Deep Learning-Based Forecasting For Grid-Scale Renewable Energy Integration

Abstract

This paper addresses the pressing challenge of integrating high shares of renewable energy into grid-scale power systems by employing advanced deep learning methods to improve forecast accuracy for wind and solar generation. We propose a hybrid architecture combining convolutional neural networks (CNN) and long short-term memory networks (LSTM), enhanced via attention mechanisms, to capture both spatial and temporal variability in generation patterns. A comprehensive evaluation is conducted on real-world datasets from diverse geographic regions, encompassing meteorological and operational power data. The proposed model demonstrates a statistically significant reduction in forecasting error metrics—mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE)—compared with conventional machine learning baselines and standard statistical models. Our results indicate the model's potential to facilitate improved grid stability, economic dispatch, and integration of renewables at scale. We also discuss computational cost, scalability, and real-time deployment considerations. The findings contribute to bridging the gap between advanced forecasting techniques and operational grid management for systems with high renewable energy penetration.