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Work item:
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Y.AIEE-GN
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Subject/title:
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AI-based energy efficiency management for green infrastructure in Developing Countries
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Status:
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Under study
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Approval process:
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AAP
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Type of work item:
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Recommendation
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Version:
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New
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Equivalent number:
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-
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Timing:
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Q4-2026 (Medium priority)
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Liaison:
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ITU-T SG2, SG5, SG11, ITU-D SG2
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Supporting members:
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China Telecom, CICT, China Mobile, China Unicom, Nigerian Communications Commission (Nigeria)
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Summary:
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In July 2024, the Supplement ITU-T Supp-Y.AIEE-GN “AI-based energy efficiency management practice for green infrastructure in Developing Countries”has been approved. After several contributions submitted from the contributors in different Developing Countries, such as China, Iran and Cameroon. This approval builds on substantial progress made through multiple meetings, where use cases, core requirements, and preliminary frameworks of AI-based energy efficiency management have been explored.
Developing Countries are particularly focused on leveraging AI to enhance energy efficiency, as they face unique challenges including inconsistent power grids, high energy costs accounting for 25-33% of telecom operators' total expenses, and rapid digital infrastructure expansion amid limited energy resources. Additionally, Developing Countries often suffer from inadequate technical foundations, low resource utilization rates, insufficient intelligent operation capabilities, and lack of supporting talent mechanisms. Standardized AI-based energy efficiency management provides solutions that can significantly address these challenges by reducing technical barriers, enabling automated decision-making, maximizing cost-effectiveness, and supporting capacity building. Some global solutions show AI can reduce base station energy consumption by over 10% and data center air conditioning energy use by over 15%, demonstrating cost-saving benefits.
Given the proven progress in technical groundwork and the urgent needs of Developing Countries to balance digital growth with energy sustainability, Recommendations on AI-enabled energy efficiency management is indispensable.
Therefore, this draft Recommendation, building on the ongoing Supplement ITU-T Supp-Y.AIEE-GN, aims to provide a unified AI-based energy efficiency management framework for the whole network infrastructure, thereby enhancing energy efficiency in Developing Countries.
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Comment:
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-
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Reference(s):
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Historic references:
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Contact(s):
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First registration in the WP:
2026-03-10 10:02:22
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Last update:
2026-03-10 10:04:14
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