-time data updates. Our results demonstrate that the selected meta-model significantly surpasses traditional methods, achieving superior metrics like R-squared and RMSE. This research showcases the potential of hyperparameter-tuned machine learning for precise wind power prediction, contributing to increased renewable energy utilization and reduced greenhouse gas emissions.