Genetic optimization of external fixed shading devices

Abstract In the present paper a genetic optimization (GO) has been carried out on an office room with a south facing window in order to design an optimal fixed shading device. Two different glazing systems have been taken into account, one standard double glass and an high performance glazing system specifically designed to prevent high sun loads. The shading device is a flat panel positioned parallel to the window and inclined by its horizontal axis. The device shades the window from direct sun penetration reducing the cooling loads in summer, but also affecting daylight and heat loads in winter limiting the sun gains, therefore the impact on the overall building energy consumption is investigated. A genetic optimization has been performed for identifying a possible geometry with the lower energy impact. Lighting loads, computed by the DAYSIM code, have been considered as inputs for the code ESP-r which drives the energy computation. The results demonstrate that electrical energy absorbed by the lighting system has to be always taken into account in designing energy efficient shading devices.

[1]  Gregory M. Maxwell,et al.  An empirical validation of modelling solar gain through a glazing unit with external and internal shading screens , 2007 .

[2]  Evangelos Grigoroudis,et al.  Towards a multi-objective optimization approach for improving energy efficiency in buildings , 2008 .

[3]  Christoph F. Reinhart,et al.  Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds , 2001 .

[4]  Danny H.W. Li,et al.  A study of the daylighting performance and energy use in heavily obstructed residential buildings via computer simulation techniques , 2006 .

[5]  Joseph Andrew Clarke,et al.  Energy Simulation in Building Design , 1985 .

[6]  Jonathan A. Wright,et al.  A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization , 2004 .

[7]  Ismael R. Maestre,et al.  A single-thin-film model for the angle dependent optical properties of coated glazings , 2007 .

[8]  Christoph F. Reinhart,et al.  Lightswitch-2002: a model for manual and automated control of electric lighting and blinds , 2004 .

[9]  Sanja Stevanović,et al.  Optimization of passive solar design strategies: A review , 2013 .

[10]  A. Laouadi,et al.  Optical models of complex fenestration systems , 2007 .

[11]  Michael Wetter,et al.  Building design optimization using a convergent pattern search algorithm with adaptive precision simulations , 2005 .

[12]  Athanasios Tzempelikos,et al.  Sensitivity analysis on daylighting and energy performance of perimeter offices with automated shading , 2013 .

[13]  Giorgio Baldinelli,et al.  Theoretical modelling and experimental evaluation of the optical properties of glazing systems with selective films , 2009 .

[14]  Sandra Mende,et al.  CLIMATE BASED SIMULATION OF DIFFERENT SHADING DEVICE SYSTEMS FOR COMFORT AND ENERGY DEMAND , 2011 .

[15]  Essia Znouda,et al.  Optimization of Mediterranean building design using genetic algorithms , 2007 .

[16]  Gilles Fraisse,et al.  Influence of the coupling between daylight and artificial lighting on thermal loads in office buildings , 2004 .

[17]  Fabio Bisegna,et al.  Daylighting with external shading devices: design and simulation algorithms , 2006 .

[18]  Chia-Yen Lee,et al.  Optimal sun-shading design for enhanced daylight illumination of subtropical classrooms , 2008 .

[19]  Singiresu S. Rao Engineering Optimization : Theory and Practice , 2010 .

[20]  Rodrigo Escobar,et al.  Thermal and lighting behavior of office buildings in Santiago of Chile , 2012 .

[21]  John Mardaljevic,et al.  Useful daylight illuminances: A replacement for daylight factors , 2006 .