AI 中文总结
针对设备上能量预测的精度-效率悖论，提出总拥有成本（TCO）框架量化并最小化净能量损失，发现复杂模型的精度增益无法抵消其能耗与电池老化带来的损失。
AI 中文摘要
能量预测旨在最大化精度以通过减少能量浪费来确保能量效率，这一目标同样适用于军事系统等关键边缘环境中的设备上预测。但本文提出了精度-效率悖论：高精度能量预测模型反而会引发净能量赤字，这源于边缘AI的推理能耗与电池老化。我们提出了能量预测的总拥有成本（TCO）框架，旨在最小化净能量损失，该框架将推理能耗与电池老化视为统一的能量损失形式，因为退化代表系统未来能量承载能力的物理耗散。我们证明，在热敏感边缘环境中，复杂架构因更高精度节省的能量，往往被其高操作强度导致的总能量损失所抵消。
英文摘要
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.
CommentsThe 5th International Conference on Mobile, Military, Maritime IT Convergence (ICMIC 2026)
