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Data Requirements for Applying Machine Learning to Energy Disaggregation

Author

Listed:
  • Changho Shin

    (Encored Technologies; Seoul 06109, Korea
    Contributed equally.)

  • Seungeun Rho

    (NC Soft Inc., Seongnam 13494, Korea
    Contributed equally.)

  • Hyoseop Lee

    (Encored Technologies; Seoul 06109, Korea)

  • Wonjong Rhee

    (Department of Transdisciplinary Studies, Seoul National University, Seoul 08826, Korea)

Abstract

Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. By applying three classification algorithms (vanilla DNN (Deep Neural Network), ML (Machine Learning) with feature engineering, and CNN (Convolutional Neural Network) with hyper-parameter tuning) and a recent regression algorithm (Subtask Gated Network) to the new dataset, we show that NILM performance can be significantly limited when the data sampling rate is too low or when the number of distinct houses in the dataset is too small. The well-known NILM datasets that are popular in the research community do not meet these requirements. Our results indicate that higher quality datasets should be used to expedite the progress of NILM research.

Suggested Citation

  • Changho Shin & Seungeun Rho & Hyoseop Lee & Wonjong Rhee, 2019. "Data Requirements for Applying Machine Learning to Energy Disaggregation," Energies, MDPI, vol. 12(9), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1696-:d:228376
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    References listed on IDEAS

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    1. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    2. Rashid, Haroon & Singh, Pushpendra & Stankovic, Vladimir & Stankovic, Lina, 2019. "Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?," Applied Energy, Elsevier, vol. 238(C), pages 796-805.
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    Citations

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    Cited by:

    1. Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.
    2. Netzah Calamaro & Moshe Donko & Doron Shmilovitz, 2021. "A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements," Energies, MDPI, vol. 14(21), pages 1-37, November.
    3. Bartłomiej Gawin & Robert Małkowski & Robert Rink, 2023. "Will NILM Technology Replace Multi-Meter Telemetry Systems for Monitoring Electricity Consumption?," Energies, MDPI, vol. 16(5), pages 1-26, February.
    4. Muhammad Asif Ali Rehmani & Saad Aslam & Shafiqur Rahman Tito & Snjezana Soltic & Pieter Nieuwoudt & Neel Pandey & Mollah Daud Ahmed, 2021. "Power Profile and Thresholding Assisted Multi-Label NILM Classification," Energies, MDPI, vol. 14(22), pages 1-18, November.

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