Grid Integration of Intermittent Renewables Using AI-Powered Forecasting Models

Abstract: As the global energy transition accelerates, the integration of intermittent renewable energy sources—particularly solar and wind—into power grids presents major challenges due to their variability and unpredictability. This study evaluates the effectiveness of artificial intelligence (AI)-powered forecasting models in enhancing grid reliability and balancing operations. A comparative analysis was conducted using long short-term memory (LSTM) networks, gradient boosting regressors (GBR), and support vector machines (SVM) trained on multi-source meteorological and generation data. Results indicate that AI-based forecasting significantly reduces forecasting error margins, enabling more stable scheduling of grid resources. The proposed approach demonstrates a 27% improvement in short-term prediction accuracy and contributes to enhanced grid flexibility and reduced reserve costs. Keywords: Renewable Energy Integration, Grid Forecasting, Artificial Intelligence, LSTM, Intermittent Energy, Load Balancing, Grid Stability