# Unsupervised energy disaggregation via convolutional sparse coding

arXiv: https://arxiv.org/abs/2207.09785 · HTML: https://mlsanity.com/paper/2207.09785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Self-supervised Learning is More Robust to Dataset Imbalance](https://mlsanity.com/paper/2110.05025/) (2021)
- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems](https://mlsanity.com/paper/1702.02505/) (2017)
- [How Robust is Unsupervised Representation Learning to Distribution Shift?](https://mlsanity.com/paper/2206.08871/) (2022)
- [Open Power System Data - Frictionless data for electricity system modelling](https://mlsanity.com/paper/1812.10405/) (2018)
- [Higher-order total variation approaches and generalisations](https://mlsanity.com/paper/1912.01587/) (2019)
- [Deep Sparse Coding for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1912.12128/) (2019)
- [Sequence-to-Sequence Load Disaggregation Using Multi-Scale Residual Neural Network](https://mlsanity.com/paper/2009.12355/) (2020)
- [Structured Dictionary Learning for Energy Disaggregation](https://mlsanity.com/paper/1907.06581/) (2019)

## Abstract (arXiv)

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.