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Technical Note

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

by 1,2, 1,2 and 1,3,*
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
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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MDPI and ACS Style

Pereira, M.; Velosa, N.; Pereira, L. dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Intrusive Load Monitoring Datasets. Data 2019, 4, 123. https://doi.org/10.3390/data4030123

AMA Style

Pereira M, Velosa N, Pereira L. dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Intrusive Load Monitoring Datasets. Data. 2019; 4(3):123. https://doi.org/10.3390/data4030123

Chicago/Turabian Style

Pereira, Manuel, Nuno Velosa, and Lucas Pereira. 2019. "dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Intrusive Load Monitoring Datasets" Data 4, no. 3: 123. https://doi.org/10.3390/data4030123

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