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Energy consumption prediction of a smart home using non-intrusive appliance load monitoring

Author

Listed:
  • Lazhar Chabane

    (University of 8 Mai 1945 Geulma
    University of Batna 2)

  • Said Drid

    (University of Batna 2
    Higher National School of Renewable Energy, Environment and Sustainable Development)

  • Larbi Chrifi-Alaoui

    (University of Picardie Jules Verne, IUT de l’Aisne)

  • Laurant Delahoche

    (University of Picardie Jules Verne, IUT de l’Aisne)

Abstract

The increasing need for energy has been a major problem in recent years. In view of this problem, energy saving and reduction of energy consumption are strongly encouraged. The residential sector accounts an important part of final energy consumption and is therefore a major challenge for improving energy efficiency. In this work, individual energy consumption is determined from measurements taken downstream at the energy meter using a single current and a single voltage sensor, without a learning phase or knowledge of the equipment inside the home. This non-intrusive appliance load monitoring (NIALM) method has several advantages: it allows us to process the load curves and to extract useful information for the identification of the uses and to prevent the most energy consuming appliances. In addition, we will apply the Auto Regressive Moving Average with eXternal inputs (ARMAX) model to predict the energy consumption. These two approaches will allow us to better analyze the management, control, metering and billing system of consumption in order to ensure better energy efficiency in buildings.

Suggested Citation

  • Lazhar Chabane & Said Drid & Larbi Chrifi-Alaoui & Laurant Delahoche, 2024. "Energy consumption prediction of a smart home using non-intrusive appliance load monitoring," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(3), pages 1231-1244, March.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:3:d:10.1007_s13198-023-02209-3
    DOI: 10.1007/s13198-023-02209-3
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    References listed on IDEAS

    as
    1. Baillie, Richard T., 1980. "Predictions from ARMAX models," Journal of Econometrics, Elsevier, vol. 12(3), pages 365-374, April.
    2. Larbi Chrifi-Alaoui & Saïd Drid & Mohammed Ouriagli & Driss Mehdi, 2023. "Overview of Photovoltaic and Wind Electrical Power Hybrid Systems," Energies, MDPI, vol. 16(12), pages 1-35, June.
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