AI-Based Renewable Energy Surplus Prediction

Our project, AI-Based Renewable Energy Surplus Prediction & National Load Flexibility Platform, aims to address one of the major challenges in integrating renewable energy into the power grid: the mismatch between renewable energy generation and electricity demand.

Solar and wind power generation changes depending on weather and environmental conditions, while electricity demand also varies throughout the day. Because of this mismatch, there can be periods where renewable energy is available in excess but cannot be fully utilized, as well as periods of low renewable generation that create demand peaks and additional pressure on the grid. This can reduce renewable energy utilization and make grid management more difficult.

Our proposed solution uses AI-based forecasting to predict renewable energy generation and electricity demand using historical, weather, and grid-related data. Based on these predictions, the system identifies upcoming surplus, deficit, and peak-demand periods. It then identifies electricity loads that have some flexibility in when they operate and recommends suitable time windows for shifting them.

The platform can consider flexible loads such as EV charging, agricultural and water pumping, industrial refrigeration, battery charging, and HVAC systems. For example, if the system predicts excess solar generation during the afternoon, it can recommend shifting suitable flexible loads to that period instead of operating them during peak-demand hours.

The main workflow is forecasting → surplus/deficit prediction → flexibility estimation → constraint-aware optimization → explainable recommendations. The system is designed as a decision-support platform rather than directly controlling the power grid. An MVP can demonstrate the concept for one region through an interactive dashboard.

The expected impact is better utilization of renewable energy, reduction of peak demand, improved load balancing, and reduced dependence on conventional power during periods when renewable energy is available. The platform can support utilities, energy planners, industries, agricultural users, and consumers in making better energy-management decisions.

By connecting renewable energy forecasting with flexible demand management, our project aims to make renewable energy integration more practical, efficient, and reliable while supporting a cleaner and more resilient electricity system.