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Integration of Machine Learning Solutions in the Building Automation System

Author

Listed:
  • Bartlomiej Kawa

    (Department of Electrical Apparatus, Faculty of Electrical, Electronic, Computer and Control Engineering, Technical University of Lodz, 90-537 Lodz, Poland)

  • Piotr Borkowski

    (Department of Electrical Apparatus, Faculty of Electrical, Electronic, Computer and Control Engineering, Technical University of Lodz, 90-537 Lodz, Poland)

Abstract

This publication presents a system for integrating machine learning and artificial intelligence solutions with building automation systems. The platform is based on cloud solutions and can integrate with one of the most popular virtual building management solutions, HomeAssistant. The System uses communication based on the Message Queue Telemetry Transport (MQTT) protocol. The example machine learning function described in this publication detects anomalies in the electricity waveforms and raises the alarm. This information determines power quality and detects system faults or unusual power consumption. Recently, increasing electricity prices on global markets have meant that buildings must significantly reduce consumption. Therefore, a fundamental element of energy consumption diagnostics requires detecting unusual forms of energy consumption to optimise the use of individual devices in home and office installations.

Suggested Citation

  • Bartlomiej Kawa & Piotr Borkowski, 2023. "Integration of Machine Learning Solutions in the Building Automation System," Energies, MDPI, vol. 16(11), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4504-:d:1163115
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    References listed on IDEAS

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    Cited by:

    1. Gabriela Walczyk & Andrzej Ożadowicz, 2024. "Building Information Modeling and Digital Twins for Functional and Technical Design of Smart Buildings with Distributed IoT Networks—Review and New Challenges Discussion," Future Internet, MDPI, vol. 16(7), pages 1-27, June.
    2. Andrzej Ożadowicz, 2023. "Technical, Qualitative and Energy Analysis of Wireless Control Modules for Distributed Smart Home Systems," Future Internet, MDPI, vol. 15(9), pages 1-21, September.

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