OPTIMIZATION OF WINDOW-WALL RATIO FOR DIFFERENT BUILDING TYPES Srijan Kr. Didwania *$, Vishal Garg **, Jyotirmay Mathur * * Malaviya National Institute of Technology, Jaipur (India) ** International Institute of Information Technology, Hyderabad (India) $ Corresponding author: email- srijandidwania@gmail.com Abstract While it is possible to check the energy performance of a given building by means of several available methods, the inverse problem of determining the optimum configuration given a desired performance is more difficult to solve. This paper investigates the application of an optimization program for identification of the optimum WWR (Window to Wall Ratio) in office buildings (G+1) of various sizes and orientation varying independently in all directions, separately for ground floor and top floor. It brings out the need for optimization based approach in achieving greater efficiency in a building using a simulation tool Plus with an optimization program GenOpt. It strongly reflects that it is not only the type of glazing (i.e. thermally efficient glazing) that needs to be emphasized, but attention should also be given to the WWR that is being adopted for different directions, different floors and different types of glass. Keywords: Optimization, WWR, Simulation, Window Performance 1.1 Introduction During the design process there is considerable scope for reducing the energy consumption in both new and existing buildings. Although some forms of reduction in energy use can be achieved by relatively simple individual measures, very high levels of performance require the coherent application of measures which together optimize the performance of the complete building system. certification of different parts of a building can be a useful tool for improving the energy performance of construction as a whole. Windows play an important role in the energy performance and therefore should be chosen carefully. Selection of type of glazing along with its proportion to the total wall area is very important for achieving a desirable indoor environment and energy consumption. As it is well known that window contributes to heat gain and day lighting, the quality of glazing and its size, frames and dividers to be used should be decided keeping in view both these aspects. Most papers published so far in the engineering domain have focused on the impact of WWR on heating and air conditioning energy consumption. Xing Su, Xu Zhang [1] discussed environmental performance optimization of window wall ratio for different window type. Influence of WWR on annual energy consumption of heating and air conditioning system of residential buildings in hot summer and cold winter region under different orientation has been discussed in [2 4]. In [5], some specific results are given in relation to the optimum aspect ratios for south window sizes, from the point of view of thermal performance. On the other hand few literatures brought up the savings in lighting energy for typical office buildings by installation of dimming controls with different glazing. Szerman [6] gives the value: 77% of lighting energy savings and 14% of total energy savings from dimming with classical windows. Opdal and Brekke [7] compared measurements and calculated results and obtained 40% of lighting saving (simulations) and 30% of lighting energy saving (measurements) from dimming. In their case, they did not find any heating and cooling consumption difference coming from the lighting management. However, the effect of WWR on energy consumption due to heating and cooling along with lighting should be further incorporated.
M. Bodart, A. De Herde [8] evaluated the impact of lighting energy savings on global energy consumption in office buildings. They combined the thermal and lighting aspects for few facade configurations to bring out the total energy savings. However there is a need to bring out the optimum glazing dimension for glasses having different properties, different facades and different orientations, which has been incorporated in this document. Various optimization methodologies for future integration in building performance tools have been discussed in [10]. Hopfe, C.J. discussed uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization [11]. He showed how the application of diverse prototypes could benefit and enhance building design methods, with the emphasis on discrete decision making and component optimization, under uncertainty. Selkowitz and Lee [12] discussed advanced interactive facades as critical elements for future green buildings. He highlighted the use of new technology, better system integration using more capable design tools, and smarter building operation that are all necessary to meet the goals of a new class of buildings that are both environmentally responsible at a regional or global level while providing the amenities and working environments that owners and occupants seek. Thus, need for an optimization based approach keeping in view the heat gain and visible light transmittance has become necessary. Use of good glazing material (ECBC standards) is not sufficient for saving energy unless the optimum glazing area is found for different types of glazing materials, different size and orientation of buildings. 1.2 Need for Optimization The use of system simulation for analyzing complex engineering problems is increasing. Such problems typically involve many independent variables, and can only be optimized by means of numerical optimization. Parametric studies are used by many designers for achieving better performance of such systems, even though only partial improvement is yielded by those studies, while requiring high labor time. In such parametric studies, one usually fixes all but one variable and tries to optimize a cost function with respect to the non-fixed variable. The procedure is repeated iteratively by varying another variable. However, every time a variable is varied, all other variables typically become non-optimal and hence need also to be adjusted. It is clear that such a manual procedure is very time-consuming and often impractical for more than two or three independent variables. GenOpt, a generic optimization program, has been developed to find with less labor time the independent variables that yield better performance of such systems. Optimization of a user-supplied cost function is done by GenOpt, using a user-selected optimization algorithm. In this study, it has been used to optimize the WWR for different orientation and direction of the building. The window to wall area ratio (WWR) has an important effect on building energy consumption for heating, air conditioning and lighting. For one thing, solar heat gains will be increased as the WWR increases, the heat exchange will be also increased for the heat transfer coefficient of window being usually larger than that of wall. On the other hand, the artificial lighting consumption will decrease as WWR increases. These two opposing facts bring out the need for optimization, such that an optimum WWR is reached where the total electricity consumption is minimized. The effect of increasing WWR on air conditioning and artificial lighting has been shown in Figure 1(a). The variation of air conditioning energy consumption with WWR changes with different orientations of the openings as discussed by Y.B. Hou and X.Z. Fu in [2]. From that study it becomes quite evident that orientations have significant effect. Air conditioning energy increase with increasing WWR, on the other hand the trend for artificial lighting energy is just opposite i.e. it decreases with increasing WWR. Thus, the overall impact of WWR on total energy consumption can be seen in Figure 1(b). It is a U-shaped curve
with minimum lying somewhere around 20% WWR. The minimum varies for different types of building and different orientation of opening. This brings out the need for optimization so as to reach the point of minimum energy consumption with combination of different WWR in all four directions. 1.3 Overview of the Optimization Tool The optimization tool used in this study was GenOpt. It is an optimization program for the minimization of a cost function that is evaluated by an external simulation program. It is developed by Lawrence Berkeley National Laboratory, Building Technologies Department, Simulation Research Group [9]. GenOpt can be coupled with any simulation program that reads its input from text files and writes its output to text files. So, the input variable is given to the simulation program by GenOpt, according to the algorithm and limits defined, and the output is taken which could be processed into the desired function that needs to be minimized. The independent variables can be continuous variables (possibly with lower and upper bounds), discrete variables, or both, continuous and discrete variables. Constraints on dependent variables can be implemented using penalty or barrier functions. GenOpt uses parallel computing to evaluate simulations. The library of GenOpt consists of local and global multi-dimensional and one-dimensional optimization algorithms, and algorithms for doing parametric runs. An algorithm interface allows adding new minimization algorithms without knowing the details of the program structure. GenOpt has been written in Java so as to make it platform independent. The platform independence and the general interface make GenOpt applicable to a wide range of optimization problems. GenOpt has not been designed for linear programming problems, quadratic programming problems, and problems where the gradient of the cost function is available. For such problems, as well as for other problems, special tailored software exists that is more efficient. Few other interesting properties of GenOpt are: 1. An optimization algorithm can be selected by a user from an algorithm library, or a custom algorithm can be implemented without having to recompile and understand the whole optimization environment. 2. An expression for the gradient of the cost function is not required by GenOpt. With GenOpt, it is easy to couple a new simulation program, specify the optimization variables and minimize the cost function. Therefore, in designing complex systems, as well as in system analysis, valuable assistance is offered by such generic optimization program.
1.4 Optimization Process A simple flow chart diagram has been shown in Figure 2. The entire process can be broadly classified into two parts, the optimization part and the simulation part. Simulation part is addressed by Plus and optimization part by GenOpt. The strings for various input and output files to be used by energy plus and GenOpt, along with the functions involved in the optimization process is contained by the initialization file. The description of different variables that need to be optimized during the process and the specification of algorithm that is to be used for optimization is contained by Command file. The variable inputs mentioned in the command section are assigned certain range of values so as to get the results within acceptable and practical limits. With start of the optimization process, the first iterate (initial value specified in the command file) is assigned to the input file of Plus which carries out simulation calculations and reports the result in its output file. This output meter value is then collected by GenOpt which is processed into a function whose minimum value is to be achieved. For example the function can be such that it gives the payback period or the total energy consumption. The next iterate is then generated by GenOpt using the initial value and the step size defined for the variable. The output value for the next input is again collected from Plus output file. Having the two function values for two different inputs, both the results are compared by GenOpt and the next iterate is assigned accordingly, i.e. if the function value for the second iterate came out to be higher than the previous one, the iterate is rolled back in opposite direction and finally the minimum function value is searched for the entire range of variables in accordance with the step size mentioned. The selection of iterates is done by the optimizer i.e. it is influenced by the algorithm selected by the user. In this study, the input variable taken was WWR (in percentage form), having a range from 5 to 100 percent. A minimum of 5% WWR was assumed to be present so as to allow some natural light and outside view. The output meter values collected from plus were Es_cool, Es_heat and Es_light, i.e. cooling, heating and lighting energy (electricity) required respectively. These values upon summation yield total electricity consumption which need to be minimized. It was referred to as Es_total.
Es_total = Es_cool + Es_heat + Es_light Initialization value for the input variable was taken 80 % and thus, the first iterate for WWR given to the input file of Plus was 80. The three meter values from its output file was collected and processed for total energy consumption. The next input given was 90 (for a step size 10) and the function value was obtained again. Having compared the Es_total value for both the cases, the third iterate was assigned accordingly which in this case couldn t have been above 90 (max value of range). Therefore, the next iterate assigned was 70. For achieving greater accuracy there is a provision for reducing the step size in the range where function was minimized. Accordingly the number of step reductions could be specified by the user. For example, if the minimum point appeared to lie for WWR between 20 and 30, the step size could be reduced to 5 and similar calculations could be carried out for the WWR of 25 as well. The step size could be further reduced to 2.5 and so on according to the number of step reductions described by the user for reaching desired accuracy level. 1.5 Building Description The building taken for the study was a typical day-time use G+1 office building (9 am to 9 pm) with a core zone surrounded by four perimeter zones (6 meters depth) on both the ground and top floors. It was considered to be situated in New Delhi (Latitude: 28.38 N, Longitude: 77.12 E) which is having a composite climate. The cooling and heating temperature set-point were taken 24 o C and 20 o C respectively. Windows were provided with internal shade for glare control. The shading control was set for a maximum allowable discomfort glare index of 22. All the other input parameters were as per the base case of ECBC described in Table-1. Table-1: Building Description Parameters Values Parameters Values Location Area (sq m) Latitude 28.38 N Gross floor area Varies Longitude 77.12 E Conditioned floor area Varies Climate Type Composite Ground Reflectance 0.2 Frames & Dividers Material PVC Geometry Frame width (cm) 4 Dimension (m) Varies Divider width (cm) 2 Floor to floor height (m) 3.5 Conductance (W/m2-K) 3.4 Number of floors 2 Divider spacing (m) 1 WWR Varies Internal Shade Thickness (cm) 0.5 Envelope Material Conductivity (W/m-K) 0.1 Roof U value (W/m2-K) 0.403 Solar transmittance 0.1 Wall U value (W/m2-K) 0.447 Solar reflectance 0.8 Floor U (W/m2-K) 0.795 Visible transmittance 0.7 Visible reflectance 0.2
HVAC System type PTAC Internal loads Fan control Constant Lighting (W/sq m) 10 Cooling COP 3.5 Equipment (W/sq m) 20 Heating coil type Electric Occupancy (sq m/person) 10 Daylight sensor Daylight control Dimming Glazing Detail Number 16 Glass U value (W/m2-K) Varies Illuminance 500 lux Glass SHGC Varies Allowable Glare Index 22 Glass VT Varies 1.6 Combinations Analyzed The study involved analysis of optimum WWR for ground floor and top floor with independent distribution of window on all four sides of the building i.e. the WWR of four directions may vary independently and the optimum combination of WWR on four sides could be obtained. Intermediate floor was also considered but the result came out to be same as that of ground coupled floor. Hence intermediate floor was neglected. The results for ground and intermediate floors came out to be same due to their identical exposure to the surrounding while the result for the top floor was different due to the effect of roof which accounted for major portion of the heat gain leading to higher HVAC loads. The climatic condition taken under consideration was composite as that of New Delhi, India. Weather file used in all the cases were IWEC files. Table-2 shows dimensions and orientations that have been analyzed for optimizing WWR with three different types of glazing as shown in Table-3. Table-2: Dimensions of analyzed buildings TYPE 1:1 1:2 1:2 FLOOR AREA (m2) NS orientation orientation 2500 50*50 70*35 35*70 1600 40*40 56*28 28*56 900 30*30 42*21 21*42 Table-3: Properties of analyzed glazing Type SHGC VLT U-value (W/m 2 -K) D253933 (Double) 0.25 0.39 3.3 S818861 (Single) 0.81 0.88 6.1 S475556 (Single) 0.47 0.55 5.6 Windows on different orientations have different effects on solar heat gain due to different cooling load factors (CLFs), cooling load temperature differences (CLTDs) and maximum solar heat gain factors (MSHGFs). Thus analysis of independently varying WWR in different directions for different orientation of building becomes essential. The comparison for optimum independent WWR with optimum symmetrical WWR has also been analyzed.
1.7 Results and Discussion The optimum WWR (in %) for various combinations of building size, orientation and glazing properties, have been presented in the tables 4-6 of this section. Table-4: WWR for double glazed, low SHGC window (D253933) Building Type Floor North East South West 50*50 (1:1) 70*35 (1:2) NS* 35*70 (1:2) * 40*40 (1:1) 56*28 (1:2) NS 28*56 (1:2) FIRST FLR 40 25 15 20 GROUND FLR 45 25 20 20 FIRST FLR 40 25 15 20 GROUND FLR 40 25 15 20 FIRST FLR 50 25 20 20 GROUND FLR 50 25 20 20 FIRST FLR 45 25 15 20 GROUND FLR 50 30 20 20 FIRST FLR 40 30 15 20 GROUND FLR 50 25 15 20 FIRST FLR 65 25 20 20 GROUND FLR 45 30 15 20 30*30 (1:1) FIRST FLR 55 25 15 20 GROUND FLR 50 25 20 25 42*21 (1:2) NS FIRST FLR 40 15 15 20 GROUND FLR 55 25 15 20 21*42 (1:2) FIRST FLR 60 25 15 20 GROUND FLR 45 25 15 20 *NS means North-South orientation (i.e. longer side facing north and south) * means East-West orientation (i.e. longer side facing east and west) It is evident that thermally efficient glass allows greater WWR which is reflected from the results in Table 4-6 as well. Also, it is well known that for the northern hemisphere and climate like that of India, maximum WWR should be in north direction, followed by east, west and minimum in south direction. It should be noted that this statement only applies in the context of this study in New Delhi which lies in the Northern Hemisphere and is specific to office buildings dominated by a high cooling load and a small heating load. In this case more glazing to the north and less or none to the west and south should typically give a more optimal energy and daylight balance, with a reduction in overheating risks. Looking at the trend for a inferior single layer glass (S818861) shown in Table-5, on an average, northern facade can attain a WWR around 20-30%, followed by east and west with about 10%, and south only up to 5%.
Table-5: WWR for single glazed, high SHGC window (S818861) Building Type Floor North East South West 50*50 (1:1) 70*35 (1:2) NS 35*70 (1:2) 40*40 (1:1) 56*28 (1:2) NS 28*56 (1:2) 30*30 (1:1) 42*21 (1:2) NS 21*42 (1:2) FIRST FLR 20 10 5 5 GROUND FLR 35 10 5 10 FIRST FLR 20 10 5 10 GROUND FLR 35 15 5 10 FIRST FLR 20 10 5 10 GROUND FLR 20 10 10 10 FIRST FLR 40 10 5 10 GROUND FLR 40 10 10 10 FIRST FLR 20 10 5 10 GROUND FLR 30 10 5 10 FIRST FLR 25 10 5 5 GROUND FLR 20 10 10 10 FIRST FLR 65 10 5 10 GROUND FLR 45 10 10 10 FIRST FLR 65 5 5 5 GROUND FLR 55 10 10 10 FIRST FLR 25 10 5 5 GROUND FLR 20 15 5 10 Table-6: window area for single glazed, moderate SHGC window (S475556) Building Type Floor North East South West 50*50 (1:1) 70*35 (1:2) NS 35*70 (1:2) 40*40 (1:1) 56*28 (1:2) NS 28*56 (1:2) FIRST FLR 25 15 10 10 GROUND FLR 40 15 10 15 FIRST FLR 25 10 10 10 GROUND FLR 35 15 10 15 FIRST FLR 30 15 10 10 GROUND FLR 30 15 15 10 FIRST FLR 30 15 10 10 GROUND FLR 35 15 10 15 FIRST FLR 25 10 10 10 GROUND FLR 30 10 10 10 FIRST FLR 30 15 10 10 GROUND FLR 35 15 10 10 30*30 (1:1) FIRST FLR 40 15 10 15
42*21 (1:2) NS 21*42 (1:2) GROUND FLR 35 10 10 10 FIRST FLR 30 5 10 10 GROUND FLR 35 10 10 10 FIRST FLR 40 15 10 10 GROUND FLR 20 5 10 20 Table-7: Comparison of energy consumption with symmetric and asymmetric windows D253933 (Double) Glazing Building Type Floor WWR WWR kwh/m2-yr (N-E-S-W) kwh/m2-yr 50*50 (1:1) 70*35 (1:2) NS 35*70 (1:2) FIRST FLR 20 48.3 40-25-15-20 48.1 GROUND FLR 25 48.5 45-25-20-20 48.2 FIRST FLR 25 47.7 40-25-15-20 47.4 GROUND FLR 25 47.9 40-25-15-20 47.5 FIRST FLR 25 48.2 50-25-20-20 48.1 GROUND FLR 25 48.4 50-25-20-20 48.2 Building Type Floor WWR S818861 (Single) Glazing WWR kwh/m2-yr (N-E-S-W) kwh/m2-yr 50*50 (1:1) 70*35 (1:2) NS 35*70 (1:2) FIRST FLR 10 50.1 20-10-5-5 49.9 GROUND FLR 15 50.7 35-10-5-10 50.5 FIRST FLR 10 49.8 20-10-5-10 49.5 GROUND FLR 15 50.3 35-15-5-10 50.1 FIRST FLR 10 50.8 20-10-5-10 50.6 GROUND FLR 10 51.3 20-10-10-10 51.1 Building Type Floor WWR S475556 (Single) Glazing WWR kwh/m2-yr (N-E-S-W) kwh/m2-yr 50*50 (1:1) 70*35 (1:2) NS 35*70 (1:2) FIRST FLR 15 49.9 25-15-10-10 49.6 GROUND FLR 20 50.2 40-15-10-15 49.9 FIRST FLR 15 49.3 25-10-10-10 49 GROUND FLR 20 49.7 35-15-10-15 49.4 FIRST FLR 15 50.6 30-15-10-10 49.8 GROUND FLR 15 50.5 30-15-15-10 50.3
Similarly, moving on to a better single layer glass (S475556) shown in Table-6, the optimum WWR came out to be 30-40%, 15%, 10%, and 10% on an average for north, east, west and south facade respectively. For double glass assembly shown in Table-4, the optimum value for WWR reached 40%-60% on north, 25% on east, 20% on west and 15% on south. Now, analyzing the results diligently, it could be identified that in general the ground floor allows a higher optimum WWR. This difference of results for ground and top floor were basically governed by two parameters, the difference in HVAC sizing for both floors and the ground reflectivity. The higher HVAC sizing due to the effect of roof on top floor resulted in greater CFM leading to higher convective heat gain through windows, thus favoring a smaller window. On the other hand the ground reflectivity would account for a smaller window on the ground floor. However it could be identified that this parameter was less pronounced than the former in most cases. In few of the cases where the zone size was smaller and the HVAC difference became less effective, this parameter played a dominant role. It could be seen that the effect of ground reflectivity dominated over the effect of HVAC sizing mostly on the north façade and that too in case of smaller building foot print. This was because in the northern hemisphere, the sun is mostly on the south and thus the reflected radiation from the ground dominates over the direct solar radiation. On the other hand, the optimum WWR attained on distributing the windows symmetrically on all four sides on the building came to be quite less as compared to independent distribution. The comparison of energy consumption (Lighting & HVAC) between both the cases (one with optimum symmetrical distribution and other with optimum independent distribution) is shown in Table-7. With these results it can be visualized that considering an optimized asymmetric window distribution proves much better than symmetric distribution. 1.8 Conclusions The U-shaped nature of the curve between total energy consumption and WWR straight away brings out the necessity for optimization based approach in determining the most efficient opening size for a particular glazing type, orientation and type of building. The method of reaching to the optimum values combining all four sides as discussed in this study proves to be very efficient with minimal efforts on manual side. It s not only the type of glazing (i.e. thermally efficient glazing recommended by ECBC) that need to be emphasized for better building performance, but attention should also be given to the WWR that is being adopted for different directions, different floors and different types of glass. Independent distribution of glazing on the four directions should be preferred rather than symmetric distribution as it can be seen that the optimized WWR for the latter case is much lower. With this, a higher WWR can be afforded in few directions for better aesthetics and outside view as compared to symmetric distribution and that too without compromising with savings, but in fact enhancing it. It also accounts for better comfort due to more of natural light than artificial lights during working hours. 1.9 References [1] Xing Su, Xu Zhang, Environmental performance optimization of window wall ratio for different window type in hot summer and cold winter zone in China based on life cycle assessment, and Buildings 42 (2010) 198 202 [2] Y. Feng, H. Yang, Defining the area ratio of window to wall in Design standard for energy-efficiency of residential buildings in hot summer and cold winter zone, Journal of Xi an University of architecture and Technology 33 (2001) 348 351. [3] Y.B. Hou, X.Z. Fu, Affection of WWR on energy consumption in region of hot summer and cold winter, Architecture Technology 10 (2002) 661 662.
[4] Y.W. Jian, Y. Jiang, Influence of WWR on annual energy consumption for heating and air conditioning in residential buildings, Heating Ventilating and Air Conditioning 36 (2006) 1 5. [5] M.N. Inanici, N. Demirbilek, Thermal performance optimization of buildings aspect ratio and south window size in five cities having different climatic characteristics of Turkey, Building and Environment 35 (2000) 41 52. [6] M. Szerman, Superlink: A Computer tool to evaluate the impact of Daylight-controlled lighting system onto the overall energetic behavior of buildings, in: Proceedings of Right Light 2, Arnhem, 1993, 673-685. [7] K.Opdal, B.Brekke, savings in lighting by utilization of daylight, in: Proceedings of Right Light 3, Newcastle-upon-Tyne, 1995, 67-74. [8] M. Bodart, A. De Herde, Global energy savings in office buildings by the use of daylighting, and Buildings 34 (2002) 421-429. [9] GenOpt(R), Generic Optimization Program, User Manual, Version 3.0.0 [10] Emmerich, M. T. M., Hopfe, C. J., Marijt, R., Hensen, J., Struck, C., & Stoelinga, P. 2008. Evaluating optimization methodologies for future integration in building performance tools. [11] Hopfe, C.J., (2009). Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization, PhD thesis, Technische Universiteit Eindhoven, The Netherlands. [12] Selkowitz, S., Aschehoug, Ø., Lee, E.S., Advanced Interactive Facades Critical Elements for Future Green Buildings?, LBNL-53876.