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Patrick Huber
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‎DNN-NILM_low-freq_Performance.xlsx

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‎README.md

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This repo contains data and code that has been used for the publication
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"Review on Deep Neural Networks applied to Low-Frequency NILM" submitted @ MDPI
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Energies. (The full reference should (hopefully) be added in close future.)
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Energies [doi.org/10.3390/en14092390](https://doi.org/10.3390/en14092390).
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This work is a considerable extension of the presentation "DNN for NILM on low
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frequency Data" that has been done at the NILM workshop 2019. You can find the
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Content:
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* `DNN-NILM_Publication-List.xlsx` contains the list of the DNN-NILM
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publications that have been reviewed in the mentioned publication. It
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corresponds with minor differences in columns and nomenclature to table 3 in
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corresponds with minor differences in columns and nomenclature to table 2 in
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the publication and is provided to allow for easy searching and filtering.
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Abbreviations are explained in the publication.
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* `Visualize_MAE.ipynb` and `Visualize_F1.ipynb` are the jupyter notebooks that
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have been used to generate the visualizations in the paper, i.e. figures 3
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and 4.
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and 4. Please be aware that citation numbering might have changed in the
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final publication.
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* `DNN-NILM_low-freq_Performance.xlsx` contains the list of metrics extracted
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from the reviewed publications. Publications that did not report metrics or
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just 'exotic' ones do not appear in the list. The file is the basis for the
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figures generated with the jupyter notebooks. Some explanations on the
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columns can be found in the tab `Explanations`. Please do not expect that
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*all* columns are filled up consistently.
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from the reviewed publications. Publications that did
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* not report metrics,
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* report metrics other than F_1-score or MAE or
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* not report metrics according to the relevant evaluation scenario
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might not appear in the list. The file is the basis for the figures generated
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with the jupyter notebooks. Some explanations on the columns can be found in
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the tab `Explanations`. Please do not expect that *all* columns are filled up
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consistently.
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In case you are an author of one of the publications and feel that erroneous
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information has been compiled in our list, do either contact

‎Visualize_F1.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"p2bbl_file = r'../dnn-nilm-manuscript/main.bbl' # path to .bbl-file\n",
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"p2bbl_file = r'main.bbl' # path to .bbl-file\n",
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"with open(p2bbl_file, 'r') as fp:\n",
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" fContent = fp.read()\n",
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" \n",
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‎Visualize_MAE.ipynb

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"metadata": {},
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"p2bbl_file = r'../dnn-nilm-manuscript/main.bbl' # path to .bbl-file\n",
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"p2bbl_file = r'main.bbl' # path to .bbl-file\n",
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"with open(p2bbl_file, 'r') as fp:\n",
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" fContent = fp.read()\n",
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" \n",
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