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Unsupervised Energy Disaggregation Via Convolutional Sparse Coding
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DOI：10.1109/TCE.2023.3324921.png)
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Abstract
En 中文
In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. The 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 methods are provided.
Keywords:
Hidden Markov models
Power demand
Smart meters
Minimization
Energy consumption
Encoding
Convolution
Energy disaggregation
non-intrusive load monitoring
health monitoring
convolutional sparse coding
Journal
IF:
10.9
Papers:
5.1K
Citations:
6.8K