Review on Deep Neural Networks applied to Low-Frequency NILM

This repo contains data and code that has been used for the publication
“Review on Deep Neural Networks applied to Low-Frequency NILM” submitted @ MDPI
Energies doi.org/10.3390/en14092390.

This work is a considerable extension of the presentation “DNN for NILM on low
frequency Data” that has been done at the NILM workshop 2019. You can find the
corresponding presentation
here

Content:

• DNN-NILM_Publication-List.xlsx contains the list of the DNN-NILM
  publications that have been reviewed in the mentioned publication. It
  corresponds with minor differences in columns and nomenclature to table 2 in
  the publication and is provided to allow for easy searching and filtering.
  Abbreviations are explained in the publication.
• Visualize_MAE.ipynb and Visualize_F1.ipynb are the jupyter notebooks that
  have been used to generate the visualizations in the paper, i.e. figures 3
  and 4. Please be aware that citation numbering might have changed in the
  final publication.
• DNN-NILM_low-freq_Performance.xlsx contains the list of metrics extracted
  from the reviewed publications. Publications that did
  • not report metrics,
  • report metrics other than F_1-score or MAE or
  • not report metrics according to the relevant evaluation scenario
    might not appear in the list. The file is the basis for the figures generated
    with the jupyter notebooks. Some explanations on the columns can be found in
    the tab Explanations. Please do not expect that all columns are filled up
    consistently.

In case you are an author of one of the publications and feel that erroneous
information has been compiled in our list, do either contact
patrick.huber@hslu.ch or open a pull request with your suggested changes. We
will appreciate your feedback!