Toward integrated building design: A parametric method for evaluating heating demand

Abstract In this study, we describe a novel method for evaluating building heating demand based on a statistical approach. Our aim is to support integrated building design by providing fast modeling with accuracy close to that of dynamic simulations. A general parametric model encompassing overall building design is proposed based on the analysis of heat transfer. The method is subsequently applied to evaluate the heating demand of a single-family house in a cold French climate. Several polynomial functions are derived from the general model as functions of the amounts of heat transferred by different mechanisms and the physical and geometric building parameters. The model is identified with a small number of dynamic simulations using the design of experiments. The model illustrates how the weighting factors for the various amounts of heat are much higher in cold climates than in hot ones. We demonstrate that building heating demand can be accurately analyzed using the design parameters of the developed model. This analysis highlights the potential of this approach for supporting building energy designers in the choice of energy-efficient solutions.

[1]  Christian Inard,et al.  Fast method to predict building heating demand based on the design of experiments , 2009 .

[2]  Mohammad S. Al-Homoud,et al.  Computer-aided building energy analysis techniques , 2001 .

[3]  Aya Hagishima,et al.  An approach for coupled simulation of building thermal effects and urban climatology , 2004 .

[4]  Viktor Dorer,et al.  Re-inventing air heating: Convenient and comfortable within the frame of the Passive House concept , 2005 .

[5]  Han-Hsi Liang,et al.  Additive model for thermal comfort generated by matrix experiment using orthogonal array , 2009 .

[6]  T. Catalina.  Estimation of residential buildings energy consumptions and analysis of renewable energy systems using a multi-criteria decision methodology , 2009 .

[7]  Christian Inard,et al.  A new methodology for the design of low energy buildings , 2009 .

[8]  P.J.C.J. De Wilde,et al.  Computational Support for the Selection of Energy Saving Building Components , 2004 .

[9]  Arno Schlueter,et al.  Building information model based energy/exergy performance assessment in early design stages , 2009 .

[10]  Satish V. Ukkusuri,et al.  Optimizing the design of a solar cooling system using central composite design techniques , 2011 .

[11]  Christian Ghiaus,et al.  Optimal settings of residential oil burners , 2002 .

[12]  Herbert A. Simon,et al.  The new science of management decision , 1960 .

[13]  Cristian Ghiaus.  Equivalence between the load curve and the free-running temperature in energy estimating methods , 2006 .

[14]  Jiju Antony,et al.  Design of experiments for engineers and scientists , 2003 .

[15]  André De Herde,et al.  A simple design tool for the thermal study of an office building , 2002 .

[16]  Sandro Macchietto,et al.  Model-based design of experiments for parameter precision: State of the art , 2008 .