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Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques

Author

Listed:
  • Karol Bot

    (Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal)

  • Inoussa Laouali

    (Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez 1049-001, Morocco)

  • António Ruano

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

  • Maria da Graça Ruano

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

Abstract

At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.

Suggested Citation

  • Karol Bot & Inoussa Laouali & António Ruano & Maria da Graça Ruano, 2021. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques," Energies, MDPI, vol. 14(18), pages 1-27, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5852-:d:636540
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    References listed on IDEAS

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    Cited by:

    1. Ravichandran Balakrishnan & Vedadri Geetha & Muthusamy Rajeev Kumar & Man-Fai Leung, 2023. "Reduction in Residential Electricity Bill and Carbon Dioxide Emission through Renewable Energy Integration Using an Adaptive Feed-Forward Neural Network System and MPPT Technique," Sustainability, MDPI, vol. 15(19), pages 1-25, September.
    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. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.

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