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NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring

by 1,2
1
ITI, LARSyS, 9020-105 Funchal, Portugal
2
Ténico Lisboa, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Data 2019, 4(3), 127; https://doi.org/10.3390/data4030127
Received: 25 June 2019 / Revised: 27 July 2019 / Accepted: 21 August 2019 / Published: 24 August 2019
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. View Full-Text
Keywords: dataset; performance evaluation; performance metrics; event detection; non-intrusive load monitoring; disaggregation; NILM; smart grid dataset; performance evaluation; performance metrics; event detection; non-intrusive load monitoring; disaggregation; NILM; smart grid
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Pereira, L. NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring. Data 2019, 4, 127.

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