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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Bibliographic Details
Published inData (Basel) Vol. 4; no. 3; p. 127
Main Author Pereira, Lucas
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2019
Subjects
Algorithms
Appliances
Business metrics
dataset
Datasets
disaggregation
Electric rates
Electricity distribution
event detection
Labeling
Monitoring
NILM
non-intrusive load monitoring
Performance evaluation
Performance measurement
performance metrics
smart grid
United Kingdom > UK
Online AccessGet full text
ISSN2306-5729
2306-5729
DOI10.3390/data4030127

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Summary: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.
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ISSN:2306-5729
2306-5729
DOI:10.3390/data4030127