Human-Centric AIoT and Swarm Intelligence for Dynamic Energy Optimization and Decision-Making in Smart Factories

This paper proposes a unified framework for real-time energy optimization, dynamic pricing response, and collaborative decision-making using AI-driven forecasting, swarm coordination, and human oversight. A synthetic dataset simulating four factories shows strong correlations between AI prediction accuracy, energy cost reduction, swarm agent efficiency, and supervisory interventions.

The results suggest such hybrid systems reduce energy waste, increase resilience, and enable ethically aligned industrial automation.