Energy Disaggregation via Learning Powerlets and Sparse Coding

Abstract

In this paper, we consider the problem of energy disaggregation, i.e., decomposing a whole home electricity signal into its component appliances. We propose a new supervised algorithm, which in the learning stage, automatically extracts signature consumption patterns of each device by modeling the device as a mixture of dynamical systems. In order to extract signature consumption patterns of a device corresponding to its different modes of operation, we define appropriate dissimilarities between energy snippets of the device and use them in a subset selection scheme, which we generalize to deal with time-series data. We then form a dictionary that consists of extracted power signatures across all devices. We cast the disaggregation problem as an optimization over a representation in the learned dictionary and incorporate several novel priors such as device-sparsity, knowledge about devices that do or do not work together as well as temporal consistency of the disaggregated solution. Real experiments on a publicly available energy dataset demonstrate that our proposed algorithm achieves promising results for energy disaggregation.

Cite

Text

Elhamifar and Sastry. "Energy Disaggregation via Learning Powerlets and Sparse Coding." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9249

Markdown

[Elhamifar and Sastry. "Energy Disaggregation via Learning Powerlets and Sparse Coding." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/elhamifar2015aaai-energy/) doi:10.1609/AAAI.V29I1.9249

BibTeX

@inproceedings{elhamifar2015aaai-energy,
  title     = {{Energy Disaggregation via Learning Powerlets and Sparse Coding}},
  author    = {Elhamifar, Ehsan and Sastry, Shankar},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {629-635},
  doi       = {10.1609/AAAI.V29I1.9249},
  url       = {https://mlanthology.org/aaai/2015/elhamifar2015aaai-energy/}
}