AI and IoT-Based Energy Optimization System for Intelligent Industrial Process Control and Automation

Abstract

Industrial plants are among the largest energy consumers, yet most still rely on fixed set-points and manual supervision that cannot adapt to changing load, equipment condition, or production demand. This paper presents an AI and IoT-based energy optimization system for intelligent industrial process control. The system uses a five-layer closed-loop architecture sensing, communication, data processing, AI, and control in which industrial IoT devices Stream Energy and process data to an edge cloud platform. Machine learning, deep learning, and reinforcement learning models then predict consumption, detect abnormal equipment behavior, and recommend or apply optimized control actions, each of which passes an independent safety validation before it reaches the equipment. Five prediction models linear regression, decision tree, random forest, ANN, and LSTM were compared; the LSTM achieved the highest accuracy 96% and the lowest prediction error 4%. Over a six-month evaluation, the integrated system consumed 253,480 kWh against 328,000 kWh under conventional control, a saving of 74,520 kWh 22.7%. Overall energy efficiency rose from 68% to 89%, and real time monitoring capability from 55% to 96%. Step by step calculations are provided for monthly and cumulative savings, and clearly labelled illustrative projections give an annual saving of about 149,000 kWh and, at an assumed tariff of USD 0.10 per kWh, about USD 14,900 per year. Throughout, reported results are separated from derived, projected, and assumed quantities. The paper also discusses data-quality, security, cost, legacy-integration, scalability, and model-reliability barriers and outlines future work on digital twins, edge AI, reinforcement learning, renewable energy integration, and multi-site validation.