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Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks

Author

Listed:
  • Inoussa Laouali

    (DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez P.O. Box 2202, Morocco)

  • Isaías Gomes

    (DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1950-044 Lisboa, Portugal
    ICT, University of Evora, 7002-554 Evora, Portugal)

  • Maria da Graça Ruano

    (DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    CISUC, Faculty of Science & Technology, University of Coimbra, 3030-290 Coimbra, Portugal)

  • Saad Dosse Bennani

    (SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez P.O. Box 2202, Morocco)

  • Hakim El Fadili

    (LIPI, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Bensouda, Fez P.O. Box 5206, Morocco)

  • Antonio Ruano

    (DEEI, Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1950-044 Lisboa, Portugal)

Abstract

Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9073-:d:989145
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    References listed on IDEAS

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    1. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    2. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    3. 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.
    4. Balezentis, Tomas, 2020. "Shrinking ageing population and other drivers of energy consumption and CO2 emission in the residential sector: A case from Eastern Europe," Energy Policy, Elsevier, vol. 140(C).
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    Cited by:

    1. Wang, Gang & Li, Zhao & Luo, Zhao & Zhang, Tao & Lin, Mingliang & Li, Jiahao & Shen, Xin, 2024. "Dynamic adaptive event detection strategy based on power change-point weighting model," Applied Energy, Elsevier, vol. 361(C).
    2. 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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