The global Passive House community is converging on Darmstadt, Germany, this week to celebrate the 25th anniversary of the Kranichstein Passive House and the 20th anniversary of the International Passive House Conference.
A great deal has changed in the world of building performance since the Kranichstein Passive House was first built. Much of that change we can attribute directly to the influence of this iconic building and the spread of the Passive House standard (which was derived from the efforts of Dr. Wolfgang Feist and others to model and optimize this particular building.) Triple-pane windows are now readily available in both Europe and North America, as are heat-recovery and energy-recovery ventilation units. A growing awareness of the benefits of airtight construction and detailing are also largely attributable to the global spread and influence of the Passive House standard.
My motivation for studying the Kranichstein building was largely coincidental. It grew out of my involvement in developing an optimization tool to help Passive House designers and energy modelers. The tool is called , or Predictive Design Technology for Passivhaus. This led me to dig a little deeper into how the Kranichstein project was designed and modeled.
My explorations revealed much about the evolution of the Passive House standard and my hope is that both the development of this optimization tool and information it has uncovered will be both interesting and helpful to others.
Since beginning my journey into the complexities and nuances underpinning the Passive House standard, it became apparent that the habit of optimization for building design is not common practice. (Optimization is the search for the best combination of components, areas, and assemblies to achieve the comfort and performance criteria of the Passive House standard in an economic manner.)
Many projects that have met the Passive House standard have clearly done so by being “shoehorned” into fitting the criteria, often by throwing large sums of money into expensive assemblies and components, rather than by optimizing the design to creatively meet the performance criteria. I, too, am guilty of this. It’s easy to not take the time to review a project more carefully to search for opportunities to improve my designs, particularly in a benign climate like California. Once you’ve managed to make your building comply with the performance metrics of the standard, why explore further options?
When approached by a team of software engineers, looking for an opportunity to use their optimization algorithm for building energy modeling, I was intrigued. The PHPP had yet to offer an optimization process or a clear method for easily enabling building designers to quickly and efficiently explore thousands of iterations. The opportunity to help create this was highly appealing.
Quite conveniently, every copy of the PHPP software is supplied with a complete example project file of the Kranichstein Passive House building as the example project. We used this example file to develop the early versions of our optimization tool. It gave us a deep appreciation for both the complexity of the Passive House Planning Package and the effort that was taken in the design of the Kranichstein project.
The evolution of our optimization tool led us to develop two functions. The first is the ability to explore a range of options for a number of single variables whilst all other variables remain fixed. We’ve named this “multi-variable optimization.” This option results in a simple graphic output, plotting each variable’s parameters on the X-axis against four Y-axis graphs of Heat Load, Heating Demand, Cooling Demand, and Primary Energy.
The second user option enables the user to select a number of variables, define their individual parameters, and then have these all run simultaneously against each other to calculate the best performing combination of each of these variables against each other. We’ve called this option “full optimize.” The output of this run is a sine wave graph/plot, combined with a downloadable data table, listing their combinations numerically.
After looking at the option to allow users to select any cell within the PHPP to explore as a variable, we elected to provide a carefully curated a set of variables that we found to have the largest impact on building performance. These would help our users to quickly and efficiently optimize their designs without wasting time on selecting insignificant and variables that would not make improve building performance.
What we found at Kranichstein
When running the Kranichstein project through our optimization tool, it became immediately apparent that every single one of the variables we selected was perfectly and precisely optimized to the Passive House standard’s heat load target of 10 W/m².
It also became apparent that its more widely known certification alternate, the heating demand target of 15 kWh/m²yr, was not as significant. Despite there clearly being a correlation between the 10 W/m² target and the heating demand target of 15 kWh/m²yr, it was clear that heating demand played a minor role in the design and selection of the Kranichstein assemblies and components.
All assemblies and components used in the Kranichstein building hewed very closely to the 15 kWh/m²yr target, with the exception of the specific window areas for each elevation. While the sum total window surface area of the building readily tracked both the heat load and heating demand targets, individually the total window areas for the south, north, and west orientations nailed the heat load target, but were clearly well below the 15 kWh/m²yr heating demand metric.
Looking at other projects
This exploration of Kranichstein in isolation wasn’t as interesting until I applied the insight it gave me to the design and optimization of other buildings. I’d always been troubled by the post by the owner of the Blue Heron EcoHaus in Saskatoon, published at Lakesideca Advisor.
In this post, Kent Earle generously shared the specifications and Hot2000 energy model predictions for his project. While it seemed like he may have originally hoped to meet the Passive House standard, his project did not meet the standard’s rigorous target metrics. He conceded that while building to the Passive House standard in Saskatoon may indeed be possible, “you’d be looking at making huge financial investments and sacrificing comfort” to achieve it.
From looking at his building design, I wasn’t so convinced. Given the fact that much of the research that undergirds the calculations within the PHPP has been derived from projects (for examples, the Saskatchewan Conservation House) located in right in his back yard and in other climates very similar to Saskatoon, I undertook some exploration of my own.
The first run of the PHPP model I developed for the Blue Heron EcoHaus confirmed similar results to what Mr. Earle had shared from the Hot2000 model run on his building. My first stop to find clues on how this building could have been optimized to meet the Passive House standard took me to the Energy Balance Graph. This gem is carefully hidden on the annual heating sheet of the PHPP. It readily revealed three obvious opportunities to optimize this design.
Windows were easily the biggest energy losers in this design, with 23.5 kWh/m²yr literally being thrown out the window. Ventilation losses via envelope leakage and recovery efficiency were next at 14.6 kWh/m²yr, closely followed by losses through the exterior walls to ambient of 14.3 kWh/m²yr.
By running this project through a full optimization run, selecting the individual assemblies, total window surface area, average window U-factor, airtightness benefit, and ventilation recovery efficiency, I was able to quickly determine how to meet the Passive House standard for this project. The results indicated that my best opportunities lay in:
- Reducing the total window surface area from 30.45 m² to half that amount.
- Increasing the thermal performance of the windows from 1.24 W/m²K to 0.62 W/m²K.
- Increasing the airtightness of this building from 0.6 ach50 to 0.2 ach50.
These three improvements allowed a reduction in the walls, roof and sub-slab insulation. This reduction in the thickness of the walls would have provided a few additional feet of interior usable space. Most significantly, the reduction in total window area would have halved the window costs on this project – an expense that is typically one of the highest component costs for any high-performance building. In addition, halving the window area would drastically increase the interior comfort of this building, and most importantly remove much of the very real risk of overheating for a project in this location with such a large number of unshaded east and west-facing windows.
As with the Kranichstein building, once the three changes listed above were implemented, this project met the Passive House certification criteria via the heat load target metric of 10 W/m². It also hypothetically shared the same airtightness target of 0.2 ach50. (This more stringent airtightness metric makes good sense for buildings in climates with extreme temperature differentials such as Saskatoon, where sub-zero winter temperatures create a massive pressure drive through any holes in the building envelope.)
Alternate design choices in Saskatchewan
The optimization options I found above that hypothetically allow the Blue Heron EcoHaus to meet the Passive House standard are only three of the many available to those who choose to explore their options. It was highly gratifying to discover that the early pioneers of superinsulated homes in Saskatchewan had recommended many of the same choices indicated by the optimization runs of both the Kranichstein Passive House and the Blue Heron EcoHaus.
In a paper submitted by Robert S. Dumont, Robert W. Bresant, Grant Jones, and Rod Kyle and presented at the SESCI Conference in 1978 in London, Ontario, just up the road from the Blue Heron EcoHaus, the authors make the following recommendation in their summary: “For conventional light-frame construction using gypsum wallboard as the interior finish, and no additional thermal mass, one should limit the south-facing window area to less than 8% of the floor area of the dwelling. Additional window area will only result in excessive heat gain during the day and too rapid temperature falls at night.”
They also offer an alternate to the quad-pane windows I’ve modeled in my hypothetical design above. “Thermal shutters can be of significant value in reducing both the heat loss from dwellings and in moderating the temperature falls at night in well insulated dwellings.” Notably, the Saskatoon project monitored in this study had quadruple-glazed widows of almost the same total 32.5 m² window area as the Blue Heron EcoHaus.
A great many lessons and conclusions may be drawn from the study of these three particular buildings. By virtue of the optimization tool that I’ve been able to utilize, I’ve isolated what I believe to be a clear thread of links that connect them:
- The Saskatchewan Conservation House focused on managing losses rather than maximizing gains. It accomplished this by not over-glazing the house and by adding exterior insulating shutters. This resulted in a building that experienced very even interior temperatures – a hallmark of a well-designed Passive House.
- When this same concept of managing losses was applied to the Blue Heron EcoHaus, it was able to hypothetically meet the Passive House standard via the 10 W/m² heat load certification metric. In Saskatoon, at this heat load target, the heating demand number fell between 25 and 27 kWh/m²yr, depending on the specific design choices made for the project.
- The Kranichstein Passive House was clearly optimized and designed around the 10 W/m² heat load target. For that project location in Darmstadt, the heat load target resulted in a heating demand number of 14 kWh/(m²yr), indicating that the heat load target is the preferred target metric for optimization in more varied climates.
Bronwyn Barry is a Certified Passive House Designer and the co-president of the North American Passive House Network.