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Life sciences · Preprint
arXiv · September 3, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unpublished preprint describing a novel unsupervised machine-learning framework (energy-aware MDE) designed to detect energy-inefficient mobile network sites by comparing each site's consumption to structurally similar peers. The method remains unvalidated against ground truth and has not undergone peer review.
Preprint. Mobile network sites with historical energy consumption measurements.. Intervention: Energy-aware Minimum Distortion Embedding (MDE) with energy-based repulsion mechanism for anomaly detection.. Compared with: Conventional anomaly detection baselines (not named)..
Proposed energy-aware Minimum Distortion Embedding (MDE) formulation extends standard MDE with energy-based repulsion mechanism to displace anomalously high-consumption sites from local neighbourhoods Experimental results report that the proposed approach outperforms conventional anomaly detection baselines (specific performance metrics not stated in abstract)
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This is an unpublished methodological study proposing a novel machine-learning approach for anomaly detection in energy data, without clinical or patient outcomes, peer review, or validation against ground truth.
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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 novel energy-aware Minimum Distortion Embedding (MDE) formulation is introduced that extends the standard MDE objective with an energy-based repulsion mechanism. This encourages sites with anomalously high energy consumption relative to comparable peers to become displaced from their local neighbourhoods in the embedding space. The resulting low-dimensional representation simultaneously preserves structural similarity and encodes energy-related deviations, enabling the identification of potentially inefficient sites through peer-relative comparison. The derived anomaly scores provide a practical mechanism for prioritising field investigations, allowing mobile network operators to focus engineering resources on sites most likely to yield energy savings. Experimental results demonstrate that the proposed approach outperforms conventional anomaly detection baselines and provides a robust foundation for large-scale energy-efficiency optimisation in mobile networks.
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