Quantum-Computing-Inspired Optimal Power Allocation Mechanism in Edge Computing Environment

Innovations in Internet of Things (IoT) technology have significantly enhanced the service qualities of power grid organizations by incorporating smart energy distribution techniques. Conspicuously, the current study presents an effective approach for distributing power load in smart homes using IoT-Edge technology by addressing efficient allocation and real-time energy demand. Specifically, the research focuses on evaluating the spatial–temporal efficiency of power grid substations for distributing energy using edge computing. An optimal distribution of power is achieved by estimating the spatial–temporal utilization index (STUI) using a quantum-computing-inspired approach for each smart home based on real-time energy usage. Additionally, an automated quantum-inspired neural network (AQNN) model is developed to predict the spatial–temporal allocation of energy for power grid substations. For validation, a 60-day simulation of four smart homes in a controlled environment is conducted. Comparison with state-of-the-art data assessment methodologies demonstrates the superiority of the proposed technique for Temporal Delay (5.9 ms), Optimization Performance [Precision (95.15%), Sensitivity (89.75%), Coverage (95.55%), and Specificity (92.99%)], Reliability (92.65%), and Stability (70%).