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Load Disaggregation Using Microscopic Power Features and Pattern Recognition

Author

Listed:
  • Wesley Angelino de Souza

    (Department of Computer Science, Federal University of São Carlos (UFSCar), Sorocaba SP 18052-780, Brazil)

  • Fernando Deluno Garcia

    (Institute of Science and Technology of Sorocaba, São Paulo State University (UNESP), Sorocaba SP 18087-180, Brazil)

  • Fernando Pinhabel Marafão

    (Institute of Science and Technology of Sorocaba, São Paulo State University (UNESP), Sorocaba SP 18087-180, Brazil)

  • Luiz Carlos Pereira da Silva

    (School of Electrical and Computer Engineering (FEEC), University of Campinas (UNICAMP), Campinas SP 13083-852, Brazil)

  • Marcelo Godoy Simões

    (Department of Electrical Engineering, Colorado School of Mines, Golden, CO 80401, USA)

Abstract

A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set.

Suggested Citation

  • Wesley Angelino de Souza & Fernando Deluno Garcia & Fernando Pinhabel Marafão & Luiz Carlos Pereira da Silva & Marcelo Godoy Simões, 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition," Energies, MDPI, vol. 12(14), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2641-:d:247078
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    References listed on IDEAS

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    Cited by:

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    2. Hari Prasad Devarapalli & V. S. S. Siva Sarma Dhanikonda & Sitarama Brahmam Gunturi, 2020. "Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion," Energies, MDPI, vol. 13(18), pages 1-15, September.
    3. İsmail Hakkı Çavdar & Vahit Feryad, 2021. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid," Energies, MDPI, vol. 14(15), pages 1-21, July.
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    5. Petros Papageorgiou & Dimitra Mylona & Konstantinos Stergiou & Aggelos S. Bouhouras, 2023. "A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images," Sustainability, MDPI, vol. 15(17), pages 1-14, August.

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