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dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Intrusive Load Monitoring Datasets

Author

Listed:
  • Manuel Pereira

    (ITI, LARSyS, 9020-105 Funchal, Portugal
    Ciências Exatas e Engenharia, Universidade da Madeira, 9020-105 Funchal, Portugal)

  • Nuno Velosa

    (ITI, LARSyS, 9020-105 Funchal, Portugal
    Ciências Exatas e Engenharia, Universidade da Madeira, 9020-105 Funchal, Portugal)

  • Lucas Pereira

    (ITI, LARSyS, 9020-105 Funchal, Portugal
    Ténico Lisboa, Universidade de Lisboa, 1049-001 Lisbon, Portugal)

Abstract

Datasets play a vital role in data science and machine learning research as they serve as the basis for the development, evaluation, and benchmark of new algorithms. Non-Intrusive Load Monitoring is one of the fields that has been benefiting from the recent increase in the number of publicly available datasets. However, there is a lack of consensus concerning how dataset should be made available to the community, thus resulting in considerable structural differences between the publicly available datasets. This technical note presents the DSCleaner, a Python library to clean, preprocess, and convert time series datasets to a standard file format. Two application examples using real-world datasets are also presented to show the technical validity of the proposed library.

Suggested Citation

  • Manuel Pereira & Nuno Velosa & Lucas Pereira, 2019. "dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Intrusive Load Monitoring Datasets," Data, MDPI, vol. 4(3), pages 1-12, August.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:3:p:123-:d:256867
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