dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets
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 availab...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Data |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5729/4/3/123 |
| _version_ | 1818008294492471296 |
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| author | Manuel Pereira Nuno Velosa Lucas Pereira |
| author_facet | Manuel Pereira Nuno Velosa Lucas Pereira |
| author_sort | Manuel Pereira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T05:27:17Z |
| format | Article |
| id | doaj.art-8b2898acc0a8489a8aa93e6706bb1d8e |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-04-14T05:27:17Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-8b2898acc0a8489a8aa93e6706bb1d8e2022-12-22T02:09:56ZengMDPI AGData2306-57292019-08-014312310.3390/data4030123data4030123dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring DatasetsManuel Pereira0Nuno Velosa1Lucas Pereira2ITI, LARSyS, 9020-105 Funchal, PortugalITI, LARSyS, 9020-105 Funchal, PortugalITI, LARSyS, 9020-105 Funchal, PortugalDatasets 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.https://www.mdpi.com/2306-5729/4/3/123datasetsNILMlibrarypythoncleaningpreprocessingconversion |
| spellingShingle | Manuel Pereira Nuno Velosa Lucas Pereira dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets Data datasets NILM library python cleaning preprocessing conversion |
| title | dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets |
| title_full | dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets |
| title_fullStr | dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets |
| title_full_unstemmed | dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets |
| title_short | dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets |
| title_sort | dscleaner a python library to clean preprocess and convert non instrusive load monitoring datasets |
| topic | datasets NILM library python cleaning preprocessing conversion |
| url | https://www.mdpi.com/2306-5729/4/3/123 |
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