Development of the adaptive PMV model for improving prediction performances

Abstract Predicted mean vote (PMV) is widely used in order to evaluate thermal comfort conditions of indoor spaces. However, recent studies show that there are discrepancies between PMV prediction and the actual mean vote (AMV) of occupants in buildings. This study has an aim to develop two types of adaptive PMV models considering physical stimulus on the human's thermal sensation and adaptive behavior. A field measurement was conducted to reveal occupant perception on and responses to indoor thermal environments and to assess the applicability of the original PMV model. We have developed two types of Adaptive PMV models based on the black-box theory and the adaptive thermal comfort theory in air-conditioned buildings. The adaptive PMV (aPMV) model based on the black-box theory shows good prediction performance only when the original PMV value ranges from −1.5 to +1.5. On the other hand, the prediction results from New PMV (nPMV) based on the adaptive comfort theory are in good agreement with actual thermal sensation of people and are reliable. Our results imply that the nPMV model could increase the prediction performance of the original PMV model and play an important role in reducing the cooling energy of air-conditioned buildings.

[1]  R. Dedear.  Developing an adaptive model of thermal comfort and preference , 1998 .

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

[3]  J. F. Nicol,et al.  Understanding the adaptive approach to thermal comfort , 1998 .

[4]  Taeyon Hwang,et al.  Performance Prediction by Modelling of a Light-pipe System used under the Climate Conditions of Korea , 2010 .

[5]  Jeong Tai Kim,et al.  The Effect of Seasons and Prevailing Environments on Adaptive Comfort Temperatures in Open Plan Offices , 2012 .

[6]  R. Dear,et al.  Thermal adaptation in the built environment: a literature review , 1998 .

[7]  Jeong Tai Kim,et al.  Energy performance of direct expansion air handling unit in office buildings , 2014 .

[8]  Manoj Kumar Singh,et al.  Adaptive thermal comfort model for different climatic zones of North-East India , 2011 .

[9]  Gail Brager,et al.  The adaptive model of thermal comfort and energy conservation in the built environment. , 2001 .

[10]  R. Yao,et al.  A theoretical adaptive model of thermal comfort – Adaptive Predicted Mean Vote (aPMV) , 2009 .

[11]  C. Bouden,et al.  An adaptive thermal comfort model for the Tunisian context: a field study results , 2005 .

[12]  Astrid Roetzel,et al.  Comparison of the EN 15251 and Ashrae Standard 55 adaptive thermal comfort models in the context of a Mediterranean climate , 2011 .

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

[14]  Jeong Tai Kim,et al.  A field survey of visual comfort and lighting energy consumption in open plan offices , 2012 .

[15]  F. Nicol,et al.  Derivation of the adaptive equations for thermal comfort in free-running buildings in European standard EN15251 , 2010 .

[16]  Jeong Tai Kim,et al.  A Field Survey of Thermal Comfort in Office Building with Thermal Environment Standard , 2011 .

[17]  Oar,et al.  An Office Building Occupants Guide to Indoor Air Quality - Printable Version , 2014 .

[18]  J. F. Nicol,et al.  The validity of ISO-PMV for predicting comfort votes in every-day thermal environments , 2002 .

[19]  노지웅.  공동주택 지하주차장의 개구유형에 따른 풍력환기 성능에 관한 연구 , 2011 .

[20]  Baizhan Li,et al.  The Chinese Evaluation Standard for the Indoor Thermal Environment in Free-running Buildings , 2012 .