SOURCE-LINKED INTELLIGENCE
A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites
Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-level energy inefficiencies such as faulty cooling controllers, idle radio equipment, and parasitic auxiliary loads often remain undetected because no ground-truth inefficiency labels exist and historical measurements may already contain embedded inefficiencies. This study proposes an unsupervised peer-relative approach based on the premise that sites with similar structural and operational characteristics should exhibit comparable energy consumption. To capture these relationships, a
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T13:14:13.000Z
First collected: 2026-09-21T04:51:57.792Z. This is not the publication date.