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Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques

Author

Listed:
  • Cristina Puente

    (Computer Science Department, ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Rafael Palacios

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Yolanda González-Arechavala

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Eugenio Francisco Sánchez-Úbeda

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

Abstract

Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations to anticipate the use of electrical appliances at home. However, the problem with this kind of analysis rests in how to isolate individual appliances from an aggregated consumption signal. In this work, we propose an unsupervised disaggregation method based on a controlled dataset obtained using smart meters in a standard household. By using soft computing techniques, the proposed methodology can identify the behavior of each of the devices from aggregated consumption records. In the approach developed in this work, it is possible to detect changes in power levels and to build a box model, consisting of a sequence of rectangles of different heights (power) and widths (time), which is highly adaptable to the real-life working conditions of household appliances. The system was developed and tested using data collected at households in France and the UK (UK-domestic appliance-level electricity (DALE) dataset). The proposed analysis method serves as a basis to be applied to large amounts of data collected by distribution companies with smart meters.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3117-:d:372387
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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.
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    6. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
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    Citations

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    Cited by:

    1. Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano & Saad Dosse Bennani & Hakim El Fadili, 2022. "Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection," Energies, MDPI, vol. 15(3), pages 1-22, February.
    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. Patricia Franco & José M. Martínez & Young-Chon Kim & Mohamed A. Ahmed, 2022. "A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions," Sustainability, MDPI, vol. 14(8), pages 1-33, April.
    4. Hwan Kim & Sungsu Lim, 2021. "Temporal Patternization of Power Signatures for Appliance Classification in NILM," Energies, MDPI, vol. 14(10), pages 1-17, May.
    5. Camilo Carrillo & Eloy Díaz Dorado & José Cidrás Pidre & Julio Garrido Campos & Diego San Facundo López & Luiz A. Lisboa Cardoso & Cristina I. Martínez Castañeda & José F. Sánchez Rúa, 2023. "Detailed Energy Analysis of a Sheet-Metal-Forming Press from Electrical Measurements," Energies, MDPI, vol. 16(19), pages 1-17, October.
    6. Fernando Sánchez Lasheras, 2021. "Predicting the Future-Big Data and Machine Learning," Energies, MDPI, vol. 14(23), pages 1-2, December.
    7. 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.

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