# Structured Dictionary Learning for Energy Disaggregation

## TL;DR

This paper introduces hierarchical, structured dictionary learning methods for energy disaggregation that leverage device operation modes, significantly improving accuracy over baseline techniques in real-world datasets.

## Contribution

It proposes a novel hierarchical approach that models concurrent device operation modes, enhancing disaggregation performance compared to existing methods.

## Key findings

- GDDM achieved up to 23.8% improvement in micro-averaged F score
- GDDM achieved up to 10% improvement in macro-averaged F score
- GDDM achieved up to 59.3% reduction in Normalized Disaggregation Error

## Abstract

The increased awareness regarding the impact of energy consumption on the environment has led to an increased focus on reducing energy consumption. Feedback on the appliance level energy consumption can help in reducing the energy demands of the consumers. Energy disaggregation techniques are used to obtain the appliance level energy consumption from the aggregated energy consumption of a house. These techniques extract the energy consumption of an individual appliance as features and hence face the challenge of distinguishing two similar energy consuming devices. To address this challenge we develop methods that leverage the fact that some devices tend to operate concurrently at specific operation modes. The aggregated energy consumption patterns of a subgroup of devices allow us to identify the concurrent operating modes of devices in the subgroup. Thus, we design hierarchical methods to replace the task of overall energy disaggregation among the devices with a recursive disaggregation task involving device subgroups. Experiments on two real-world datasets show that our methods lead to improved performance as compared to baseline. One of our approaches, Greedy based Device Decomposition Method (GDDM) achieved up to 23.8%, 10% and 59.3% improvement in terms of micro-averaged f score, macro-averaged f score and Normalized Disaggregation Error (NDE), respectively.