Unsupervised energy disaggregation via convolutional sparse coding

TL;DR

This paper introduces an unsupervised method for energy disaggregation in households using convolutional sparse coding, enabling activity detection without labeled data, which can support non-intrusive health monitoring.

Contribution

It presents a novel unsupervised approach based on convolutional sparse coding and the iPALM algorithm for energy disaggregation, with convergence guarantees and experimental validation.

Findings

Effective in classifying active/passive power consumption

Comparable or superior to supervised methods on test data

Supports non-intrusive activity and health monitoring

Abstract

In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. This method aims to classify power consumption as active or passive, granting the ability to report on the residents' activity and presence without direct interaction. This lays the foundation for applications like non-intrusive health monitoring of private homes. The proposed method is based on minimizing a suitable energy functional, for which the iPALM (inertial proximal alternating linearized minimization) algorithm is employed, demonstrating that various conditions guaranteeing convergence are satisfied. In order to confirm feasibility of the proposed method, experiments on semi-synthetic test data sets and a comparison to existing, supervised methods are provided.