[Submitted on 25 Jun 2026]
Title:The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting
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Abstract:Energy forecasting aims to maximize accuracy to ensure energy efficiency by reducing energy waste, an objective that applies equally to on-device forecasting for mission-critical edge environments, including military systems. However, this paper identifies the Accuracy-Efficiency Paradox: high-precision energy forecasting models can ironically trigger a net energy deficit. This stems from both edge AI's inference energy consumption and battery aging. We propose a Total Cost of Ownership (TCO) framework for energy forecasting, designed to minimize net energy loss. This framework treats not only inference energy consumption but also battery aging as a unified form of energy loss, as degradation represents a physical dissipation of the system's future energy-carrying capacity. We demonstrate that in thermally sensitive edge environments, energy saved by the superior precision of complex architectures is often outweighed by the total energy lost through their high operational intensity.
Comments: The 5th International Conference on Mobile, Military, Maritime IT Convergence (ICMIC 2026)
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.26134 [cs.AI]
(or arXiv:2608.26134v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.26134
arXiv-issued DOI via DataCite
Submission history
From: Jaeik Jeong [view email] [v1] Thu, 25 Jun 2026 01:22:55 UTC (203 KB)
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