Influence of Three Dynamic Predictive Clothing Insulation Models on Building Energy Use, HVAC Sizing and Thermal Comfort

In building energy simulation, indoor thermal comfort condition, energy use and equipment size are typically calculated based on the assumption that the clothing insulation is equal to a constant value of 0.5 clo during the cooling season and 1.0 clo during the heating season. The assumption is not reflected in practice and thus it may lead to errors. In reality, occupants frequently adjust their clothing depending on the thermal conditions, as opposed to the assumption of constant clothing values above, indicating that the clothing insulation variation should be captured in building simulation software to obtain more reliable and accurate results. In this study, the impact of three newly developed dynamic clothing insulation models on the building simulation is quantitatively assessed using the detailed whole-building energy simulation program, EnergyPlus version 6.0. The results showed that when the heating ventilation and air conditioning system (HVAC) is controlled based on indoor temperature the dynamic clothing models do not affect indoor operative temperatures, energy consumption and equipment sizing. When the HVAC is controlled based on the PMV model the use of a fixed clothing insulation during the cooling (0.5 clo) and heating (1.0 clo) season leads to the incorrect estimation of the indoor operative temperatures, energy consumption and equipment sizing. The dynamic clothing models significantly ( p < 0.0001) improve the ability of energy simulation tools to assess thermal comfort. The authors recommend that the dynamic clothing models should be implemented in dynamic building energy simulation software such as EnergyPlus.

[1]  R. Andersen,et al.  Occupant performance and building energy consumption with different philosophies of determining acceptable thermal conditions , 2009 .

[2]  Stephanie Gauthier,et al.  Predictive thermal comfort model: Are current field studies measuring the most influential variables? , 2012 .

[3]  Jørn Toftum,et al.  A Bayesian Network approach to the evaluation of building design and its consequences for employee performance and operational costs , 2009 .

[4]  Bjarne W. Olesen,et al.  People's clothing behaviour according to external weather and indoor environment , 2007 .

[5]  Anne Marsden,et al.  International Organization for Standardization , 2014 .

[6]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[7]  K. Cena,et al.  Field study of occupant comfort and office thermal environments in a hot, arid climate , 1999 .

[8]  Guy R. Newsham,et al.  Clothing as a thermal comfort moderator and the effect on energy consumption , 1997 .

[9]  P. Fanger.  Moderate Thermal Environments Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort , 1984 .

[10]  R. de Dear,et al.  The adaptive model of thermal comfort and energy conservation in the built environment , 2001, International journal of biometeorology.

[11]  Vice President,et al.  AMERICAN SOCIETY OF HEATING, REFRIGERATION AND AIR CONDITIONING ENGINEERS INC. , 2007 .

[12]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[13]  Gail Brager,et al.  Developing an adaptive model of thermal comfort and preference , 1998 .

[14]  Ingvar Holmér,et al.  Personal factors in thermal comfort assessment: clothing properties and metabolic heat production , 2002 .

[15]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[16]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[17]  Stefano Schiavon,et al.  Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures , 2013 .

[18]  Richard de Dear,et al.  Weather, clothing and thermal adaptation to indoor climate , 2003 .