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

Manuel Pereira, Nuno Velosa and Lucas Pereira
Additional contact information
Manuel Pereira: ITI, LARSyS, 9020-105 Funchal, Portugal
Nuno Velosa: ITI, LARSyS, 9020-105 Funchal, Portugal
Lucas Pereira: ITI, LARSyS, 9020-105 Funchal, Portugal

Data, 2019, vol. 4, issue 3, 1-12

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.

Keywords: datasets; NILM; library; python; cleaning; preprocessing; conversion (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/4/3/123/pdf (application/pdf)
https://www.mdpi.com/2306-5729/4/3/123/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:4:y:2019:i:3:p:123-:d:256867

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().