Design Optimization of Glazing Façade by
Using the GPSPSOCCHJ Algorithm
Mona Khatami1,M.Sc. Maria Kordjamshidi2, PhD Be hrouz Mohammad Kari3, PhD
[University of Ilam] [University of Ilam] [University of Tehran] [email protected] [email protected] [email protected] Alire za Zolfaghari4, PhD
[University of Birjand] [email protected]
ABSTRACT
Engineering design is a process to find the best solution to satisfy various design criteria. This work aim s to optim ize the glazing façade performance and the window size by m inimizing the heating, cooling and electric lighting demand of office buildings. Accordingly, this paper presents a comprehensive analysis in order to study the balance between daylighting benefits and energy requirements in perimeter office spaces taking into account glazing properties control with window size, orientation and clim atic conditions. The glazing area and therm ophysical properties of the window were taken as the main variables. The optimization was carried out by using a combination of Energy plus7.0.0 and GenOpt softwares. The energy consum ption can significantly change affected by geometric param eters, m aterials properties and types of window glass, orientation and clim atic conditions. Optimum range of each param eter was calculated in order to m inim ize annual energy consumption with a hybrid multidimensional optimization algorithm: GPSPSOCCHJ algorithm . Furtherm ore, since the annual energy consumption effectively depends on the type of air conditioning system, the optim ization process was carried out individually with both evaporative cooling system and compression cooling system . The results indicated that using the evaporative cooling system compared is m ore appropriate and econom ical in com parison with the compression cooling system. Also, investigations indicated that reflective double glass and low-e double glazed with argon layer glass is appropriate for Tehran office building and can respectivelyallocate the m axim um level of window area and the m inim um of energy consum ption.
Keywords: Glazing façade, Optim ization, GPSPSOCCHJ optim ization algorithm , Energy consum ption
INTRUDUCTION
Window is considered as one of the most important components influencing the thermal performance of buildings. T heir shape, size, optical and thermal properties, orientation and shading/daylighting attachments determine the interior daylighting conditions as well as the visual and thermal comfort for the occupants. T he balance between daylight provision and reduction in energy consumption or demand through appropriate control of solar has been investigated in a few studies by several researchers (Lee et al., 1995; Citherlet et al., 2001; Franzetti et al., 2004; Hviid et al., 2008; Tzempelikos et al., 2010). Coupling between daylighting and thermal simulation is necessary for a comprehensive an alysis. In 1998, Clarke et al. compared the annual energy consumption of three different types of glazing system using ESP -r and found reductions of about 4.5%, 10.9% and 6% in maximum heating capacity, maximum cooling capacity and total energy consumption respectively.
Optimized glass facade design may improve exploitation of daylight and result in significant savings in electricity consumption for lighting. Reinhart (2002) calculated the daylight availability for several Canadian locations considering the effects of climate, external shading, facade orientation, glazing type and occupancy schedules. T he study showed that location, orientation and blind slat angle all have a significan t impact on daylight autonomy while external objects and glazing type were less important. Optimized glass facade design may improve exploitation of daylight and result in significant savings in electricity consumption for lighting. Reinhart (2002) calculated the daylight availability for several Canadian locations considering the effects of climate, external shading, facade orientation, glazing type and occupancy schedules. T he study showed that location, orientation and blind slat angle all have a significant impact on daylight autonomy while external objects and glazing type were less important.
T he sophisticated characterizations of window and shading systems sparked a large amount of studies on this topic (Reinhart and Walkenhorst, 2001; Walkenhorst et al., 2002; Robinson and Stone, 2006; Loutzenhiser et al., 2007), and various calculation models that predict illuminance on the interior surfaces of a building as well as on the work plane level are available (Mardaljevic, 2001; Fakra et al., 2011). T he different models have some limitations; for example, some models use constant glass transmittance, some others use limited evaluation metrics such as daylight factors (Ghisi and Tinker, 2005); and some have limitation in sky luminance inputs. Moreover, it is complicated to modify existing software codes to adapt specific necessities or to present results using different measures. As to the latter, advanced daylighting metrics may be properly used in daylight performance evaluation (Nabil and Mardaljevic, 200 6; Reinhart et al., 2006). Finally, the significant computational time, the complex calculation procedure and the inability to interpret simulation results are all factors preventing the design community from picking up the advanced design analysis schemes with very few exceptions (Reinhart and Wienold, 2011).
T his study has been tried to optimize the window size and glass type with the objective of minimization of annual energy consumption function. In such a way, while reducing energy consumption, occupants’ thermal comfort and the brightness level of each space remain in the acceptable range.For this purpose the modeling of thermal and visual performance of building’s transparent façade is performed by EnergyPlus software and the results are optimized by GenOpt software and GPSPSOCCHJ algorithm and the effect of all parameters among solar heat gain coefficient (SHGC), thermal transmittance of window (Uvalue), and visual transmittance is
considered.Finally, calculations and evaluations will lead to provide window design recommendations due to climate.
METHODS
In this paper, as shown in Figure 1, a case room is considered in accordance with the case No. 600 in ASHRAE 140 standard. Accordingly, this sample space is an office with the dimensions 6 × 8 × 2.7 m3 in the middle of a tall building which only a wall with 8m width and 2.7m height is in contact with outdoor climatic conditions of Tehran. According to Table1 the wall adjacent to the outdoor, specified by common materials for office buildings that respectively from in to out includes veneer plaster, insulation, concrete block, stucco and stone.
Table 1. The wall adjacent to the outdoor construction
field units obj1 obj2 obj3 obj4 obj5
name stone 25mm stucco concrete block 50mm insulation plaster(light) roughness medium rough Smooth medium rough medium rough medium smooth thickness m 0.03 0.0254 0.2 0.0508 0.01 conductivity W/m.K 3.17 0.72 0.33 0.03 0.16 density kg/m3 2560 1856 1380 43 600 specific heat J/kg.K 790 840 880 1210 1000
As shown in Table2, eight types of window glass have been considered: 6mm clear single glazed, clear double glazed with argon layer, clear double glazed with air layer, low-e clear single glazed, reflective clear single glazed, low-e clear double glazed with argon layer, low-e clear double glazed with air layer, reflective clear double glazed with air layer. Dimming of overhead electric lighting is determined from interior daylight illuminance calculated at one or two reference points. T wo reference points in coordinates 3 ×1.6×1 m3 and 3×6.4×1 m3 toward the wall adjacent to the outdoorare considered as lighting evaluation criterion. Also, in order to simulate the thermal and lighting energy demands, the EnergyPlus software is used.
Table 2. Type s of window glaz ing construction
Field Obj1 Obj2 Obj3 Obj4 Obj5 Obj6 Obj7 Obj8
Name 6mm clear single glazed clear double glazed with argon layer clear double glazed with air layer low-e clear single glazed reflective clear single glazed low-e clear double glazed with argon layer low-e clear double glazed with air layer reflective clear double glazed with air layer Outside layer Clear 6mm Clear 3mm Clear 3mm PYR B clear 6mm REF a clear mid 6mm PYR B clear 6mm PYR B clear 6mm REF a clear mid 6mm Layer2 Argon 13mm Air 13mm Argon 13mm Air 13mm Argon 13mm
Layer3 Clear 6mm Clear 6mm
Clear 6mm Clear 6mm Clear 6mm
EnergyPlus is one of the most comprehensive whole-building energy simulation tools that are capable of modeling several features including solar irradiance and illuminance under different sky conditions, advanced fenestration systems, blind controls, indoor illuminance maps, lamp controls, and heating/cooling energy impact associated with daylighting controls (Seo et al., 2011). Building model, location and Climatic conditions design in Software environment. EnergyPlus weatherdata file is used for energy performance calculations and indoor climate analysis. Hourly based outdoor climate data (dry-bulb air temperature, relative humidity, wind speed, direct solar radiation and diffuse radiation on horizontal surfaces for 8784 hours) was used to create the model for calculation. Comparability of current study results for other climatic areas can be done through monthly and yearly average parameters which are indicated in Table3 (Hanni.al et al., 2012).
Table 3. Re fe re nce ye ar parame te rs
Month Air temperature ˚C Relative humidity % Wind speed m/s Direct solar radiation Wh/m2 Diffuse radiation Wh/m2 Jan 2.4 63 1.7 3014 1176 Feb 4.8 55 2.5 3506 1604 Mar 10.2 44 2.9 3820 1923 Apr 16.2 36 3.3 4735 2343 May 22.3 30 3.3 5859 2396 Jun 27.5 24 3.1 7640 2319 Jul 30.9 24 2.8 7632 2032 Aug 29.5 24 2.2 7234 2049 Sep 25 25 2.3 6687 1642 Oct 18.2 33 2.1 5238 1488 Nov 11 45 1.8 3959 1169 Dec 5 59 1.5 2992 1085 Avg 16.9 38.5 2.5 5193.0 1768.8
By Considering Constraints that describe below the building annual energy consumption with a focus on providing residents thermal comfort is calculated by EnergyPlus.
1. -0.5 ≤ PMV ≤ 0.5
2. T he minimum illuminance required by international standards on the desk: 500 Lux
3. Heat generated within the space caused by a computer, printer and other accessories available: 800 W 4. Number of people: 4
5. Hours due to the discontinuous use: 7:00 to16:00 6. People with the metabolic rate of 100 W while seated
7. People Clothing thermal resistance, 0.6, 0.5, 0.7, 0.9 clo respectively for spring, summer, autumn and winter conditions.
8. Constant heating set point: 23.5˚C 9. Constant cooling set point: 26˚C
10. Internal gains for lights: Lighting level calculation method is used to create t he maximum amount of lights to this set of attribute choices : 400 W
Figure 1 Office space in the initial position study
A key part of using optimization tools with artificial intelligence-based algorithms for optimal design is defining an appropriate objective function and constraints. In the issue examined in this article inside light level, inside temperature, sunshade dimensions, wall thermal resistance, energy consumption In order to provide lighting, heating and cooling, All are measurable quantities that can be offered based on the objective function and constraints. On the other hand, the purpose of this study was the amount of illuminanace inside the building and its thermal behavior which is obtained by minimizing the building's annual energy consumption influenced by the optimal size of the window and its different types. For daylighting control types available in EnergyPlus, optimization algorithms must support discrete (on-off or 2 or 3 steps controls) and continuous (dimming cont rol) variables. In addition, the selected algorithm should support intrinsic approximation problems. Detailed buildings energy simulation tools such as EnergyPlus, T RNSYS, and DOE-2 involve solving a series of systems of partial and ordinary differential equations that are coupled to algebraic equations. Therefore, an optimal solution for a continuous cost function may be difficult to obtain without using a heuristic approach (Wetter et al., 2003). Wetter (2008) recommends hybrid algorithms using the General Pattern Search (GPS) method coupled with the HookeeJeeves algorithm with multiple starting points or the Particle Swarm Optimization (PSO) algorithm. Using this algorithm in GenOpt, with Energyplus output as input of the optimization problem, can be found to answer issue.
As previously noted, the objective function of this issue is the total annual energy consumption which is minimized by determining the coefficients for the efficiency and production cost of the energy. The above issue is optimized and analyzed for two efficiency, compression cooling system and evaporative cooling system in Tehran climate.
RESULTS
Order to determine and Analyzed the optimum dimensions of the window at the four main directions, for 8 types of glass studied by using either compression or evaporative cooling system, after performing optimization for 64-state results were as follows. Figures 2, 3, 4 and 5 show the optimal window area respectively for the North, South, East and West orientations, for evaporative cooling system in comparison with compression cooling system. As can be seen, the use of evaporative cooling system in the same condition the optimum window size will be larger in all four directions. According to Figure 2, in the north, reflective double glass in both cases is an option and its size in evaporative cooling system is 4.5×2.25 m2 (47% of surface) and in compression cooling system is 4×2 m2 (37% of surface). As shown in Figure 3 in the south, reflective double glass is the best and its optimum size in evaporative cooling system is 5×2.5 m2 (58% of surface) and in compression cooling system is 4.31×2.15 m2 (43% of surface).
Figure 2 Com parison of optimal window sizes for eight types in North, for systems, evaporative cooling and
com pression cooling
Figure 3 Com parison of optimal window sizes for eight types in South, for systems, evaporative cooling and
Figure 4 Com parison of optimal window sizes for eight types in East, for systems, evaporative cooling and
com pression cooling
Figure 5 Com parison of optimal window sizes for eight types in West, for systems, evaporative cooling and
com pression cooling
Data in Figure 4 indicate that East is a good choice for reflective double glass and the dimensions of evaporative cooling system is 2.23×4.46 m2 (46% surface) and of compression cooling system is 4×2 m2 (37% of surface). As well as shown in Figure 5 in west direction, If using compression cooling system reflective double glass, and if using evaporative cooling system Low-emissivity double glass with argon layer and reflective double
0 2 4 6 8 10 12 14 6mm clear single glazed clear double glaz ed with air layer clear double glaz ed with argon layer low-e clear double glaz ed with air layer low-e clear double glaz ed with argon layer low-e clear single glazed reflective clear double glaz ed with air layer reflective clear single glaz ed E as t W indo w A re a (m 2 )
Window Area with Compress ion cooling system Window Area with Evaporative cooling system
glass are better choice and Their dimensions are respectively 1.98 × 3.96 m2 (36% surface) in compression cooling system and 2.23 × 4.46 m2 (46% surface) for both glasses in evaporative cooling system.
T he Figure 6 shows that the optimum surface area for eight types of glass windows in the four cardinal directions, by using Evaporative cooling system instead of compression cooling system, increases in various glasses from 13 to 300 percent. As seen in Figure 6, using an evaporative cooling system the window size can be significantly increased with the objective to minimize the energy consumption. Among this low-emissivity double glass with argon layer with more than 300% increase in the south had the highest and reflective double glass with air layer with up to 34%, had the lowest increase. T his difference is due to the Low-emissivity double glass is greater than reflective double glass amount of solar energy.
Also, Figures 7 and 8 show the window optimum area for eight types of glasses in four different directions with each of the desired cooling system. It is observed that with evaporative cooling system, the optimum amount of window area in the south is higher than the other main directions and the area of reflective Double glazed window in both systems is higher than the other window.
Figure 6 Percent increase in the optimal value of the window area for eight types in four directions, for use
of the evaporative cooling system for com paring com pression cooling system
Figure 8 The optim al value of the window for the com pression cooling system efficiency
DISCUSSION AND CONCLUSION
In this study, the effects of location, material and size of the windows were investigated in order to minimize the annual energy consumption of administrative units with emphasis on the effect of fenestration surface. T he results indicate that using the evaporative cooling system compared to compression cooling system is more appropriate and more economical in T ehran climate and if office window has been placed in the optimum orientation, glass area can allocate up to 50% of façade surface. Also, reflective double glass and low-e double glazed with argon layer glass are appropriate for Tehran office building units and can respectivelyallocate the maximum level of window area and the minimum of energy consumption. Moreover, in the same conditions, using the evaporative cooling system, window optimum size in the four main directions can be varied from 19% for single-glazed window to 58% for reflective double-glazed window in the South direction.
REFERANCES
Lee, E., Selkowitz, S., 1995. “The design and evaluation of integrated envelope and lighting control strategies for commercial buildings”. ASHRAE Transactions, 101 (1), 326-342.
T zempelikos, A., Bessoudo, M., Athienitis, A.K, Zmeureanu, R., 2010. “Indoor thermal environmental conditions near glazed facades with shading devices – Part II: Thermal comfort simulation and impact of glazing and shading properties”. Building and Environm ent, 45, pp. 2517-2525.
Hviid, C., Nielsen, T.R., Svendsen, S. 2008. “Simple tool to evaluate the impact of daylight on building energy consumption”. Solar Energy, 82 (9), pp. 787-798.
Franzetti, C., Fraisse, G., Achard, G., 2004. “Influence of the coupling between daylight and artificial lighting on thermal loads in office buildings”. Energy and Buildings, 36, pp. 117-126.
Citherlet, S., Clarke, J.A., Hand, J., 2001. “Integration in building physics simulation”. Energy and Buildings, 33, pp. 451-461.
Walkenhorst, O., Luther, J., Reinhart, C., Timmer, J., 2002. “ Dynamic annual daylight simulations based on one-hour and one-minute means of irradiance data”. Solar Energy, 72(5), pp. 385-395.
T zempelikos, A., Athienitis, A., 2007. “The impact of shading design and control on building cooling and lighting demand”. Solar Energy, 81, pp. 369-382.
Reinhart, C., Walkenhorst, O., 2001. “ Validation of dynamic Radiance-based daylight simulations for a test office with external blinds”. Energy and Buildings, 33 (7), pp. 683-697.
Loutzenhiser, P., Manz, H., Felsmann, C., Strachan, P.A., Maxwell, G.M., 2007. “An empirical validation of modeling solar gain through a glazing unit with external and internal shading screens”. Applied Thermal
Robinson, D., Stone, A., 2006. “ Internal illumination prediction based on a simplified radiosity algorithm”. Solar
Energy, 80 (3) , pp. 260-267.
Walkenhorst, O., Luther, J., Reinhart, C., Timmer, J., 2002. “ Dynamic annual daylight simulations based on one-hour and one-minute means of irradiance data”. Solar Energy, 72(5), pp. 385-395.
Mardaljevic, J., 2004. “Spatio-temporal dynamics of solar shading for a parametrically defined roof system”.
Energy and Buildings, 36 (8), pp. 815-823.
Fakra, A.F., Miranville, F., Boyer, H., Guichard, S., 2011. “ Development of a new model to predict indoor daylighting: Integration in CODYRUN software and validation”. Energy Conversion and Managem ent, 52 (7), pp. 2724-2734.
Ghisi, E., T inker, J., 2005. “An Ideal Window Area concept for energy efficient integration of daylight and artificial light in buildings”. Energy and Buildings, 40 (1), pp. 51-61.
Reinhart, C., Selkowitz, S., 2006. “Daylighting—Light, form, and people”. Energy and Buildings, 38(7), pp. 515-517.
Nabil, A., Mardaljevic, J., 2006. “Useful daylight illuminances: A replacement for daylight factors”. Energy and
Buildings, 38(7), pp. 905-913.
Reinhart, C., Wienold, J., 2011. “ T he daylighting dashboard – A simulation-based design analysis for daylit spaces”. Buildings and Environment, 46(2), pp. 386-396.
Seo, D., Ihm, P., and Krarti, M., 2011. “Development of an optimal daylighting controller”, Building and Environment, 46, pp. 1011-22.
Hani, A., and Koiv, T.A., 2012. “Optimization of office building facades in a warm summer continental climate”, Smart Grid and Renewable Energy, pp. 222-230.
Wetter, M., Polak, E., 2003. “A convergent optimization method using pattern search algorithms with adaptive precision simulation”, IBPSA Conference, Eindhoven, Netherland.
Wetter, M., Wright, J., 2003. “Comparison of a generalized pattern search and a genetic algorithm opt imization method”, IBPSA Conference, Eindhoven, Netherland.
Wetter, M., 2008. “GenOpt generic optimization program”, User manual version 2.1.1., Technical report LBNL-54199, Building Technologies Program, Simulation Research Group, Lawrence Berkeley National Laborat
