Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Attia S, Hensen JL. Investigating the impact of different thermal comfort models for zero energy buildings in hot climates. In: Proceedings 1st int. conf. on energy and indoor environment for hot climates; 2014.

  2. Nicol JF, Humphreys M. Understanding the adaptive approach to thermal comfort. ASHRAE Trans. 1998;104(1):991–1004.

    Google Scholar 

  3. Ma N, Aviv D, Guo H, Braham WW. Measuring the right factors: a review of variables and models for thermal comfort and indoor air quality. Renewa Sustain Energy Rev. 2021;135: 110436.

    Article  Google Scholar 

  4. Jung W, Jazizadeh F. Energy saving potentials of integrating personal thermal comfort models for control of building systems: comprehensive quantification through combinatorial consideration of influential parameters. Appl Energy. 2020;268: 114882.

    Article  Google Scholar 

  5. Kang M, Kim KR, Lee J-Y, Shin J-Y. Determination of thermal sensation levels for Koreans based on perceived temperature and climate chamber experiments with hot and humid settings. Int J Biometeorol. 2022;66:1095–107.

    Article  Google Scholar 

  6. Liu W, Lian Z, Liu Y. Heart rate variability at different thermal comfort levels. Eur J Appl Physiol. 2008;103(3):361–6.

    Article  Google Scholar 

  7. Vanos JK, Warland JS, Gillespie TJ, Kenny NA. Review of the physiology of human thermal comfort while exercising in urban landscapes and implications for bioclimatic design. Int J Biometeorol. 2010;54(4):319–34.

    Article  Google Scholar 

  8. Matzarakis A, Amelung B. Physiological equivalent temperature as indicator for impacts of climate change on thermal comfort of humans. In: Seasonal forecasts, climatic change and human health. Springer; 2008. p. 161–172.

  9. Katavoutas G, Flocas HA, Matzarakis A. Dynamic modeling of human thermal comfort after the transition from an indoor to an outdoor hot environment. Int J Biometeorol. 2015;59(2):205–16.

    Article  Google Scholar 

  10. De Dear R, Schiller Brager G. The adaptive model of thermal comfort and energy conservation in the built environment. Int J Biometeorol. 2001;45(2):100–8.

    Article  Google Scholar 

  11. Yang W, Zhang G. Thermal comfort in naturally ventilated and air-conditioned buildings in humid subtropical climate zone in China. Int J Biometeorol. 2008;52(5):385–98.

    Article  Google Scholar 

  12. Djamila H, Chu C-M, Kumaresan S. Effect of humidity on thermal comfort in the humid tropics. J Build Constr Plan Res. 2014;2(02):109.

    Google Scholar 

  13. Mabdeh S, Ahmad S, Alradaideh T, Bataineh A. Low-cost ventilation strategies to improve the indoor environmental quality by enhancing the natural ventilation in multistory residential buildings. Period Eng Nat Sci. 2020;8(4):2045–67.

    Google Scholar 

  14. Haldi F, Robinson D. Modelling occupants’ personal characteristics for thermal comfort prediction. Int J Biometeorol. 2011;55(5):681–94.

    Article  Google Scholar 

  15. Kielan P, Kciuk M, Piecyk J. Biofeedback therapy application with eeg signal visualization and the optimization of success factor algorithm. Int J Electron Telecommun. 2020;607–12.

  16. Islam M, Rahaman A. Development of smart healthcare monitoring system in iot environment. SN Comput Sci. 2020;1(3):1–11.

    Article  Google Scholar 

  17. Kehagias D, Jankovic M, Siavvas M, Gelenbe E. Investigating the interaction between energy consumption, quality of service, reliability, security, and maintainability of computer systems and networks. SN Comput Sci. 2021;2(1):1–6.

    Article  Google Scholar 

  18. Akter L, Islam M, Al-Rakhami MS, Haque M. Prediction of cervical cancer from behavior risk using machine learning techniques. SN Comput Sci. 2021;2(3):1–10.

    Article  Google Scholar 

  19. Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):1–21.

    Article  MathSciNet  Google Scholar 

  20. Ganesh GA, Sinha SL, Verma TN, Dewangan SK. Investigation of indoor environment quality and factors affecting human comfort: a critical review. Build Environ. 2021;204: 108146.

    Article  Google Scholar 

  21. Kumar S, Tiwari P, Zymbler M. Internet of things is a revolutionary approach for future technology enhancement: a review. J Big data. 2019;6(1):1–21.

    Article  Google Scholar 

  22. Kciuk M. Openwrt operating system based controllers for mobile robot and building automation system students projects realization. In: 15th International Workshop on Research and Education in Mechatronics (REM); 2014. IEEE. p. 1–4

  23. Barroso S, Bustos P, Núñez P. Towards a cyber-physical system for sustainable and smart building: a use case for optimising water consumption on a smartcampus. J Ambient Intell Humaniz Comput. 2022;1–21.

  24. Yu J, Kim M, Bang H-C, Bae S-H, Kim S-J. Iot as a applications: cloud-based building management systems for the internet of things. Multimed Tools Appl. 2016;75(22):14583–96.

    Article  Google Scholar 

  25. Song J, Kunz A, Schmidt M, Szczytowski P. Connecting and managing m2m devices in the future internet. Mobile Netw Appl. 2014;19(1):4–17.

    Article  Google Scholar 

  26. Eckhart M, Ekelhart A. Digital twins for cyber-physical systems security: state of the art and outlook. Secur Qual Cyber-Phys Syst Eng. 2019;383–412.

  27. Zhang H, Yan Q, Wen Z. Information modeling for cyber-physical production system based on digital twin and automationml. Int J Adv Manuf Technol. 2020;107(3):1927–45.

    Article  Google Scholar 

  28. Zhao Q, Lian Z, Lai D. Thermal comfort models and their developments: a review. Energy Built Environ. 2021;2(1):21–33.

    Article  Google Scholar 

  29. Rajasekar SJS. An enhanced iot based tracing and tracking model for covid-19 cases. SN Comput Sci. 2021;2(1):1–4.

    Article  MathSciNet  Google Scholar 

  30. Uribe D, Bustamante W, Vera S. Potential of perforated exterior louvers to improve the comfort and energy performance of an office space in different climates. In: Building simulation, vol. 11, pp. 695–708. Springer; 2018.

  31. McNaughton EJ, Gaston KJ, Beggs JR, Jones DN, Stanley MC. Areas of ecological importance are exposed to risk from urban sky glow: Auckland, aotearoa-new zealand as a case study. Urban Ecosyst. 2022;25(1):273–84.

    Article  Google Scholar 

  32. Davoodi A, Johansson P, Aries M. The use of lighting simulation in the evidence-based design process: a case study approach using visual comfort analysis in offices. In: Building simulation, vol. 13, pp. 141–153. Springer; 2020.

  33. Fernandes LL, Lee ES, Thanachareonkit A, Selkowitz SE. Potential annual daylighting performance of a high-efficiency daylight redirecting slat system. In: Building simulation, vol. 14, pp. 495–510. Springer; 2021.

  34. Weather Data Sources Weather Data Download - Katowice 125600. https://energyplus.net/weather-location/europe_wmo_region_6/POL/POL_Katowice.125600_IMGW

Download references

Acknowledgements

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.

Funding

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.

Author information

Author notes

  1. Marek Kciuk and Tomasz Bijok are contributing authors.

Authors and Affiliations

  1. Department of Mechatronics, Silesian University of Technology, Akademicka 10a, 44-100, Gliwice, Poland

    Marek Kciuk & Grazia Lo Sciuto

  2. Department of Urban and Spatial Planning, Faculty of Architecture, Silesian University of Technology, Akademicka 7, 44-100, Gliwice, Poland

    Tomasz Bijok

  3. Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria, 6, 95125, Catania, Italy

    Grazia Lo Sciuto

Authors
  1. Marek Kciuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomasz Bijok
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Grazia Lo Sciuto
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have contributed equally. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Grazia Lo Sciuto.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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). https://doi.org/10.1007/s42979-022-01411-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01411-7

Keywords

Search

Navigation