Development and assessment of simplified building representations under the context of an urban energy model: Application to arid climate environment

Abstract For buildings in arid areas, requiring significant cooling loads, predictions of long term building cooling loads and the development of accurate Building Energy Models (BEM's) are of critical importance. However, numerous challenges and limitations are encountered in the course of generating such energy models. Furthermore, recent studies have emphasized the integration of the urban heat island phenomena in modeling building energy behavior in urban environments. This study assesses the loss of accuracy versus gain in computational effort incurred by the use of different types of simplified building representations relative to a more detailed one when applied in a building case study. The study shows that such simplification results in limited loss of accuracy, when compared to a detailed model. The RC-based simplified model reported a satisfying level of performance and was thus used to simulate the building cooling load of the case in an urban context. Furthermore, results showed an anticipated increase of cooling load demonstrating the practicality of the developed simplified model to be used in this context.

[1]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[2]  Fabio Bisegna,et al.  Climate models for the assessment of office buildings energy performance , 2004 .

[3]  Paul Raftery,et al.  Calibrating whole building energy models: Detailed case study using hourly measured data , 2011 .

[4]  Leslie K. Norford,et al.  Estimation of urban temperature and humidity using a lumped parameter model coupled with an EnergyPlus model , 2015 .

[5]  Emily M. Ryan,et al.  Validation of building energy modeling tools under idealized and realistic conditions , 2012 .

[6]  R. Britter,et al.  A resistance-capacitance network model for the analysis of the interactions between the energy performance of buildings and the urban climate , 2012 .

[7]  Paul Raftery,et al.  Calibrating whole building energy models: An evidence-based methodology , 2011 .

[8]  Tao Lu,et al.  Calibrating numerical model by neural networks: A case study for the simulation of the indoor temperature of a building , 2015 .

[9]  Ardeshir Mahdavi,et al.  A CASE STUDY OF OPTIMIZATION-AIDED THERMAL BUILDING PERFORMANCE SIMULATION CALIBRATION , 2013 .

[10]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[11]  Prabir Barooah,et al.  A method for model-reduction of non-linear thermal dynamics of multi-zone buildings , 2012 .

[12]  Abdelaziz Laouadi.  Development of a radiant heating and cooling model for building energy simulation software , 2004 .

[13]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

[14]  Rp Rick Kramer,et al.  Simplified thermal and hygric building models： A literature review , 2012 .

[15]  Ardeshir Mahdavi,et al.  An optimization-based approach to recurrent calibration of building performance simulation models , 2012 .

[16]  Y. Charabi,et al.  Assessment of the canopy urban heat island of a coastal arid tropical city: The case of Muscat, Oman , 2011 .

[17]  Valéry Masson,et al.  A Physically-Based Scheme For The Urban Energy Budget In Atmospheric Models , 2000 .

[18]  Gongsheng Huang,et al.  Re-evaluation of building cooling load prediction models for use in humid subtropical area , 2013 .