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Open AccessTechnical Note

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

1
ITI, LARSyS, 9020-105 Funchal, Portugal
2
Ciências Exatas e Engenharia, Universidade da Madeira, 9020-105 Funchal, Portugal
3
Ténico Lisboa, Universidade de Lisboa, 1049-001 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Data 2019, 4(3), 123; https://doi.org/10.3390/data4030123
Received: 1 July 2019 / Revised: 7 August 2019 / Accepted: 8 August 2019 / Published: 12 August 2019
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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. View Full-Text
Keywords: datasets; NILM; library; python; cleaning; preprocessing; conversion datasets; NILM; library; python; cleaning; preprocessing; conversion
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Pereira, M.; Velosa, N.; Pereira, L. dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets. Data 2019, 4, 123.

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