Hybrid CNN–GRU-Based Demand–Supply Forecasting to Enhance Sustainability in Renewable-Integrated Smart Grids

The rapid integration of renewable energy sources in smart grids has introduced significant uncertainty in both power generation and consumption patterns, posing challenges to environmental, economic, and operational sustainability. Accurate short-term forecasting of energy demand and supply is essential for achieving optimal scheduling, grid stability, and resilient operation in renewable-integrated power systems. This study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for intelligent joint demand–supply forecasting in smart grids. The model was developed and implemented in MATLAB using real-world datasets comprising electricity consumption, photovoltaic (PV) generation, temperature, and irradiance variables. Comparative evaluations demonstrate that the hybrid CNN–GRU outperforms single-model approaches, including Long Short-Term Memory (LSTM), GRU, and eXtreme Gradient Boosting (XGBoost), based on Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. On a 14-day test set, the proposed model achieves RMSE values of approximately 34 kW for demand and 28 kW for PV generation, with MAPE of approximately 4% and 6%, respectively. Furthermore, average net-load RMSE is reduced by approximately 15–25% relative to GRU/LSTM baselines, while maintaining controlled errors of approximately 35–40 kW during sharp ≥100 kW/15 min ramp events. By reducing net-load uncertainty and improving forecasting precision, the proposed framework enhances renewable energy utilization, supports more efficient reserve allocation and storage scheduling, and provides a quantitative tool for sustainability-oriented energy management. Consequently, the study contributes to the advancement of sustainable smart grid operation and the broader transition toward low-carbon and resilient energy systems.