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

Author

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  • Lucas Pereira

    (ITI, LARSyS, 9020-105 Funchal, Portugal
    Ténico Lisboa, Universidade de Lisboa, 1049-001 Lisbon, Portugal)

Abstract

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.

Suggested Citation

  • Lucas Pereira, 2019. "NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring," Data, MDPI, vol. 4(3), pages 1-9, August.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:3:p:127-:d:260635
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    References listed on IDEAS

    as
    1. Paula Meehan & Conor McArdle & Stephen Daniels, 2014. "An Efficient, Scalable Time-Frequency Method for Tracking Energy Usage of Domestic Appliances Using a Two-Step Classification Algorithm," Energies, MDPI, vol. 7(11), pages 1-26, October.
    2. Stephen Makonin & Z. Jane Wang & Chris Tumpach, 2018. "RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis," Data, MDPI, vol. 3(1), pages 1-9, February.
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