An Adaptive Hybrid Approach Leveraging Machine Learning and Optimization Techniques for Enhanced Energy Efficiency in Smart Grids

Abstract

The smart grid represents a significant advancement in energy infrastructure, promising enhanced efficiency, reliability, and sustainability. However, realizing its full potential requires sophisticated control strategies capable of adapting to the dynamic and unpredictable nature of energy demand and supply. This paper presents a novel adaptive hybrid approach that combines machine learning (ML) techniques with optimization algorithms to improve energy efficiency in smart grids. The ML component leverages historical data and real-time sensor information to predict energy demand and renewable energy generation, while the optimization component uses these predictions to optimally allocate resources, schedule energy storage, and manage demand response programs. The proposed approach is evaluated through simulations using realistic smart grid scenarios, demonstrating significant improvements in energy efficiency, reduced peak demand, and enhanced integration of renewable energy sources compared to traditional methods. The results highlight the potential of the hybrid approach to address the challenges of modern energy management and contribute to a more sustainable and resilient energy future.