**5. Conclusion**

Sustainable high power consumption at cell sites has reduced the financial status of some of these cell sites. This ugly situation of high cell site power consumption is

### *Optimized Energy Efficiency in a Telecommunication Company: Machine Learning Approach DOI: http://dx.doi.org/10.5772/intechopen.104488*

mitigated by introducing optimized energy efficiency by using machine learning to reduce power consumption at communication base station (BTS) sites. To achieve this enthusiastically, in this process, the related work is checked to find out its shortcomings, and the power consumption of the inspected cell site module is characterized, determined, and inspected. SIMULINK model is developed, specified, and optimized. Machine learning rule base that minimizes the high power consumption of the cell site module monitors the power consumed by the module and minimizes it at high power. Design and train ANNs with designed machine learning rules to reduce power consumption improve its network performance at base stations. Next, we will develop an algorithm that implements it. Finally, based on the results obtained when the algorithm was integrated into the network, we developed a power consumption model for the network under investigation and improved energy efficiency at the cell site with and without machine learning, validated and justified the rate. The results of extensive simulation show that the conventional maximum power consumption of the cell site is 5746 kW, while the maximum machine learning power consumption of the system is 4733 kW. From these results, the improvement rate of power consumption reduction of cell sites by integrating machine learning into the system is 17.9% on the first day, while the maximum power consumption of conventional cell sites is 3 days of machine learning. You can see that it is 5191 kW by eye. Learning is integrated into the system and is 4731 kW. From these results, it can be seen that the improvement rate of the power consumption reduction of the cell site when the machine learning technology is incorporated into the system on the third day is 8.9%, and the conventional maximum power consumed by the cell site is 5417 kW. I understand, on the other hand, when machine learning is integrated into the system, the reduction of cell sites significantly reduces the power consumption to 4448 kW, which is 17.9% of the power consumption, and the conventional maximum power consumption of cell sites is 5708 kW. The learning built into the system is 4687 kW, which is 17.9 superior to the traditional approach in terms of cell site power savings.
