AI-Based Energy Forecasting for Smart Grids with Renewable Integration

The growing integration of variable renewable energy sources (VRES), particularly wind and solar, into power systems is essential for advancing global decarbonization and energy sustainability goals. However, their inherent variability and weather dependence introduce significant challenges in maintaining power grid reliability, optimizing operations, and ensuring efficient market participation. Accurate forecasting of renewable generation and energy demand remains a critical problem. Traditional statistical and shallow machine learning approaches often struggle to model the complex spatio-temporal dynamics of VRES, leading to suboptimal performance under non-stationary and high-variability conditions. To address this, we propose a novel deep learning-based energy forecasting framework tailored for smart grids with high renewable penetration. Our solution integrates Long Short-Term Memory (LSTM) networks for capturing nonlinear temporal patterns, Convolutional Neural Networks (CNN) for extracting spatial dependencies, and attention mechanisms to enhance temporal feature prioritization across forecasting horizons. The model is implemented with exogenous inputs including temperature, wind speed, solar irradiance, land use, elevation, and geographic location. A real-time data assimilation layer using Kalman Filtering enables dynamic recalibration, improving model adaptability to changing weather and seasonal trends. Probabilistic forecasting is incorporated using Bayesian LSTM and quantile regression for uncertainty quantification. Evaluation on multiple real-world datasets from the National Renewable Energy Laboratory (NREL) and NOAA reveals that our approach achieves a 28.7% reduction in Mean Absolute Error (MAE) and a 31.4% improvement in Root Mean Square Error (RMSE) compared to traditional statistical models (ARIMA, SARIMA), and a 19.5% improvement over Support Vector Machines and Random Forests. Furthermore, the model shows a 24% enhancement in Continuous Ranked Probability Score (CRPS) for probabilistic accuracy.