A new method for calculating the thermal effects of irregular internal mass in buildings under demand response

Abstract Accurately modeling thermal storage and discharge play a pivotal role in predicting the peak savings of demand response buildings. Considerable studies have been conducted on the transient thermal behavior of building envelopes, much of which has focused on the thermal mass effects of building envelopes and floors. However, it is unclear how to precisely describe the cooling storage effects of irregular internal mass such as furniture. EnergyPlus and other simulation tools have internal mass models, but these models require ambiguous inputs such as internal mass surface area, thickness, volume and thermal properties. These inputs are impossible to obtain due to the irregular shapes and random spatial distributions of internal mass. In this paper, the novel “Effective Area” method is proposed that improves the theory of the conventional “Equivalent Slab” method. The new method establishes a relationship between the actual furniture and the equivalent furniture through a converted coefficient in the dynamic heat transfer equations. Experiments are conducted to test and verify the accuracy of the new method and to calculate common parameters, such as the converted coefficient and the distribution density of the irregular internal mass in some typical office setups.

[1]  Tim Weber,et al.  An optimized RC-network for thermally activated building components , 2005 .

[2]  Yaolin Lin,et al.  Coupling of thermal mass and natural ventilation in buildings , 2008 .

[3]  J. Douglas Balcomb,et al.  HEAT STORAGE AND DISTRIBUTION INSIDE PASSIVE SOLAR BUILDINGS , 1983 .

[4]  K. A. Antonopoulos,et al.  Effect of indoor mass on the time constant and thermal delay of buildings , 2000 .

[5]  Zuohuan Zheng,et al.  Nonlinear coupling between thermal mass and natural ventilation in buildings , 2003 .

[6]  Yaolin Lin,et al.  A new virtual sphere method for estimating the role of thermal mass in natural ventilated buildings , 2011 .

[7]  Constantinos A. Balaras,et al.  The role of thermal mass on the cooling load of buildings. An overview of computational methods , 1996 .

[8]  Lina Yang,et al.  Cooling load reduction by using thermal mass and night ventilation , 2008 .

[9]  K. A. Antonopoulos,et al.  Finite‐difference prediction of transient indoor temperature and related correlation based on the building time constant , 1996 .

[10]  R. R. Crawford,et al.  A method for deriving a dynamic system model from actual building performance data , 1985 .

[11]  Peng Xu,et al.  Demand Shifting with Thermal Mass in Large Commercial Buildings in a California Hot Climate Zone , 2010 .

[12]  Shengwei Wang,et al.  A fast chiller power demand response control strategy for buildings connected to smart grid , 2015 .

[13]  Luisa F. Cabeza,et al.  Heating and cooling energy trends and drivers in buildings , 2015 .

[14]  John E. Seem Modeling of Heat Transfer in Buildings , 1987 .

[15]  J. Braun,et al.  Model-based demand-limiting control of building thermal mass , 2008 .

[16]  Pengcheng Xu,et al.  Thermal Mass Design in Buildings – Heavy or Light? , 2006 .

[17]  Simeng Liu,et al.  Experimental Analysis of Model-Based Predictive Optimal Control for Active and Passive Building Thermal Storage Inventory , 2005 .

[18]  K. A. Antonopoulos,et al.  Apparent and effective thermal capacitance of buildings , 1998 .

[19]  James E. Braun,et al.  An Inverse Gray-Box Model for Transient Building Load Prediction , 2002 .

[20]  R. L. Earle CHAPTER 5 – HEAT-TRANSFER THEORY , 1983 .

[21]  Morris Grenfell Davies,et al.  WALL TRANSIENT HEAT FLOW USING TIME-DOMAIN ANALYSIS , 1997 .

[22]  Peng Xu,et al.  Demand reduction in building energy systems based on economic model predictive control , 2012 .

[23]  Da Yan,et al.  DeST — An integrated building simulation toolkit Part I: Fundamentals , 2008 .