Selecting the Best Regression Model for Wind Power Prediction and Management for VPP

Modern power systems increasingly rely on renewable energy, making effective prediction and management strategies essential, particularly for wind power, known for its variability and intermittency. This study delves into the application of machine learning models to predict wind power generation and optimize power management within virtual power plants (VPPs). It emphasizes key processes such as data preprocessing, feature engineering, and the use of advanced algorithms like Lasso Regression, Support Vector Machine (SVM) Regression, Adaptive Boosting (ADA Boost), and Random Forest Regression. The analysis focuses on critical meteorological and operational factors—wind speed, Low Voltage (LV) Active Power, and wind direction—that significantly impact wind energy output. The study addresses common data challenges, including missing values and feature scaling, to enhance model accuracy and reliability. By developing predictive models, the research enables efficient resource allocation, dynamic energy dispatch, and robust management strategies for VPPs. Through machine learning, the study proposes innovative solutions to improve grid stability, enhance renewable energy utilization, and promote sustainable energy systems. These insights pave the way for resilient and efficient integration of wind energy into modern power infrastructures.