How good is good enough? Re-evaluating the bar for energy disaggregation

TL;DR

This paper assesses the minimum accuracy needed in energy disaggregation to effectively infer household characteristics, demonstrating that even basic disaggregation methods significantly improve household inference tasks.

Contribution

It introduces novel unsupervised energy disaggregation techniques that effectively predict household occupancy and static properties, challenging the perceived necessity for highly accurate disaggregation.

Findings

Disaggregation improves occupancy estimation by up to 30%.

Disaggregation enhances static household property estimation by up to 10%.

Even rudimentary disaggregation techniques are sufficient for meaningful household inference.

Abstract

Since the early 1980s, the research community has developed ever more sophisticated algorithms for the problem of energy disaggregation, but despite decades of research, there is still a dearth of applications with demonstrated value. In this work, we explore a question that is highly pertinent to this research community: how good does energy disaggregation need to be in order to infer characteristics of a household? We present novel techniques that use unsupervised energy disaggregation to predict both household occupancy and static properties of the household such as size of the home and number of occupants. Results show that basic disaggregation approaches performs up to 30% better at occupancy estimation than using aggregate power data alone, and are up to 10% better at estimating static household characteristics. These results show that even rudimentary energy disaggregation techniques are sufficient for improved inference of household characteristics. To conclude, we re-evaluate the bar set by the community for energy disaggregation accuracy and try to answer the question "how good is good enough?"