Great Windows Have the Greatest Influence on Energy Performance

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This article exposes why great windows are so important when energy efficiency is a priority. The analysis was performed as part of research for the book, Energy Free: Homes for a Small Planet, published in December 2009. Written by architect and green building advocate Ann Edminster, she documents how windows in the same house will perform in three different climates.

Contractors should take from this article that the total R-value of the window assembly is much more important than the R-value of C.O.G. (center of glass). This R-value of the total window assembly gives the best indication of how well the window will halt infiltration as well as slow the transfer of conductive heat through glass.

Finally, this analysis is proof that great windows are the most important energy saver when compared to building orientation, wall insulation, roof insulation, window area, and HVAC equipment efficiency.

In all but one scenario modeled, window R-value (the resistance to conductive heat flow) had the greatest influence range of the variables studied. High R-value windows are likely to provide excellent performance benefits in many projects, particularly those with a high window-to-floor area ratio.

In order to illustrate the variable significance of a range of building energy features based on building type and climate, a number of simulations were done on two different building types, a suburban detached single-family home and an urban zero-lot line single-family home, shown in the photos below.

Both buildings are located in the San Francisco Bay Area. However, they were modeled in three cities to test the influence of different building characteristics in these three very different climates:

San Francisco: 2,700 heating degree-days (HDD), 100 cooling degree-days (CDD); heating-dominated, DOE marine climate zone

Palm Desert: 800 HDD, 4,200 CDD; cooling-dominated, DOE hot-dry climate zone

South Lake Tahoe: 7,800 HDD, 40 CDD; heating-dominated, DOE cold climate zone

For each building type and location combination (a total of six scenarios), a range of values for the following seven parameters were modeled; each parameter can influence energy performance:

• Orientation – each building was rotated through 360 degrees in 45-degree increments

• Wall insulation – values ranging from R-13 to R-43 *

• Roof insulation – values ranging from R-19 to R-74 **

• Window area – as-built, 20 percent more, and 20 percent less

• Window U-values – best and worst available values, U-.09 (R-11) and U-.87 (R-1) respectively

• Window solar heat gain coefficient (SHGC) – best and worst available values, 0.2 and 0.7 respectively

• HVAC equipment – gas furnaces, heat pumps, and combined hydronic with a range of efficiency values

* The “real” value is U-0.023 for the wall assembly, factoring in the percentage of framing and all other layers. The insulation consists of a layer of R-14 continuous rigid insulation and R-40.5 cavity insulation (foam plastic insulation, R-3.6/inch in 2×12 cavity).

** Simplified as for wall insulation.

The values chosen for analysis were intended to represent a range of feasible options for the homes in question. Not all variables tested would represent realistic options for retrofit projects. For example, altering the orientation of an existing home is not a viable retrofit option, and it is highly improbable that 2×12 wall construction would be considered as a retrofit alternative, except perhaps in the most extreme climates.

The modeling yields source energy values (expressed in KBtu/sf-year) for both the standard design (i.e., minimally code-compliant) and the proposed design (i.e., the same home with some feature or features altered). The modeling was done by changing the base case, altering one variable at a time, through the range of values described above for each, and recording the change in source energy for the proposed design as a percent better (or worse) than the standard design (code minimum). The range of influence of each variable was then determined by answering the question: by changing a specific variable or building feature, how much is it possible to affect the source energy use of the design? This was established by calculating the difference between the lowest and highest percent changes in energy performance among all values tested for the feature in question.

For example, the Urban home in San Francisco is oriented at 78 degrees. It was rotated to 123 degrees, 168 degrees, and so forth, around the compass at eight different orientations. The lowest source energy value found was 21.33 KBtu/sf-year at 123 degrees, or 32.3% better than code; the highest source energy value was 22.19 KBtu/sf-year at 33 degrees, or 29.6% better than code. The delta between the low and high values is 2.7%. This represents the range of influence of this particular variable, for this home, in this climate. Changes in orientation only have the ability to influence energy performance within a range of 2.7 percent.

In contrast, the same home modeled in Palm Desert produced energy performance values ranging from 23.4 to 48.2 percent worse than the standard design – an influence range of 24.7 percent. This climate-based differential can be attributed to the home having glazing on only two façades, and the base case glazing SHGC not being optimized for cooling; in San Francisco’s mild climate, the effect of this is much less important than it would be in the heavily cooling-dominated climate of Palm Desert.

The Suburban home produced a much narrower influence range for changes in orientation in all three climates (5.7 percent in San Francisco, 8.0 percent in Palm Desert, 3.9 percent in South Lake Tahoe). Since there is glazing relatively evenly distributed among all four façades of this home, these very different outcomes would be expected.

After conducting all the simulations and recording the range of influence for each variable, the variables were then ranked from high to low in terms of their relative influence on overall energy performance. Figure 3 below shows the influence range for each parameter modeled, and its ranking, with ranking 1 being assigned to the parameter that had greatest influence on overall building performance in each scenario and 7 being assigned to the parameter that had the least influence.


Figure 3. RANGE OF INFLUENCE AND RANKING OF ENERGY VARIABLES

While recognizing that this modeling represents a very limited, non-representative exercise, without applicability to any other specific project, a few of the more notable findings, and their implications, are enumerated below.

  1. Finding: In all but one scenario, window U-value had the greatest influence range (the number one ranking), with values from 22.6 percent to 54.8 percent. Implication: High R-value (low U-value) windows are likely to provide excellent performance benefits in many projects. Note that neither of these projects has a particularly high window-to-floor area ratio; in projects with higher window-to-floor area ratios, U-value will be even more important.
  2. Finding: Window SHGC produced the biggest spread in influence range, with values from 0.3 percent to 87 percent. Its influence is generally high, however, ranked #1, #2, or #3 in four of six scenarios. Implication: SHGC is likely to be quite influential on performance, and is highly climate- and orientation-specific. It therefore should be analyzed carefully to optimize glazing performance.
  3. Finding: Roof insulation had much less influence than expected, given the emphasis generally placed on it; values ranged from 3.0 percent to 8.6 percent, rankings in the bottom three. Implication: Both of the homes modeled were two-story, and one-story homes would be likely to show more influence from changes in roof insulation. It also should be noted that the minimum level of roof insulation modeled was R-19, and since the law of diminishing returns dictates that the effect of each added increment in R-value has less impact, this should not be interpreted to suggest that less roof insulation would represent a good economy. Not so.
  4. Finding: Wall insulation, by contrast, is relatively high in influence, with rankings of #2 or #3 in all but one scenario, and influence range values between 14 percent and 25 percent. Implication: increasing values in wall insulation deserves careful consideration, with particular attention to the inclusion of continuous insulation, which has roughly twice the impact of a comparable R-value added to the wall cavity.

A project team should do building-specific energy modeling to uncover the unique effects of different characteristics on the building design and to establish priorities for incorporating energy features. Also, while energy modeling is a very valuable guide, the benefits of innovative design approaches may not be fully captured by a model. A critical evaluation of results by an experienced, energy-savvy home design professional will be invaluable in determining the best features to incorporate in an energy-efficient new home design.

Conclusion

While this analysis does not address every climate, design, or other circumstance, it provides ample evidence that windows with thermal resistance of R-5 or better can offer extremely good value, in both new homes and existing homes where energy performance is a priority for the homeowner, developer, or design team.

About the Author

Ann V. Edminster, M.Arch., LEED AP+ Homes

Ann Edminster is a recognized national expert on green home design and construction. She is a principal author of the LEED for Homes Rating System and consults to builders, owners, developers, supply chain clients, design firms, investors, entrepreneurs, non-profits, and public agencies. She sits on advisory boards of a number of green building companies, including Serious Energy, is a contributing editor to GreenBuildingAdvisor.com and Ultimate Home Design, and has consulted on numerous LEED Platinum Home projects that are targeting multiple high-performance ratings and certifications, including net-zero energy. She is a widely acclaimed green building educator and facilitator and has co-organized three international green building conferences, the most recent of which was Build Well 2010. Her new book, Energy Free: Homes for a Small Planet, offers guidance on net-zero energy home design. Her own home remodel won a green building award in January 2007.

 

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