OPTIMISATION OF COMMERCIAL BUILDING MANAGEMENT PROCESSES USING USER BEHAVIOUR ANALYSIS SYSTEMS SUPPORTED BY COMPUTATIONAL INTELLIGENCE AND RTI

Michał Styła

michal.styla@cbrti.pl
Information Technology Research & Development Center (CBRTI sp. z o.o.) (Poland)
https://orcid.org/0000-0002-1141-0887

Przemysław Adamkiewicz


1Information Technology Research & Development Center, 2University of Economics and Innovation, Faculty of Transport and Information Technology (Poland)
http://orcid.org/0000-0003-3425-9566

Abstract

The aim of the presented project was to create a comprehensive building management system equipped with a network of wireless and energy-efficient sensors that collect data about users and on their basis control final devices such as lighting, ventilation, air conditioning and heating. In the presented system, end devices can be both products offered by the market (commercial) and proprietary solutions (own). This is to allow the adaptation of commercial radio communication protocols with high integration capabilities and common occurrence. In addition, the system has been enriched with an innovative system of tracking and building navigation and access control, which are supported by a network of radio beacons and radio-tomographic imaging technology (RTI). The whole system is to be supervised by computational intelligence learned from scratch.


Keywords:

Building automation, Building management systems, Computational intelligence, Indoor radio communication, Radio navigation, Tomography

Bluetooth SIG. Bluetooth Core Specification, version 4.0.; Bluetooth SIG: Kirkland, WA, USA, 2010.
  Google Scholar

Cavallini A.: iBeacons Bible, 2015.
  Google Scholar

Corna A., Fontana L., Nacci A. A., Sciuto D.: Occupancy detection via ibeacon on android devices for smart building management. Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE), 2015, 629–632.
DOI: https://doi.org/10.7873/DATE.2015.0753   Google Scholar

Havard N., McGrath S., Flanagan C., MacNamee C.: Smart Building Based on Internet of Thing Technology. Proceedings of the 12th International Conference on Sensing Technology (ICST), 2018.
DOI: https://doi.org/10.1109/ICSensT.2018.8603575   Google Scholar

Kozłowski E., Mazurkiewicz D., Żabiński T., Prucnal S., Sęp J.: Machining sensor data management for operation-level predictive model. Expert Syst. Appl. 159, 2020, 1–22.
DOI: https://doi.org/10.1016/j.eswa.2020.113600   Google Scholar

Liu H., Darabi H., Banerjee P., Liu J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37, 2007, 1067–1080.
DOI: https://doi.org/10.1109/TSMCC.2007.905750   Google Scholar

Matteuchi M.: An Adaptive Indoor Positioning System Based on Bluetooth Low Energy RSSI. Politecnico di Milano, Milano 2012.
  Google Scholar

Montgomery D. C., Peck E. A., Vining G. G.: Introduction to Linear Regression Analysis. World Scientific Publishing, Singapore 2012.
  Google Scholar

Node-RED Guide. Available online: http://noderedguide.com/ (accessed on 19.02.2022).
  Google Scholar

Peng Y., Rysanek A., Nagy Z., Schlter A.: Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Appl. Energy 211, 2018, 1343–1358.
DOI: https://doi.org/10.1016/j.apenergy.2017.12.002   Google Scholar

Postolache O. A., Dias Pereira J. M., Silva Girao P. M. B.: Smart Sensors Network for Air Quality Monitoring Applications. IEEE Trans. Instrum. Meas. 58, 2009, 3253–3262.
DOI: https://doi.org/10.1109/TIM.2009.2022372   Google Scholar

Rivera-Illingworth F., Callaghan V., Hagras H.: Automated Discovery of Human Activites inside Pervasive Living Spaces. Proceedings of the International Symposium on Pervasive Computing and Applications, 2006, 77–82.
DOI: https://doi.org/10.1109/SPCA.2006.297520   Google Scholar

Rymarczyk T., Kozłowski, E., Kłosowski G., Niderla, K.: Logistic Regression for Machine Learning in Process Tomography. Sensors 19, 2019, 3400.
DOI: https://doi.org/10.3390/s19153400   Google Scholar

Styła M., Oleszek M., Rymarczyk T., Maj M., Adamkiewicz P.: Hybrid sensor for detection of objects using radio tomography. Applications of Electromagnetics in Modern Engineering and Medicine, PTZE, 2019, 219–233.
DOI: https://doi.org/10.23919/PTZE.2019.8781693   Google Scholar

Suykens, J.A., Vandewalle J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 1999, 293–300.
DOI: https://doi.org/10.1023/A:1018628609742   Google Scholar

Tragos E. Z., Foti M., Surligas M., Lambropoulos G., Pournaras S., Papadakis S., Angelakis V.: An IoT based intelligent building management system for ambient assisted living. Proceedings of the IEEE International Conference on Communication Workshop (ICCW), 2015, 246–252.
DOI: https://doi.org/10.1109/ICCW.2015.7247186   Google Scholar

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Published
2022-03-31

Cited by

Styła, M., & Adamkiewicz, P. (2022). OPTIMISATION OF COMMERCIAL BUILDING MANAGEMENT PROCESSES USING USER BEHAVIOUR ANALYSIS SYSTEMS SUPPORTED BY COMPUTATIONAL INTELLIGENCE AND RTI. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(1), 28–35. https://doi.org/10.35784/iapgos.2894

Authors

Michał Styła 
michal.styla@cbrti.pl
Information Technology Research & Development Center (CBRTI sp. z o.o.) Poland
https://orcid.org/0000-0002-1141-0887

Authors

Przemysław Adamkiewicz 

1Information Technology Research & Development Center, 2University of Economics and Innovation, Faculty of Transport and Information Technology Poland
http://orcid.org/0000-0003-3425-9566

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