Samet ERKOL, Melih YÜCESAN, Muhammet GÜL, Ali Fuat GÜNERI

Investigation of issues affecting thermal comfort in water system underfloor heating applications of buildings with Bayesian networks

Thermal comfort is related to the stability of the ambient temperature. Constant changes in ambient temperature appear as a situation that negatively affects comfort. The selected build-ing systems must be arranged to maintain this stability for the continuity of thermal comfort. In this study, issues affecting thermal comfort in water system underfloor heating applications of buildings are handled and analyzed using the Bayesian Network modeling methodology. Visual examples of the problems encountered in field applications are also given. Three dif-ferent scenarios are tested with the constructed Bayesian Network model. In the first scenario, assumed that mechanical project failures were prevented. In this case, it was observed that the failure rate decreased by about 5%. In the second scenario, assumed that mechanical applica-tion failures are prevented along with mechanical project failures. The failure rate decreased by 11% compared to the first situation. The third scenario assumed that the mechanical proj-ect preparation phase was concluded without any problems, the mechanical project was im-plemented without any failures, and the mechanical system was commissioned without any problems. In the last scenario, the failure rate decreased by 14% compared to the first case, and the probability of not providing thermal comfort remained at 2%. As a result of these three scenarios, the possibility of not providing thermal comfort in the underfloor heating system is detailed and interpreted.

Keywords:

Bayesian Network, Thermal Comfort, Water System Underfloor Heating,

PDF

___

REFERENCES [1] Veeraboina P, Ratnam GY. Analysis of the opportunities and challenges of solar water heating system (SWHS) in India: Estimates from the energy audit surveys & review. Renew Sustain Energy Rev 2012;16:668–676. [CrossRef]
[2] Parhizkar T, Aramoun F, Esbati S, Saboohi Y. Efficient performance monitoring of building central heating system using Bayesian Network method. J Build Eng 2019;26:100835. [CrossRef]
[3] Barzin R, Chen JJJ, Young BR, Farid MM. Application of PCM underfloor heating in combination with PCM wallboards for space heating using price based control system. Appl Energy 2015;148:39–48. [CrossRef]
[4] Wang Z, de Dear R, Luo M, Lin B, He Y, Ghahramani A, Zhu Y. Individual difference in thermal comfort: A literature review. Build Environ 2018;138:181–193. [CrossRef]
[5] Khodakarami J, Nasrollahi N. Thermal comfort in hospitals – A literature review. Renew Sustain Energy Rev 2012;16:4071–4077. [CrossRef]
[6] Park JY, Nagy Z. Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review. Renew Sustain Energy Rev 2018;82:2664–2679. [CrossRef]
[7] Karmann C, Schiavon S, Bauman F. Thermal comfort in buildings using radiant vs. all-air systems: A critical literature review. Build Environ 2017;111:123–131. [CrossRef]
[8] Djongyang N, Tchinda R, Njomo D. Thermal comfort: A review paper. Renew Sustain Energy Rev 2010;14:2626–2640. [CrossRef]
[9] Rupp RF, Vásquez NG, Lamberts R. A review of human thermal comfort in the built environment. Energy Build 2015;105:178–205. [CrossRef]
[10] Zomorodian ZS, Tahsildoost M, Hafezi M. Thermal comfort in educational buildings: A review article. Renew Sustain Energy Rev 2016;59:895–906. [CrossRef]
[11] Taleghani M, Tenpierik M, Kurvers S, van den Dobbelsteen A. A review into thermal comfort in buildings. Renew Sustain Energy Rev 2013;26:201–215. [CrossRef]
[12] Khaleel A, Ahmed A, Dakkama H, Al-Shohani̇ W. Effect of exhaust layout on the indoor thermal comfort under harsh weather conditions. J Therm Eng 2020;7:148–160. [CrossRef]
[13] Alam MS, Salve UR. Factors affecting on human thermal comfort inside the kitchen area of railway pantry car - a review. J Therm Eng 2021;14:2093–2106. [CrossRef]
[14] Enescu D. A review of thermal comfort models and indicators for indoor environments. Renew Sustain Energy Rev 2017;79:1353–1379. [CrossRef]
[15] Auffenberg F, Stein S, Rogers A. A personalised thermal comfort model using a Bayesian network. In Twenty-Fourth IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence, 2015
[16] Aoki S, Mukai E, Tsuji H, Inoue S, Mimura E. Bayesian networks for thermal comfort analysis. IEEE Int. Conf. on Systems, Man and Cybernetics 2007, p. 19191923.
[17] Ghahramani A, Tang C, Yang Z, Becerik-Gerber B. A study of time-dependent variations in personal thermal comfort via a dynamic Bayesian network. In: Conference: First International Symposium on Sustainable Human-Building Ecosystems, 2015, p. 99-107. [CrossRef]
[18] Jensen KL, Toftum J, Friis-Hansen P. A Bayesian Network approach to the evaluation of building design and its consequences for employee performance and operational costs. Build Environ 2009;44:3:456–462. [CrossRef]
[19] Li M, Wang H, Wang D, Shao Z, He S. Risk assessment of gas explosion in coal mines based on fuzzy AHP and bayesian network. Process Saf Environ Prot 2020;135:207–218. [CrossRef]
[20] Zhou J, Asteris PG, Armaghani DJ, Pham BT. 2020. Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dyn Earthq Eng 2020;139:106390. [CrossRef]
[21] Sun S, Zhang C, Yu G. A Bayesian network approach to traffic flow forecasting. IEEE Trans Intell Transp Syst 2006;7:1:124–132. [CrossRef]
[22] Yucesan M, Gul M, Guneri AF. A Bayesian network-based approach for failure analysis in weapon industry. J Therm Eng 2021;7:2:222229. [CrossRef]