Hybrid Optimization Approach for Energy Savings in Cooling Tower using IoT and Gradient Boosting

Improving operational efficiency and lowering environmental impact are made possible by optimizing energy usage in industrial cooling tower systems. This paper presents a new method for optimizing the management of industrial cooling towers, with a particular emphasis on energy savings, by combining Internet of Things (IoT) technologies with Gradient Boosting approaches. IoT sensors monitor the cooling system’s temperature, flow rate, and humidity in real time. This data makes predictive analytics on system performance and energy usage possible by training Gradient Boosting models. It shows that the proposed approach successfully achieves substantial energy savings while keeping cooling tower performance at its optimum via extensive testing and analysis. The findings show that Gradient Boosting and IoT monitoring might help industrial settings with proactive energy management. By providing a workable framework for improving cooling tower operations' energy efficiency of sustainable industrial practices. The results highlight the significance of using data-driven strategies and advanced technology to solve energy problems in manufacturing, which will enhance efficiency and sustainability while reducing costs. Data analysis from the real world, performance measurements, and comparison findings show that the system is more efficient and system performance of assessment.