Integration of Machine Learning Solutions in the Building Automation System

Bartlomiej Kawa and Piotr Borkowski ()
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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

Energies, 2023, vol. 16, issue 11, 1-18

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.

Keywords: building management system; anomaly detection; cloud building system; system integration; machine learning; isolation forest; home assistant; energy monitoring; energy consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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