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nilm_transfer_learning

This repository is the code basis for the paper titled "Using Deep Learning and Knowledge Transfer to Disaggregate Energy Consumption"

https://github.com/rgtzths/nilm_transfer_learning

Science Score: 57.0%

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    Found 1 DOI reference(s) in README
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    Low similarity (8.5%) to scientific vocabulary

Keywords

machine-learning nilm nilm-algorithms transfer-learning
Last synced: 6 months ago · JSON representation ·

Repository

This repository is the code basis for the paper titled "Using Deep Learning and Knowledge Transfer to Disaggregate Energy Consumption"

Basic Info
  • Host: GitHub
  • Owner: rgtzths
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 580 KB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
machine-learning nilm nilm-algorithms transfer-learning
Created over 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

NILM Transfer Learning

Python Requirements

Create a conda environment

Create a conda environment - conda env create -f environment.yml

Activate the environment - conda activate nilmtk-env

Install NILMTK with the changes made - conda install nilmtk-3.5-py_0.tar.bz2 (might need to build package if using not using liinux - the dir with everything you need is the nilmtk folder)

Install Packages (in the environment)

  • Install tensorflow
    • pip3 install tensorflow==2.5.0
  • Install PyWavellets
    • pip3 install PyWavelets==1.1.1

Datasets

The datasets should be placed outside the repository in a folder called datasets. The folder structure should be:

  • datasets

    • ukdale
    • ukdale.h5
    • refit
    • refit.h5

  • Download UKDale H5 - https://data.ukedc.rl.ac.uk/browse/edc/efficiency/residential/EnergyConsumption/Domestic/UK-DALE-2017/UK-DALE-FULL-disaggregated/ukdale.h5.zip

  • Download REFIT CSV -

  • Convert REFIT to H5 using the NILMTK Converter

Side Note

In the transfer learning process you need to change the fridgefrezzer name in the refit baseresults to fridge.

Authors

License

This project is licensed under the MIT License - see the LICENSE file for details

Citation

If you use this code please site our work: Teixeira, Rafael & Antunes, Mário & Gomes, Diogo. (2021). Using Deep Learning and Knowledge Transfer to Disaggregate Energy Consumption. 1-7. 10.1109/ICWAPR54887.2021.9736149.

Owner

  • Name: Rafael Teixeira
  • Login: rgtzths
  • Kind: user
  • Location: Aveiro
  • Company: Instituto de Telecomunicações

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Rafael"
  given-names: "Teixeira"
  orcid: "https://orcid.org/0000-0000-0000-0000"
title: "Using Deep Learning and Knowledge Transfer to Disaggregate Energy Consumption"
version: 1.0.0
doi: 10.1109/ICWAPR54887.2021.9736149
date-released: 2021-12-05
url: "https://github.com/rgtzths/ICMLC_ICWAPR_code_base"
preferred-citation:
  type: conference-paper
  authors:
  - family-names: "Teixeira"
    given-names: "Rafael"
    orcid: "https://orcid.org/0000-0001-7211-382X"
  - family-names: "Antunes"
    given-names: "Mário"
    orcid: "https://orcid.org/0000-0002-6504-9441"
    orcid: "https://orcid.org/0009-0008-1193-2483"
  - family-names: "Gomes"
    given-names: "Diogo"
    orcid: "https://orcid.org/0000-0002-5848-2802"
  title: "Using Deep Learning and Knowledge Transfer to Disaggregate Energy Consumption"
  doi: 10.1109/ICWAPR54887.2021.9736149
  conference:
    name: "International Conference on Wavelet Analysis and Pattern Recognition"
    city: "Adelaide"
    country: "Australia"
    date-start: 2021-12-04
    date-end: 2021-12-05

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Dependencies

nilmtk/setup.py pypi
  • hmmlearn >=0.2.1
  • jupyterlab *
  • matplotlib ==3.1.3
  • networkx ==2.1
  • numpy *
  • pandas ==0.25.3
  • pyyaml *
  • scikit-learn >=0.21.2
  • scipy *
  • tables *
environment.yml pypi
nilmtk/environment.yml pypi