Optimized Energy Efficiency in a Telecommunication Company: Machine Learning Approach

Energy efficiency is the use of technology that requires less energy to perform the same task. It was considered to introduce optimized energy efficiency by using machine learning to reduce power consumption at communication base station (BTS) sites. This process involves reviewing relevant work to identify defects, characterizing and determining the power consumption of the cell site under investigation, developing a SIMULINK model for the cell site under investigation, and identifying the module. It also includes optimizing high power consumption; design a machine learning rule base to monitor the power consumption of the module. Train artificial neural network (ANN) on machine learning rules designed to reduce cell power consumption, thereby improving network performance. The next step is developing an algorithm to implement it, and finally, to design a power consumption model for the network under investigation. The results obtained after a large simulation show that the traditional maximum power consumed at the cell site is 5746 kW, while the power when machine learning is injected into the system is 4733 kW. Integrating machine learning into the system resulted in 4731 kW, an 8.9% performance improvement.