Efficient Power Grid Management Using Quantum Computing and Machine Learning
S. Aslam, G. Tabita, J. S. V. Gopala Krishna, Manesh Ramkrishna Palav · Advances in computational intelligence and robotics book series · 2024
This research work aims to explore a new approach to enhance power grid management efficiency by combining machine learning with quantum computing. This groundbreaking research aims to resolve the many problems associated with power distribution, load balancing, and resilience to fully optimise these areas in modern energy systems. The proposed method makes use of quantum algorithms to accomplish accurate and speedy computations by leveraging the inherent parallelism of quantum computing. for optimizing power grid management tasks such as energy distribution, load balancing, and grid stability. Development of novel quantum-inspired optimization algorithms capable of efficiently solving power grid management tasks, demonstrating improvements in energy efficiency, grid stability, and cost reduction compared to traditional methods. Integration of machine learning models for demand forecasting, anomaly detection, and predictive maintenance, enabling proactive and data-driven decision-making in power grid operations.