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OCCUPANT BEHAVIOR AND THERMAL COMFORT FIELD ANALYSIS IN TYPICAL EDUCATIONAL RESEARCH INSTITUTION: A CASE STUDY

ABSTRACT
An experimental field study has been conducted for typical educational research building facility (office building). The research data was gathered by the systematic monitoring of the offices and adaptive occupant behavior during the typical working day in the spring period. Different sensors and data loggers for temperature, relative humidity, CO2 concentration, had been mounted in order to collect data for analysis of thermal comfort conditions. Moreover, occupant surveys and interviews in form of questionnaire were also brought to examine the psychological and social impacts of the occupants’ behavior regarding energy consumption. The inductive scientific method is used for data processing, i. e. descriptive and inferential statistical analysis of the results was made. Based on the analysis of the conducted study, it was found that thermal environment of the observed building is within the standards (i. e. specific parameters are within the range) and that the occupants are generally satisfied with thermal conditions in their offices. However, they do not pay much attention to conserving energy which is an important finding as it is directly related to the energy consumption. Thus, more attention should be directed to the education of the users and in general, to enable energy savings in the future.
KEYWORDS
PAPER SUBMITTED: 2017-09-15
PAPER REVISED: 2017-11-27
PAPER ACCEPTED: 2017-12-20
PUBLISHED ONLINE: 2018-02-18
DOI REFERENCE: https://doi.org/10.2298/TSCI170915013P
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2018, VOLUME 22, ISSUE Supplement 3, PAGES [S785 - S795]
REFERENCES
  1. Robinson, D., Some trends and research needs in energy and comfort prediction, Proceedings of the conference on comfort and energy use in buildings — getting them right, 2006
  2. Bianco, V., Scarpa, F., Tagliafico, L. A., Estimation of primary energy savings by using heat pumps for heating purposes in the residential sector, Appl. Therm. Eng., 114 (2017), 5, pp. 938-947
  3. Nižetić, S., Duić, N., Papadopoulos, A. M., Tina, G. M., Grubišić-Čabo, F., Energy efficiency evaluation of a hybrid energy system for building applications in a Mediterranean climate and its feasibility aspect, Energy, 90 (2015), pp. 1171-1179
  4. Nižetić, S., Papadopoulos, A. M., Tina, G. M., Rosa-Clot, M., Hybrid energy scenarios for residential applications based on the heat pump split air-conditioning units for operation in the Mediterranean climate conditions, Energy Build. 140 (2017), pp. 110-120
  5. Zafar, S., Dincer, I., Thermodynamic analysis of a combined PV/T-fuel cell system for power, heat, fresh water and hydrogen production, Int. J. Hydrogen Energy 39 (2014), 19, pp. 9962-9972
  6. Hong, T., Yan, D., D'Oca, S., Chen, C., Ten questions concerning occupant behavior in buildings: The big picture, Build. Environ.114 (2017), pp. 518-530
  7. Masoso O. T., Grobler, L. J., The dark side of occupants' behaviour on building energy use, Energy Build. 42 (2010), pp. 173-177
  8. Kosonen, R., Tan, F., Assessment of productivity loss in air-conditioned buildings using PMV index, Energy Build. 36 (2004), 10 spec. iss., pp. 987-993
  9. Ye, G., Yang, C., Chen, Y., Li, Y., A new approach for measuring predicted mean vote (PMV) and standard effective temperature (SET), Build. Environ. 38 (2003), 1, pp. 33-44
  10. Kim, J. T., Lim, J. H., Cho, S. H., Yun, G. Y., Development of the adaptive PMV model for improving prediction performances, Energy Build. 98 (2015), pp. 100-105
  11. De Dear, R., Brager, G., Cooper, D., Developing an adaptive model of thermal comfort and preference, ASHRAE Transactions 104 (1998), Part 1., pp. 1-312
  12. De Dear, R., Thermal comfort in practice, Indoor Air 14 (2004), suppl. 7, pp. 32-39
  13. Yao, R., Li, B., Liu, J., A theoretical adaptive model of thermal comfort - Adaptive Predicted Mean Vote (aPMV), Build. Environ. 44 (2009), 10, pp. 2089-2096
  14. Nicol, J. F., Humphreys, M. A., Adaptive thermal comfort and sustainable thermal standards for buildings, Energy Build. 34 (2002), 6, pp. 563-572
  15. Brager, G. S., De Dear, R. J., Thermal adaptation in the built environment : a literature review, Energy Build. 27 (1998), pp. 83-96
  16. Ye, X. J., Zhou, Z. P., Lian, Z. W., Liu, H. M., Li, C. Z., Liu, Y. M., Field study of a thermal environment and adaptive model in Shanghai, Indoor Air 16 (2006), 4, pp. 320-326
  17. Wagner, A., Gossauer, E., Moosmann, C., Gropp, T., Leonhart, R., Thermal comfort and workplace occupant satisfaction-Results of field studies in German low energy office buildings, Energy Build. 39 (2007), 7, pp. 758-769
  18. Lenzuni, P., Freda, D., Del Gaudio, M., Classification of thermal environments for comfort assessment, Ann. Occup. Hyg. 53 (2009), 4, pp. 325-332
  19. Karjalainen, S., Gender differences in thermal comfort and use of thermostats in everyday thermal environments, Build. Environ. 42 (2007), 4, pp. 1594-1603
  20. E Instruments International LLC, Multifunctional IAQ Monitor AMI 300 Manual, France (2008.)
  21. Yun G. Y., Steemers, K., Time-dependent occupant behaviour models of window control in summer, Build. Environ. 43 (2008), pp. 1471-1482
  22. Hong, T., Sun, H., Chen, Y., Taylor-Lange, S. C., Yan, D., An occupant behavior modeling tool for co-simulation, Energy Build. 117 (2016), pp. 272-281

© 2018 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence