OPTIMIZED ENERGY EFFICIENCY THROUGH REDUCTION OF POWER CONSUMPTION IN A TELECOMMUNICATION BASE TRANCEIVER STATION (BTS) SITE USING MACHINE LEARNING
Keywords:
Optimized, Energy Efficiency, Reduction of Power Consumption, Telecommunication Base Transceiver Station, Machine LearningAbstract
The high consumption of power by modules of a cell site has paralyzed the activities of such site. This is vehemently surmounted by introducing optimized energy efficiency through reduction of power consumption in a telecommunication base transceiver station (BTS) site using machine learning. To vehemently achieve this, it is done in this process , reviewing the related works to know its short comings, characterizing and determining the power consumption of the modules of the cell site under study, developing a SIMULINK model for the cell site under study, optimizing the established high power consumed by the modules of the cell site to a minimal, designing a machine learning rule base that will monitor the power consumed on the modules and minimize it if high and training ANN in the designed machine learning rules for a reduced power consumption in the cell site thereby enhancing its network performance. Then, developing an algorithm that will implement it. Finally, developing a power consumption model for the network under study based on the result obtained when the algorithm is integrated in it and validating and justifying the percentage improvement of energy efficiency in the cell site with and without the application of machine learning. The results obtained after extensive simulation is the highest conventional power consumed by the cell site is5764KW while that when machine learning is inculcated in the system is4733KW. With these results, it signifies that the percentage improvement in the reduction of power consumed in the cell site when machine learning is incorporated in the system in day one is17.9%, the highest conventional power consumed in the cell site in day 3 is5191KW while that when machine learning is integrated in the system is 4731KW.With these results achieved, it shows that the percentage improvement in power consumption reduction in the cell site when machine learning technique is imbibed in the system in day 3 is 8.9%, the highest conventional power consumed in the cell site is5417KW. On the other hand, when machine learning is integrated in the system, it reduced drastically to4448KW which is 17.9% power consumed by the cell site reduction and the highest conventional power consumed by the cell site is5708KW while that when machine learning is injected in the system is4687KW which is 17.9% better that the conventional approach as regards power consumption reduction in the cell site