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Benchmarking Optimization-Based Energy Disaggregation Algorithms

Author

Listed:
  • Oladayo S. Ajani

    (Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Korea)

  • Abhishek Kumar

    (Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Korea)

  • Rammohan Mallipeddi

    (Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Korea)

  • Swagatam Das

    (Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700108, India)

  • Ponnuthurai Nagaratnam Suganthan

    (School of Electrical and Electronic Engineering, Nanyang Technical University, Singapore 639798, Singapore)

Abstract

Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED.

Suggested Citation

  • Oladayo S. Ajani & Abhishek Kumar & Rammohan Mallipeddi & Swagatam Das & Ponnuthurai Nagaratnam Suganthan, 2022. "Benchmarking Optimization-Based Energy Disaggregation Algorithms," Energies, MDPI, vol. 15(5), pages 1-19, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1600-:d:755160
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    References listed on IDEAS

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    1. Idiano D’Adamo & Pasquale Marcello Falcone & Michael Martin & Paolo Rosa, 2020. "A Sustainable Revolution: Let’s Go Sustainable to Get Our Globe Cleaner," Sustainability, MDPI, vol. 12(11), pages 1-5, May.
    2. Tsai, Men-Shen & Lin, Yu-Hsiu, 2012. "Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation," Applied Energy, Elsevier, vol. 96(C), pages 55-73.
    3. Pamulapati, Trinadh & Mallipeddi, Rammohan & Lee, Minho, 2020. "Multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling," Applied Energy, Elsevier, vol. 267(C).
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    Cited by:

    1. Jeewon Park & Oladayo S. Ajani & Rammohan Mallipeddi, 2023. "Optimization-Based Energy Disaggregation: A Constrained Multi-Objective Approach," Mathematics, MDPI, vol. 11(3), pages 1-13, January.

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