Adaptive Ensemble Learning for Energy Demand Forecasting in Smart Grids

The proposed research introduces an Adaptive Ensemble Learning framework designed to enhance energy demand forecasting in smart grids, enabling more accurate, reliable, and responsive energy management. The system integrates multiple machine learning models within a dynamic ensemble structure that adaptively adjusts its composition based on real-time performance feedback. By employing a hybridized training approach, the framework captures both short-term variations and long-term consumption trends, effectively addressing the diverse and fluctuating patterns in energy usage. The adaptive mechanism ensures that the most suitable learning models contribute to the final prediction at any given time, improving forecasting precision under varying grid conditions. The proposed system also leverages advanced optimization and decision fusion techniques to strengthen its robustness against data inconsistencies and sudden demand shifts, thereby enhancing its applicability in real-world smart grid operations. This approach not only advances forecasting accuracy but also supports proactive grid management, efficient resource allocation, and sustainable energy utilization.