PHASED: Phase-Aware Submodularity-Based Energy Disaggregation

TL;DR

PHASED is a novel energy disaggregation method that uses power system structure and submodular optimization to significantly improve accuracy over existing models.

Contribution

It introduces a phase-aware, submodularity-based optimization approach that leverages power distribution system structure for enhanced energy disaggregation accuracy.

Findings

Improves disaggregation accuracy by up to 61%.

Achieves better predictions for heavy load appliances.

Utilizes a difference of submodular functions for optimization.

Abstract

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.