NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring
Datasets are important for researchers to build models and test how these perform, as well as to reproduce research experiments from others. This data paper presents the NILM Performance Evaluation dataset (NILMPEds), which is aimed primarily at research reproducibility in the field of Non-intrusive...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/4/3/127 |
| _version_ | 1811187091413925888 |
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| author | Lucas Pereira |
| author_facet | Lucas Pereira |
| author_sort | Lucas Pereira |
| collection | DOAJ |
| description | Datasets are important for researchers to build models and test how these perform, as well as to reproduce research experiments from others. This data paper presents the NILM Performance Evaluation dataset (NILMPEds), which is aimed primarily at research reproducibility in the field of Non-intrusive load monitoring. This initial release of NILMPEds is dedicated to event detection algorithms and is comprised of ground-truth data for four test datasets, the specification of 47,950 event detection models, the power events returned by each model in the four test datasets, and the performance of each individual model according to 31 performance metrics. |
| first_indexed | 2024-04-11T13:57:17Z |
| format | Article |
| id | doaj.art-a489aee2c5ca49eba65ec6609c0777e9 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-04-11T13:57:17Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-a489aee2c5ca49eba65ec6609c0777e92022-12-22T04:20:17ZengMDPI AGData2306-57292019-08-014312710.3390/data4030127data4030127NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load MonitoringLucas Pereira0ITI, LARSyS, 9020-105 Funchal, PortugalDatasets are important for researchers to build models and test how these perform, as well as to reproduce research experiments from others. This data paper presents the NILM Performance Evaluation dataset (NILMPEds), which is aimed primarily at research reproducibility in the field of Non-intrusive load monitoring. This initial release of NILMPEds is dedicated to event detection algorithms and is comprised of ground-truth data for four test datasets, the specification of 47,950 event detection models, the power events returned by each model in the four test datasets, and the performance of each individual model according to 31 performance metrics.https://www.mdpi.com/2306-5729/4/3/127datasetperformance evaluationperformance metricsevent detectionnon-intrusive load monitoringdisaggregationNILMsmart grid |
| spellingShingle | Lucas Pereira NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring Data dataset performance evaluation performance metrics event detection non-intrusive load monitoring disaggregation NILM smart grid |
| title | NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring |
| title_full | NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring |
| title_fullStr | NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring |
| title_full_unstemmed | NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring |
| title_short | NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring |
| title_sort | nilmpeds a performance evaluation dataset for event detection algorithms in non intrusive load monitoring |
| topic | dataset performance evaluation performance metrics event detection non-intrusive load monitoring disaggregation NILM smart grid |
| url | https://www.mdpi.com/2306-5729/4/3/127 |
| work_keys_str_mv | AT lucaspereira nilmpedsaperformanceevaluationdatasetforeventdetectionalgorithmsinnonintrusiveloadmonitoring |