American Tower Corporation’s African subsidiary (ATC Africa) and PowerX yesterday announced a strategic partnership that is designed to bring the significant efficiency and environmental benefits of PowerX’s artificial intelligence (AI) solutions to Africa’s telecommunications industry by optimizing energy usage at tower sites. After reporting a successful joint program, ATC Africa employed PowerX’s AI analytics across a select number of sites in Burkina Faso, Kenya, Niger, Nigeria, and Uganda.
PowerX’s AI platform leverages PowerX’s ability to analyze data to optimize ATC’s site performance, ensuring maximum greenhouse gas (GHG) emissions reduction.
ATC Africa said it is committed to providing connectivity while substantially reducing reliance on fossil fuels through the deployment of new sites that adhere to low GHG emissions site specifications, in accordance with its science-based targets (SBTs).
Marek Busfy, SVP and Chief Executive Officer, ATC Africa stated, “To date, ATC Africa has invested over $300 million in energy efficiency improvements, renewable energy deployments and energy storage solutions to reduce the use of fossil fuels at our sites. Our strategic partnership with PowerX will place us at the forefront of the industry by leveraging the innovative power of AI data-led solutions to optimize the use of power and reduce GHG emissions. I am confident our strategic partnership will grow and contribute to our science-based targets to reduce GHG emissions by at least 40% by 2035 against a 2019 baseline and deliver on our low GHG emissions site strategy.”
PowerX’s CEO, Andrew Schafer said, “Our alliance with ATC will set bold new industry standards for tower energy efficiency. AI-driven analytics uniquely lead the way in efficiently managing power and providing accurate and auditable records in environmental sustainability such as how much diesel and GHG emissions have been eliminated from site operations. We apply sophisticated machine learning and pattern recognition tools to existing site data to identify inefficiencies and anomalies previously buried deep in fragmented data sets.”
Reader Interactions