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Design and Modeling of Intelligent Building Office and Thermal Comfort Based on Probabilistic Neural Network


Thermal comfort is strictly related to the efficient use of the environmental and energy resources to maintain or improve the quality life and thermal well-being of the residents. This article proposes to integrate the architectural building information and processing real-time data collected by IoT system to design room office. The control of room climate, lighting, and sun protection is the major goal of this research activity, which has the advantages of simplifying user operation, improving the energy efficiency, and ensuring the minimum energy consumption. Temperature, relative humidity, pressure and lighting sensors have been installed in office room. For the simulation of a virtual working room, a 3D model has been employed. The Spatial Daylight Autonomy (sDA), and Annual Sun Exposure (ASE), the annual glare distributions and lighting energy demand have been calculated in the simulated room. The thermal comfort in the building room is controlled by a digital sensors system connected to micro-controller module. Therefore, a neural network method is proposed to enhance the data collection and classification efficiency for an advanced thermal comfort analysis in the building office. The proposed method was validated by means of a confusion matrix exhibiting the correct classifications of 100\(\%\) of samples within the testing dataset.

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The authors wish to thank Agata Goleśna, Julia Nikodem, Janusz Polański, Kornel Nalik, Piotr Białas and Agata Abela for their contribution at the laboratory of Department of Mechatronics and at the Faculty of Architecture, Department of Urban and Spatial Planning, Silesian University of Technology.


This research was supported by the project ”Including students in scientific research through research clubs and project-oriented teaching”, in connection with the participation of the Silesian University of Technology in the ”Initiative of Excellence - Research University” program (contract No. 08 / IDUB / 2019/84 of 16 December 2019). Therefore, this research was supported by project ABS-PRO (Automatic BioSignal Processing) LINEA 2, University of Catania, Italy, Research Incentive Plan ”PIA.CE.RI” 2020-2022.

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Correspondence to Grazia Lo Sciuto.

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Kciuk, M., Bijok, T. & Lo Sciuto, G. Design and Modeling of Intelligent Building Office and Thermal Comfort Based on Probabilistic Neural Network. SN COMPUT. SCI. 3, 485 (2022).

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  • Multi-sensor systems
  • Thermal comfort
  • Spatial daylight autonomy
  • Daylight glare probability
  • Confusion matrix
  • Probabilistic neural network