PHASED: Phase-Aware Submodularity-Based Energy Disaggregation
This work addresses energy disaggregation for understanding and reducing energy usage, representing a strong specific gain in the domain.
The paper tackles energy disaggregation by proposing PHASED, an optimization method that uses power distribution system structure and submodular functions, resulting in up to 61% accuracy improvement over state-of-the-art models and better predictions for heavy load appliances.
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.