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Will NILM Technology Replace Multi-Meter Telemetry Systems for Monitoring Electricity Consumption?

Author

Listed:
  • Bartłomiej Gawin

    (Department of Business Informatics, Faculty of Management, University of Gdańsk, 81-864 Sopot, Poland)

  • Robert Małkowski

    (Department of Power Electronics and Electrical Machines, Faculty of Electrical and Control Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland)

  • Robert Rink

    (Automatics and System Analysis Department, Gdańsk Division, Institute of Power Engineering Research Institute in Warsaw, 01-330 Warsaw, Poland)

Abstract

The estimation of electric power utilization, its baseload, and its heating, light, ventilation, and air-conditioning (HVAC) power component, which represents a very large portion of electricity usage in commercial facilities, are important for energy consumption controls and planning. Non-intrusive load monitoring (NILM) is the analytical method used to monitor the energy and disaggregate total electrical usage into appliance-related signals as an alternative to installing multiple electricity meters in the building. However, despite considerable progress, there are a limited number of tools dedicated to the problem of reliable and complete energy disaggregation. This paper presents an experiment consisting in designing an electrical system with electrical energy receivers, and then starting NILM disaggregation using machine learning algorithms (MLA). The quality of this disaggregation was assessed using dedicated indicators. Subsequently, the quality of these MLA was also verified using the available BLUED data source. The results show that the proposed method guarantees non-intrusive load disaggregation but still requires further research and testing. Measurement data have been published as open research data and listed in the literature section repository.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2275-:d:1081789
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    References listed on IDEAS

    as
    1. Bartlomiej Gawin & Bartosz Marcinkowski, 2020. "Setting up Energy Efficiency Management in Companies: Preliminary Lessons Learned from the Petroleum Industry," Energies, MDPI, vol. 13(21), pages 1-16, October.
    2. 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.
    3. Abrar Mahi-al-rashid & Fahmid Hossain & Adnan Anwar & Sami Azam, 2022. "False Data Injection Attack Detection in Smart Grid Using Energy Consumption Forecasting," Energies, MDPI, vol. 15(13), pages 1-17, July.
    4. Amir Rafati & Hamid Reza Shaker & Saman Ghahghahzadeh, 2022. "Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review," Energies, MDPI, vol. 15(1), pages 1-16, January.
    5. Inoussa Laouali & Isaías Gomes & Maria da Graça Ruano & Saad Dosse Bennani & Hakim El Fadili & Antonio Ruano, 2022. "Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks," Energies, MDPI, vol. 15(23), pages 1-29, November.
    6. Cristina Puente & Rafael Palacios & Yolanda González-Arechavala & Eugenio Francisco Sánchez-Úbeda, 2020. "Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques," Energies, MDPI, vol. 13(12), pages 1-20, June.
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