A Smart Energy Consumption System Architecture for Sustainable Semiconductor Manufacturing and AI Workload Operations

Energy consumption in the semiconductor industry, particularly from AI chip fabrication and high-performance computing (HPC) workloads, has risen dramatically, driven by exponential growth in AI model training and inference demands. Traditional energy management systems are inadequate to balance cost, sustainability goals, and performance requirements in such high-complexity environments. This paper proposes a novel Smart Energy Consumption System Architecture (SECSA) tailored for semiconductor fabrication plants (fabs), AI chip consumers, and data center operations supporting AI workloads. SECSA integrates real-time energy monitoring, predictive analytics, hybrid control strategies, renewable energy orchestration, and demand response optimization. Through simulation and architectural analysis, we demonstrate how SECSA can reduce energy costs by up to 35%, lower carbon emissions, improve grid reliability participation, and enable energy-aware workload scheduling. We present design principles, modeling frameworks, integration strategies, and evaluation results showing feasibility and advantages over traditional energy systems.