Evaluation of Window Energy Rating Models for Different Houses and European Climates

Transcription

1 Evaluation of Window Energy Rating Models for Different Houses and European Climates J. and A. Roos Department of Materials Science, The Ångström Laboratory Uppsala University P.O. Box 534, S Uppsala Sweden Abstract In this paper different window energy rating systems (WERS) are evaluated and compared. The comparisons are made for different European climates, types of buildings and orientations. The purpose of the paper is to evaluate how complex a WERS needs to be in order to be able to provide a reliable energy rating which can be applied to different buildings in different climates and orientations. The results indicate that Europe needs to be separated into several different climate zones. A simple linear heating energy-rating model of the form Ag-BU (where g is the g-factor, U is the U-value of the window, and A and B are empirical coefficients), may be sufficient within certain regulations. For warm locations, a linear cooling energy rating, depending only on the g factor, may also yield an acceptable rating. More advanced models that take into account the building type and that uses hourly climate files shows good fit with building simulation programs. 1. INTRODUCTION The glazing of an energy efficient window includes one or more panes coated with a thin film that improves the heating and/or cooling performance of the window. In a heating-dominated location this film enhances the greenhouse effect by reflecting infrared radiation (low-emittance coatings). In a cooling dominated location the film should reflect the near infrared part of the solar radiation and thus prevent excess heat from entering the building (solar control coatings). Quite a large variety of energy efficient windows, which are based on the above principles, are already available on the market. However, the energy efficiency of the window is not immediately obvious, which makes the choice of window difficult for a consumer. There is ongoing work in several countries 1,2,3,4,5,6,7,8, with the purpose of establishing a system for energy labeling, or energy rating, of windows, which would indicate the possible savings of an advanced window as compared to a standard window. The goal is to increase the use of energy efficient products, by illustrating their benefits. The same problem has been identified for energy efficient appliances (such as refrigerators, freezers etc) for which the product needs to be labeled in some way to indicate its reduced power consumption. There are several ways of establishing a window energy rating system (WERS). However, many problems are also involved. The most striking problems are that the energy efficiency of a window depends on in which climate it is used, in which type of building it is used, and which direction it is facing. This means that not only the parameters of the window determine its final energy performance, but also other external parameters. The different ways to elaborate a WERS can be categorized as: 1. Include physical properties: Compare windows based on their physical data, such as the heat leakage (Uvalue), total solar energy transmittance (g) and the light transmittance, (T vis ). 2. Include climate: Make a simple energy balance of the window of the type: Ag-BU, where the empirical coefficients A and B depend on annual or seasonal solar radiation and temperature (degree-days) within the climate zone. Different orientations of the window can also be considered by varying A. 3. Include building properties: Identify simplified building parameters in order to distinguish between different building types. 4. Full scale building simulation: Perform full-scale simulations within a certain climate zone, in which the investigated windows are placed in a certain category. ISES 21 Solar World Congress 193

2 Category 1 is very simple and general but often fails to provide reliable information about how much energy the window saves. Category 4 provides accurate results (provided the simulation model is correct) but it also requires a lot of input data, which are not generally available, and experienced users. Categories 2 and 3 are somewhere in between. All four categories can be divided into several subcategories, and the different models work best with specific recommendations for different climate zones. The key issue is to provide reliable energy performance information without having to go to category 4 each time a window is evaluated. Categories 2 and 3 have to be included in some way, but how detailed the required input data need to be is still an open question. The division into different climate zones is necessary for whichever method that is used. In this paper, different window energy rating systems of the above types are evaluated and compared. The comparisons are made for different European climates, types of buildings and orientations. The comparisons are made for the glazings, and the frame U-value is kept constant. The purpose of the paper is to evaluate how complex a WERS needs to be in order to be able to provide a reliable energy rating which can be applied to different buildings in different climates and orientations. 2. PREREQUISITES This study is limited to three different climates: Stockholm, Berlin and Madrid, having annual average temperatures of about 6.9, 9. and 13.5 C, respectively. The solar radiation impinging on a horizontal surface for these sites are about: 98, 1 and 17 kwh/m 2 yr, respectively. The hourly climate files are synthetically produced with the software Meteonorm 9. The windows are tested in two types of buildings, one base-case as described in table 1, and one low-energy house having the same data as the house in table 1, except that it has a lower wall U-value of.18 W/m 2 K and ventilation plus infiltration of 1.2 ach. Table 1: Data for the base case house. Heat loss to the ground is calculated according to the Swedish norm 1 (i.e. no time delay and heat loss is multiplied by.75 to account for storage in the ground). * Cooling set point is only of importance for Madrid. BASE CASE HOUSE Dimension 1*2.7*1 m 3 Wall U-value.43 W/m 2 K Glazing U-value 2.9 W/m 2 K Glazing g-factor 5 % Frame U-value 2.2 W/m 2 K Ventilation+infiltration 2 ach GWAR 2 % Window orientation Equal to all orientations Free heat, Q int 2 kwh/yr Heating set point 2 Cooling set point * In order to investigate the differences from changing only the U-value or only the g-factor of the glazing, a test set of fictitious glazings were used having U-values of 2.9, 1.7 and.91, and g-factors of 75, 5 and 25% (giving 9 different alternatives). The models that are used for the comparison are the model 11,12, which is a simple version of category 4, mentioned in the introduction. This model is further developed for this study by the inclusion of the anisotropic Hay and Davies 13 model for the solar radiation on vertical surfaces and by taking the different angle dependence of the transmittance of the coated glazings into account 14,15. This gives us an hourly dynamic model, but with constant wall and window U-values. The model is validated versus a detailed building simulation program (DEROB-LTH 16,17 ) for different climates and glazings in figure 1. ISES 21 Solar World Congress 194

3 Saved energy (kwh/m 2 yr), compared to an uncoated DGU Low-energy house U=1.9, g=67, DEROB U=1.9, g=67, U=.9, g=58, DEROB U=.9, g=58, Stockholm, Heating Berlin, Heating Madrid, Heating Madrid, Cooling Figure 1. Validation of the simple hourly, dynamic model versus the detailed dynamic simulation program DEROB-LTH for different climates and glazings. The y-axis gives the energy saving versus an uncoated double glazed unit, DGU, (U=2.9 W/m 2 K, ). The rightmost four columns give the saved cooling energy (for Madrid) the rest of the columns refer to the saved heating energy. The model is compared to the model 18,19 which can be listed under category 3 as mentioned above. This model uses hourly climate files from the climate zone but highly simplifies the building to only one parameter, the balance temperature. The balance temperature decides whether the heat flows through the window are useful or not for the building and defines the heating season for the building depending on the outside temperature. In this report the balance temperature is calculated from the building data 2. The and models are compared to a linear (referred to as Danish ) model of the form Ag-BU (Category 2), which represents the heating energy balance of the window, such as implemented by Denmark 4 and other countries 1. In the linear model the A coefficient represent the useful solar heat impinging on the window and thus depends on the climate and the type of building. The B coefficient depends mostly (see results below) on the climate and is represented by the number of degree hours for the location. The angle dependent transmittance is considered equal for all glazings and models in the comparisons below. The Danish model is the simplest of the ones compared here, but it requires that the coefficients are evaluated in some way for each climate zone. The coefficients can be deduced 6 or extracted from (fitted to) more detailed building simulations, as is done in this report. The model requires hourly climate files for the climate zone and that the balance temperature is guessed, assessed or calculated 18,2. The model requires all basic building data such as the UA-values, ventilation, internal heat production, etc. 3. RESULTS In figure 2 the heating energy rating results are shown for the three different climates, two different buildings and for all the nine glazings. The x-axis gives the and the y-axis gives the saved heating energy versus a base case window, which is set to U=2.9 W/m 2 K and g=5%. Thus, the base case glazing appears at the zero on the y-axis and the other points illustrates how much energy that is saved for that location and building when another glazing is selected. Hence, the y-axis does not give any information of how much energy the building needs, only the saved energy caused by a window substitution. The saved energy is given in kwh per square meter glazed area and year. The arrows in the figures indicate the different g-factors and in the legend the A and B coefficients for the Danish model, which is approximately fitted to the other two models, are shown. The higher the value on the y-axis, the higher is the heating energy rating and the better the window performs, in this aspect. Naturally, the higher the g-factor and the lower the U-value, the better is the heating energy rating. The rating response to both the U-value and the g-factor seems to be linear (figure 2). The slope of the lines is steeper the colder the climate is, and also the poorer the building is (i.e. poorer insulated, higher infiltration etc.). ISES 21 Solar World Congress 195

4 The and the model are in quite good agreement. When it comes to the Danish model of the form Ag-BU, B seems to be determined almost only by the climate zone, although there is a slightly lower saving for a reduced U-value in the low energy house than in the base case house. A is determined by the type of building, the better the building, the shorter is the heating season and thus, the smaller is A and the importance of the g- factor (for heating). The colder the climate the larger is the number of degree-days and thus the B coefficient. The A coefficient depends on the climate zones, the available solar energy and its angles of incidence. It is therefore not straightforward to state how the A coefficient changes, depending on the climate zone, see figure Stockholm *A=37, B=11 g=5% Stockholm, low energy house *A=3, B=11 g=5% c. d Berlin *A=33, B=9 g=5% Berlin, low energy house *A=29, B=9 g=5% e. f Madrid *A=4, B=6 g=5% Madrid, low energy house *A=35, B=6 g=5% Figure 2: Comparison of the saved heating energy rating compared to the base case window between the and the model. The Danish model is approximately fitted linearly to these two models. The figures illustrate the comparisons for the Stockholm, Berlin and Madrid climate, respectively, and for the base case and the low energy house, respectively. ISES 21 Solar World Congress 196

5 For the warm Madrid climate a sole heating energy rating is insufficient, since overheating is also of concern. In figure 3 the saved cooling energy when changing from the base case window to another alternative is plotted versus the g-factor, for the two different houses. It is seen that for the cooling energy rating the g-factor is more important than the U-value. The dependence on the g-factor is higher for the low energy house than for the base case house, since the low energy house is more insulated and more sensitive to overheating. Furthermore, the importance of the U-value is higher in the low energy house, figure 3b. In the Danish linear energy rating, no model is specified for cooling, but it is seen here that it may be possible to use some kind of linear cooling energy rating, depending only on the g-factor of the window. In this case a linear cooling energy rating of the form -Cg, where C is a coefficient depending on the climate and building, is approximately fitted to the other models. The cooling energy rating is thus better the lower the g-factor is, which means that some restriction of the light transmittance should also be introduced. For instance, if the light transmittance should be higher than 6%, the lowest possible g-factor (or more correct the direct solar transmittance, T sol ) is about 3%, since about half of the solar energy is located within the visible spectral region. The maximum difference between the and the model is about 2 kwh/m 2 yr when changing from to (figure 3b). The maximum difference between the base case house and the low energy house is about 4 kwh/m 2 yr when changing from to (figure 3). ooling energy (kwh/m 2 yr) Saved c Madrid g-factor (%) Linear* *C=2 U-value difference cooling energy (kwh/m 2 yr) Saved 8 Madrid, low energy house g-factor (%) Linear* *C=28 U-value difference Figure 3: Saved cooling energy compared to the base case versus the g-factor. A linear model of the form - Cg is approximately fitted to the and models. The U-value is of minor importance for the saved cooling. A limitation in the light transmittance should also be included. In figure 2 the glazings are equally distributed in all the directions. In figure 4a, 5% of all the glazed area is oriented towards the south, and the rest equally distributed to the north, east and west directions, for the base case house. It is seen that such change give rise to an increased A coefficient and vice versa if 5% of the glazing is situated to the north (figure 4b), compared to figure 2a. This means that if the glazing fraction is concentrated to the south, for this cold location, the heating energy rating depending on the g-factor is increased. If the glazing area is concentrated to the north the heating energy rating depends less on the g-factor. /m 2 yr) Saved heating energy (kwh Stockholm, 5% south facing glazing *A=45, B=11 g=5% /m 2 yr) Saved heating energy (kwh Stockholm, 5% north facing glazing *A=32, B=11 g=5% Figure 4: The heating energy rating in Stockholm for the base case house with 5% glazed area to the south (figure 4a) and north (figure 4b) facing direction, respectively. ISES 21 Solar World Congress 197

6 For the cooling energy rating there is also a dependence of the glazing orientation, as illustrated in figure 5. However, in figure 5a it seen that for 5% south facing glazed area the cooling energy rating is similar to when the glazing is equally distributed around the house (figure 3a). This is because the extra throughput through the larger south facing glazings is balanced by the reduced throughput through the east and west facing glazings. With 5% north facing glazed area (figure 5b) the slope of the cooling rating versus the g-factor is decreased, compared to figure 3a. Saved cooling energy (kwh/m 2 yr) Madrid, 5% south glazing area g-factor (%) Linear* *C=2 U-value difference Saved cooling energy (kwh/m 2 yr) Madrid, 5% north glazing area g-factor (%) Linear* *C=17 U-value difference Figure 5: The cooling energy rating in Madrid for the base case house with 5% glazed area to the south (figure 5a) and north (figure 5b) facing direction, respectively. All the results above indicate that it seems possible to use a linear model for the heating and cooling energy rating of the glazings. However, the coefficients A and B depend on the type of building and the climate zone. Figure 6a illustrates the size of the errors when the same linear heating energy rating for the base case is used for the low energy house and the house with 5% south facing glazing. The errors are of the order of 2 kwh/m 2 yr when comparing a glazing with g=5% and. For the cooling energy rating in Madrid the errors are of the same magnitude if the base case cooling energy rating is used for the cooling energy rating for the low energy house, figures 3a and 3b. In figure 6b it is illustrated how the results differ between the climate zones. The difference between the Stockholm and the Berlin climate when going from a glazing with U=2.9 W/m 2 K to a glazing with U=.91 W/m 2 K is of the order of 3-4 kwh/m2yr. The same difference, but between the Stockholm and the Madrid climate is about 1 kwh/m 2 yr Stockholm base case, A=37 Low energy house, A=3 5% south facing glazing A=45 g=5% Base case house Stockholm A=37, B=11 Berlin A=33, B=9 Madrid A=4, B=6 g=5% Figure 6: Errors, using the base case linear heating energy rating model for different types of houses (figure 6a). Differences using a linear model in the three different climates (figure 6b). ISES 21 Solar World Congress 198

7 4. DISCUSSION AND CONCLUSIONS In this paper we have compared the heating and cooling energy rating for glazings in different houses and European climates. The simulations and comparisons indicate that: There seem to be a linear response of the energy rating versus both the U-value and the g-factor. The model is in good accordance with the model, but it requires the balance temperature and hourly climate files. A linear model of the Danish type is possible but the coefficients A and B vary for different buildings and climates. Coefficient A decreases with better buildings and with higher glazing fraction to the north. Coefficient B is basically equal to the degree-day number for the climate zone and thus decreases for warmer climates. Using the same coefficients in the Danish model, within the same climate but for different types of buildings does not seem to yield very high errors (figure 6a). This means that for the heating energy rating a linear model for a base case house may be acceptable enough to use for all residential buildings within that climate zone. The difference between the climate zones (figure 6b), illustrate a maximum difference of about 4 kwh/m 2 yr between Stockholm and Berlin, and a very high difference between the Stockholm and the Madrid climate. This indicates that a climate zone should be less than the distance between Stockholm and Berlin in size. It may even be acceptable with as few zones as one for Spain and Portugal, one for France, one for Germany and Benelux, and one or two for Scandinavia, but probably not larger. For warmer locations, it also seems possible to use a cooling energy rating of the form -Cg, where the coefficient C decreases for poorer buildings and for buildings with higher glazed fraction to the north than for the base case type (Figures 3 and 5). A limitation of the lowest allowed light transmittance should also be included. For the Madrid climate the cooling energy rating differed about 4 kwh/m 2 yr when going from to for different type of buildings (figure 3). For both the heating and cooling energy rating the A and C coefficients could be reduced with some shading factor because the actual total g-factor is almost always reduced in some way by different types of shadings in a real building. This also makes the correlation between the actual energy performance and the energy rating uncertain. Additional work needs to be made when it comes to the differences between climates, both for cooling and heating. Furthermore, more building variations should be investigated. For instance, office modules with glazing orientations only to the south or north should be studied for different glazed areas, internal loads, etc. When a WERS is going to be established it is necessary to define the climate zones and which type of buildings that should be used for base case. Maybe one house type and one office type would be sufficient to serve the purpose. Category 1 is obviously not sufficient for energy rating. A linear model (category 2) may be accurate enough, but the heating and cooling coefficients needs to be evaluated for the building types and climate zones. The model (category 3) seems to give a correct rating but requires the balance temperature for the building, hourly climate files and software. Furthermore, this model gives the possibility to compare glazings with different angle dependent transmittance properties 15, something that is difficult to do with category 2. This can be of importance since a glazing with a normal transmittance of 75% does not, normally, have the same angle dependence profile of the transmittance as a glazing with a normal transmittance of 25%. Category 4 can of course be used 7 but requires all building and climate data and thus some kind of systematization or categorization of buildings in order to be applicable. ISES 21 Solar World Congress 199

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