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Quantifying Compressed Air Leakage through Non-Intrusive Load Monitoring Techniques in the Context of Energy Audits

Author

Listed:
  • Gustavo Felipe Martin Nascimento

    (G2Elab, Grenoble INP, CNRS, Université Grenoble Alpes, F-38000 Grenoble, France
    Department of Electrical and Electronic Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil)

  • Frédéric Wurtz

    (G2Elab, Grenoble INP, CNRS, Université Grenoble Alpes, F-38000 Grenoble, France)

  • Patrick Kuo-Peng

    (Department of Electrical and Electronic Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil)

  • Benoit Delinchant

    (G2Elab, Grenoble INP, CNRS, Université Grenoble Alpes, F-38000 Grenoble, France)

  • Nelson Jhoe Batistela

    (Department of Electrical and Electronic Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil)

Abstract

Following the recent European directives highlighting the need to increase energy efficiency in the European Union, this work aims to show the possibility of using Non-Intrusive Load Monitoring (NILM) techniques to improve energy audits by estimating the compressed air leakage from a dataset of a tertiary building. The first step towards the reduction of energy consumption is performing an energy audit, in which a detailed analysis of the energy performance is executed. This analysis usually uses on-site measured data by the auditors. However, the time available for these measurements is limited and may not include some modes of operation. One example of that is the quantification of compressed air leaks. This task can be performed by estimating the flow rate during a no compressed air consumption period. However, these periods may not coincide with the auditors’ original schedule. This problem could be addressed by using historical data. Nevertheless, historical data from energy management systems usually are only available for global consumption, and rarely for individual appliances. In this context, a NILM approach would be helpful to enhance energy audits carrying analysis of modes of operation not included in the on-site measurements. In this paper, the leaks are firstly quantified using measurements mostly for benchmarking purposes. The results suggested 62% of leaks in the study case. In a second step, the Factorial Hidden Markov Model (FHMM) was applied to the data. Five typical working days, simulating the context of an energy audit, were used as training data, while one week during vacation time, with no compressed air consumption, was used to quantify the leaks. The results show that it was possible, in the context of an energy audit, to estimate the compressed air leakage using NILM techniques in this dataset with less than a 1% difference when compared to the estimation made with actual measurement. Finally, savings estimations considering the elimination of the leaks were performed, varying between 10% and 100% of the leakage repair. Considering the ideal scenario of complete leaks elimination, the savings would represent around 44% in the compressed air system and 4.75% of the current annual global consumption.

Suggested Citation

  • Gustavo Felipe Martin Nascimento & Frédéric Wurtz & Patrick Kuo-Peng & Benoit Delinchant & Nelson Jhoe Batistela, 2022. "Quantifying Compressed Air Leakage through Non-Intrusive Load Monitoring Techniques in the Context of Energy Audits," Energies, MDPI, vol. 15(9), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3213-:d:803977
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    References listed on IDEAS

    as
    1. Mario E. Berges & Ethan Goldman & H. Scott Matthews & Lucio Soibelman, 2010. "Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring," Journal of Industrial Ecology, Yale University, vol. 14(5), pages 844-858, October.
    2. Christos Athanasiadis & Dimitrios Doukas & Theofilos Papadopoulos & Antonios Chrysopoulos, 2021. "A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption," Energies, MDPI, vol. 14(3), pages 1-23, February.
    3. Gustavo Felipe Martin Nascimento & Frédéric Wurtz & Patrick Kuo-Peng & Benoit Delinchant & Nelson Jhoe Batistela, 2021. "Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error," Energies, MDPI, vol. 14(24), pages 1-15, December.
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