论文标题
分阶段：基于阶段的基于次感染的能量分解
PHASED: Phase-Aware Submodularity-Based Energy Disaggregation
论文作者
论文摘要
能源分解是从汇总测量中辨别单个电器能量消耗的任务，这有望理解和减少能源使用情况。在本文中，我们提出了分阶段，这是一种具有两个关键特征的能量分解的优化方法：分阶段（i）利用功率分配系统的结构，以利用现有方法忽略的易于可用的测量结果，（ii）将问题作为差异差的差异提出。我们通过应用大量最小化算法的离散优化变体来迭代地最小化成本函数的全局上限序列以获得高质量的近似解决方案，从而利用了这种形式。分阶段提高了最先进模型的分解准确性高达61％，并可以更好地预测重负荷设备。
Energy disaggregation is the task of discerning the energy consumption of individual appliances from aggregated measurements, which holds promise for understanding and reducing energy usage. In this paper, we propose PHASED, an optimization approach for energy disaggregation that has two key features: PHASED (i) exploits the structure of power distribution systems to make use of readily available measurements that are neglected by existing methods, and (ii) poses the problem as a minimization of a difference of submodular functions. We leverage this form by applying a discrete optimization variant of the majorization-minimization algorithm to iteratively minimize a sequence of global upper bounds of the cost function to obtain high-quality approximate solutions. PHASED improves the disaggregation accuracy of state-of-the-art models by up to 61% and achieves better prediction on heavy load appliances.
