Unsupervised energy disaggregation via convolutional sparse coding
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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.
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References · 10
- 2021Self-supervised Learning is More Robust to Dataset Imbalancecited by 123
- 2015Neural NILM: Deep Neural Networks Applied to Energy Disaggregationcited by 54
- 2016Sequence-to-point learning with neural networks for nonintrusive load monitoringcited by 48
- 2017Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problemscited by 40
- 2022How Robust is Unsupervised Representation Learning to Distribution Shift?cited by 24
- 2018Open Power System Data - Frictionless data for electricity system modellingcited by 16
- 2019Higher-order total variation approaches and generalisationscited by 14
- 2019Deep Sparse Coding for Non-Intrusive Load Monitoringcited by 4
- 2020Sequence-to-Sequence Load Disaggregation Using Multi-Scale Residual Neural Networkcited by 3
- 2019Structured Dictionary Learning for Energy Disaggregationcited by 2
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