The Accuracy-Efficiency Paradox: Quantifying Net Energy Loss in on-Device Energy Forecasting

准确性与效率的悖论：量化端侧能源预测中的净能量损失

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. 摘要： 能源预测旨在通过减少能源浪费来最大化准确性，从而确保能源效率。这一目标同样适用于包括军事系统在内的关键任务边缘环境下的端侧预测。

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. 然而，本文指出了“准确性与效率的悖论”：高精度的能源预测模型反而可能导致净能量亏损。这源于边缘人工智能的推理能耗以及电池老化问题。

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. 我们提出了一种用于能源预测的总体拥有成本（TCO）框架，旨在最小化净能量损失。该框架不仅将推理能耗，还将电池老化视为一种统一的能量损失形式，因为电池退化代表了系统未来储能能力的物理损耗。

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. 我们证明，在对温度敏感的边缘环境中，复杂架构因其卓越的精度所节省的能源，往往会被其高运行强度所带来的总能量损失所抵消。