Artificial Intelligence Based Energy Management System for Industry

In industry, energy management is essential for maintaining power supply within demand limits. Nowadays, digital monitoring systems are used to monitor load demand, which are manually operated based on the readings from these systems. In the modern era, Artificial Intelligence (AI) is being utilized to control energy management systems, making them more efficient. This project presents an AI model that uses a machine learning algorithm and an Arduino controller for real-time data collection. The system measures key parameters such as voltage, current, and load state during peak hours. A solar panel provides reliable power for continuous monitoring. Data collected from the controller is stored in the form of a dataset, which is then used by the machine learning algorithm. The algorithm utilizes the dataset to predict various operational strategies, including economic dispatch, load shedding, load shifting, load scheduling, and load prioritization. This system achieves higher performance scores compared to recent solutions. Additionally, sensor errors are significantly lower than those of previous methods, enhancing grid stability and reducing consumption costs. This showcases the system’s potential for efficient energy management.