Development of a genetic algorithm framework for energy-efficient power management in base transceiver stations
- 1Department of Electrical and Electronics Engineering, University of Ilorin
- 2Department of Electrical and Computer Engineering, JEDS Campus
- 3Department of Electrical Engineering, Kampala International University
Abstract
Rapid technological advancement has increased environmental concerns and intensified the need for sustainable energy practices across the telecommunications sector. Mobile network operators are increasingly challenged by rising energy costs and the growing demand to reduce carbon emissions while maintaining reliable network performance. Consequently, improving energy efficiency in Base Transceiver Stations (BTSs) has become an important strategy for achieving environmentally sustainable mobile communication systems. This study developed a Genetic Algorithm (GA)-based optimization framework to minimize energy consumption in a BTS. The objectives include identifying and collecting power consumption data of BTS components at a selected site, develop an energy consumption model for the equipment, design optimization algorithm for reducing energy usage, and evaluate the effectiveness of the proposed energy-saving approach. Field measurements were conducted using a clamp meter to determine the current consumed by individual BTS equipment. The collected data were modelled and simulated using MATLAB R2020a and analysed on a personal computer. Measurements were performed during three operational periods within a 24-h cycle: off-peak, moderate, and peak periods. Results showed that energy consumption was highest during peak periods and lowest during off-peak periods. The proposed GA-based optimization achieved energy savings of 53.29%, 29.40%, and 39.90% during off-peak, moderate, and peak periods, respectively, demonstrating improved BTS energy efficiency and reduced operational power demand.
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BibTeX
@article{jimoh2026development,
title = {{Development of a genetic algorithm framework for energy-efficient power management in base transceiver stations}},
author = {B. O. Jimoh and O. Ibrahim and Mutiu Shola Bakare and A. A. Isa and Y. S. Hadi and A. K. Yusuf},
journal = {Discover Sustainability},
year = {2026},
month = jul,
doi = {10.1007/s43621-026-04083-y},
url = {https://doi.org/10.1007/s43621-026-04083-y},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development of a genetic algorithm framework for energy-efficient power management in base transceiver stations
AU - B. O. Jimoh
AU - O. Ibrahim
AU - Mutiu Shola Bakare
AU - A. A. Isa
AU - Y. S. Hadi
AU - A. K. Yusuf
PY - 2026
DA - 2026/07/01
JO - Discover Sustainability
DO - 10.1007/s43621-026-04083-y
UR - https://doi.org/10.1007/s43621-026-04083-y
AB - Rapid technological advancement has increased environmental concerns and intensified the need for sustainable energy practices across the telecommunications sector. Mobile network operators are increasingly challenged by rising energy costs and the growing demand to reduce carbon emissions while maintaining reliable network performance. Consequently, improving energy efficiency in Base Transceiver Stations (BTSs) has become an important strategy for achieving environmentally sustainable mobile communication systems. This study developed a Genetic Algorithm (GA)-based optimization framework to minimize energy consumption in a BTS. The objectives include identifying and collecting power consumption data of BTS components at a selected site, develop an energy consumption model for the equipment, design optimization algorithm for reducing energy usage, and evaluate the effectiveness of the proposed energy-saving approach. Field measurements were conducted using a clamp meter to determine the current consumed by individual BTS equipment. The collected data were modelled and simulated using MATLAB R2020a and analysed on a personal computer. Measurements were performed during three operational periods within a 24-h cycle: off-peak, moderate, and peak periods. Results showed that energy consumption was highest during peak periods and lowest during off-peak periods. The proposed GA-based optimization achieved energy savings of 53.29%, 29.40%, and 39.90% during off-peak, moderate, and peak periods, respectively, demonstrating improved BTS energy efficiency and reduced operational power demand.
SN - 2662-9984
ER -