Energy Consumption Optimization in Smart Factories Using IoT and Reinforcement Learning

In the era of Industry 4.0, optimizing energy consumption in smart factories was crucial for enhancing operational efficiency and sustainability. This paper explores a novel approach to energy management in smart manufacturing environments by integrating Internet of Things (IoT) devices with Reinforcement Learning (RL) algorithms. The proposed system leverages IoT -enabled sensors to collect real-time data on energy usage patterns, production schedules, and machine performance. This data was then fed into an RL model that continuously learns and adapts to optimize energy consumption by making intelligent, automated decisions. The RL framework employed in this study was designed to balance energy efficiency with production demands, ensuring that the factory operates at peak efficiency without compromising productivity. Through simulation and real-world testing, the system dynamically adjusts machine operation times, schedules preventive maintenance, and optimizes resource allocation, leading to significant reductions in energy waste. The IoT -RL integration enables predictive analytics, allowing for proactive energy management strategies that anticipate and mitigate energy spikes. The results show a substantial reduction in overall energy consumption, coupled with improved production efficiency. This research contributes to the growing body of work on sustainable manufacturing practices and highlights the potential of IoT and machine learning technologies in achieving energy-efficient smart factories. The findings have important implications for the future of industrial automation and energy conservation, making them relevant to both academia and industry.