Two-stage regression model of thermal comfort in office buildings

Building and Environment

Volumn 57, Issue , 2012, Pages 88-96

  Wu, Siyu ^a   Sun, Jian Qiao ^a  

^a University of California   (United States)

Author keywords

HVAC system; Multi stage regression; PMV; Thermal comfort

Indexed keywords

A-COEFFICIENT; ARCHITECTURAL PARAMETERS; CONTROL DESIGN; CONTROL VARIABLE; HVAC SYSTEM; MULTI-STAGE; PMV; PREDICTED MEAN VOTE; REGRESSION MODEL; ROOT MEAN SQUARED ERRORS;

OFFICE BUILDINGS; REGRESSION ANALYSIS; THERMAL COMFORT;

AIR CONDITIONING;

ACCURACY ASSESSMENT; AIR QUALITY; BUILDING; CONTROL SYSTEM; INDOOR AIR; MULTIPLE REGRESSION; NUMERICAL MODEL; VENTILATION;

PAPAYA MOSAIC VIRUS;

EID: 84861220321     PISSN: 03601323     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.buildenv.2012.04.014     Document Type: Article

References (43)

1. Freire R.Z., Oliveira G.H.C., Mendes N. Predictive controllers for thermal comfort optimization and energy savings. Energ Buildings 2008, 40(7):1353-1365.
2. Buildings and their impact on the environment: a statistical summary. http://www.epa.gov/greenbuilding/pubs/gbstats.pdf.
3. Ye G., Yang C., Chen Y., Li Y. A new approach for measuring predicted mean vote (PMV) and standard effective temperature (SET*). Building Environ 2003, 38(1):33-44.
4. Atthajariyakul S., Leephakpreeda T. Real-time determination of optimal indoor-air condition for thermal comfort, air quality and efficient energy usage. Energ Buildings 2004, 36(7):720-733.
5. Liang J., Du R. Design of intelligent comfort control system with human learning and minimum power control strategies. Energy Conversion Manag 2008, 49(4):517-528.
6. Fanger P.O. Thermal comfort 1970, Danish Technical Press, Copenhagen.
7. Fanger P.O., Melikov A.K., Hanzawa H., Ring J. Air turbulence and sensation of draught. Energ Buildings 1988, 12(1):21-39.
8. Max S. A simplified model of thermal comfort. Energ Buildings 1985, 8(1):37-50.
9. Atthajariyakul S., Leephakpreeda T. Neural computing thermal comfort index for HVAC systems. Energy Conversion Manag 2005, 46(15, 16):2553-2565.
10. Djongyang N., Tchinda R., Njomo D. Thermal comfort: a review paper. Renewable Sustainable Energy Rev 2010, 14(9):2626-2640.
11. Fanger P.O., Toftum J. Extension of the PMV model to non-air-conditioned buildings in warm climates. Energ Buildings 2002, 34(6):533-536.
12. Kulkarni M.R., Hong F. An experimental technique for thermal comfort comparison in a transient pull down. Building Environ 2004, 39(2):189-193.
13. Brager G.S., de Dear R.J. Thermal adaptation in the built environment: a literature review. Energ Buildings 1998, 27(1):83-96.
14. Homod R.Z., Sahari K.S.M., Almurib H.A.F., Nagi F.H. RLF and TS fuzzy model identification of indoor thermal comfort based on PMV/PPD. Building Environ 2012, 49:141-153.
15. Hamdi M., Lachiver G., Michaud F. A new predictive thermal sensation index of human response. Energ Buildings 1999, 29(2):167-178.
16. Fundamentals: ASHRAE Handbook American society of heating refrigerating and air conditioning engineers 1993, Georgia, Atlanta.
17. Chen K., Jiao Y., Lee E.S. Fuzzy adaptive networks in thermal comfort. Appl Math Lett 2006, 19(5):420-426.
18. Hwang R.L., Shu S.Y. Building envelope regulations on thermal comfort in glass facade buildings and energy-saving potential for PMV-based comfort control. Building Environ 2011, 46(4):824-834.
19. Wang L., Nyuk H.W., Li S. Facade design optimization for naturally ventilated residential buildings in Singapore. Energ Buildings 2007, 39(8):954-961.
20. Stavrakakis G.M., Zervas P.L., Sarimveis H., Markatos N.C. Development of a computational tool to quantify architectural-design effects on tthermal comfort in naturally ventilated rural houses. Building Environ 2010, 45(1):65-80.
21. Stavrakakis G.M., Karadimou D.P., Zervas P.L., Sarimveis H., Markatos N.C. Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks. Building Environ 2011, 46(2):298-314.
22. Stavrakakis G.M., Zervas P.L., Sarimveis H., Markatos N.C. Optimization of window-openings design for thermal comfort in naturally ventilated buildings. Appl Math Model 2012, 36(1):193-211.
23. Yu J., Yang C., Tian L., Liao D. Evaluation on energy and thermal performance for residential envelopes in hot summer and cold winter zone of China. Appl Energy 2009, 86(10):1970-1985.
24. Ghaddar N., Ghali K., Chehaitly S. Assessing thermal comfort of active people in transitional spaces in presence of air movement. Energ Buildings 2011, 43(10):2832-2842.
25. Prianto E., Depecker P. Optimization of architectural design elements in tropical humid region with thermal comfort approach. Energ Buildings 2003, 35(3):273-280.
26. Ho S.H., Rosario L., Rahman M.M. Thermal comfort enhancement by using a ceiling fan. Appl Thermal Eng 2009, 29(8-9):1648-1656.
27. Wu S., Sun J.Q. A physics-based linear parametric model of room temperature in office buildings. Building Environ 2012, 50:1-9.
28. Mazzei P., Minichiello F., Palma D. HVAC dehumidification systems for thermal comfort: a critical review. Appl Thermal Eng 2005, 25(5-6):677-707.
29. Fundamentals: ASHRAE Handbook American Society of heating Refrigerating and air conditioning Engineers 1997, Georgia, Atlanta.
30. Hyland W., Wexler A. Formulations for the thermodynamic properties of the saturated phases of H2O from 173.15 K to 473.15 K. Proc ASHRAE Trans 1983, vol. 89:500-519.
31. Hyland W., Wexler A. Formulations for the thermodynamic properties of dry air from 173.15 K to 473.15 K, and of saturated moist air from 173.15 K to 372.15 K. Proc ASHRAE Trans 1983, vol. 89:520-535.
32. Sonntag D. Important new values of the physical constants of 1986, vapour pressure formulations based on the ITS-90, and psychrometer formulae. Z Meteorol 1990, 70(5):340-344.
33. Baierlein R. Thermal physics 2003, Cambridge University Press, Cambridge, England.
34. Perrot P. A to Z of thermodynamics 1998, Oxford University Press, Oxford, England.
35. Baguley T. Beyond ANOVA: from repeated measures to multilevel models 2008, Proceedings of PsyPAG Mathematics Statistics and Computing Workshop, Nottingham Trent University, Nottingham.
36. Ahn J. Beyond single equation regression analysis: path analysis and multi-stage regression analysis. Am J Pharm Educ 2002, 66:37-42.
37. Kristjansson S.D., Kircher J.C., Webb A.K. Multilevel models for repeated measures research designs in psychophysiology: an introduction to growth curve modeling. Psychophysiology 2007, 44(5):728-736.
38. Bland J.M., Altman D.G. Statistics notes: correlations, regression, and repeated data. Br Med J 1994, 308:896.
39. Mardia K.V., Kent J.T., Bibby J.M. Multivariate analysis (probability and mathematical statistics) 1980, Academic Press, Waltham, Massachusetts.
40. Maxwell S.E., Delaney H.D. Designing experiments and analyzing data: a model comparison perspective 2003, Routledge Academic, London.
41. Bishop C.M. Neural networks for pattern recognition 1995, Clarendon Press, Oxford.
42. Schunn C.D., Wallach D. Evaluating goodness-of-fit in comparison of models to data, in. Psychologie der Kognition: reden and vorträge anlässlich der emeritierung von Werner Tack 2005, 115-154. University of Saarland Press, Saarbrueken, Germany. W. Tack (Ed.).
43. Wu S., Sun J.Q. Cross-level fault detection and diagnosis of building HVAC systems. Building Environ 2011, 46(8):1558-1566.