AI-Enabled Predictive Control System for Energy Optimization in Smart Manufacturing Plants

Abstract

The growing demand for sustainable industrial production and energy-efficient manufacturing processes has accelerated the adoption of intelligent automation technologies in smart manufacturing environments. This study presents an AI-enabled predictive control system for energy optimization in smart manufacturing plants to improve operational efficiency, reduce energy consumption, and enhance industrial sustainability. The proposed framework integrates artificial intelligence, Industrial Internet of Things (IIoT) devices, real-time monitoring systems, and predictive analytics to establish an adaptive energy management platform for manufacturing operations. A data-driven methodology was implemented in which production parameters, machine utilization patterns, environmental conditions, and energy consumption data were continuously collected and analyzed using machine learning and predictive control algorithms. The developed system employed predictive modeling techniques to forecast energy demand, optimize equipment scheduling, and dynamically regulate manufacturing processes based on operational requirements. Simulation and analytical results demonstrated substantial reductions in energy consumption, peak load demand, operational costs, and equipment idle time when compared with conventional energy management approaches. The AI-based predictive control mechanism also improved process stability, production efficiency, and resource utilization while supporting real-time decision-making in complex industrial environments. Furthermore, the intelligent optimization framework enabled proactive maintenance planning and minimized energy wastage through adaptive control strategies. The study concludes that AI-enabled predictive control systems provide an effective and scalable solution for energy optimization in smart manufacturing plants by integrating intelligent automation, predictive analytics, and real-time industrial monitoring. The proposed approach contributes to the development of sustainable Industry 4.0 manufacturing systems with enhanced productivity, operational reliability, and environmental performance.