Part Three: The measurable effects of disregarded user behavior

From: Smart office buildings – curse or blessing? Human needs and “smartification”

How can robustness analysis be used to assess the impact of incorrect assumptions in building design processes? What empirical data is available? In contrast to the first parts of this article series “Smart office buildings – curse or blessing?”, the focus in this article is not on human needs and behaviors, but on their impact on energy consumption.

Today, newly constructed and renovated buildings are highly optimized in terms of energy efficiency, at least as far as economic efficiency allows. This involves using a model to forecast the energy consumption of a planned building. Models are always more or less detailed representations of reality. Often, assumptions have to be made, e.g., about the future intensity of use. This is necessary and correct, because otherwise no statements at all would be possible about a building that has not yet been completed.

However, deviations from the assumptions or input parameters of the calculation do not lead to a proportional but to a non-linear change of the energy consumption. This is interesting because buildings react differently to changes in the input parameters, depending on their construction and technology system. The following studies show that highly technical and efficient buildings tend to react very sensitively, small changes in the input parameters lead to a strong increase in energy consumption.

Robustness analysis provides a method for evaluating the effects of assumptions made and their possible deviations in reality.   

The following paragraphs first explain the context and then illustrate how this method can be used to assess the impact of incorrect assumptions regarding human behavior in buildings.

For an initial, general assessment of the effects, however, we do not yet need complex analyses, but can rely on statistical data on measured energy consumption and the energy requirements previously calculated in the planning.

The consumption- / demand deviation

First, unconsidered human behavior leads to a consumption/demand deviation due to rebound effects. Empirical surveys show that the measured energy consumption of highly efficient buildings is approx. 5% higher than the energy demand forecast for the same building according to the German Energy Saving Ordinance (EnEV). This means that more energy is consumed than calculated in advance. In contrast, the average consumption of less efficient buildings is more than 25% below the values determined in advance. This is shown in the following figure, in which the consumption factor (calculated as measured consumption divided by calculated demand) for the various efficiency classes of the energy certificate are shown (insulation capability of existing buildings in Germany). In particular, people reduce the effect of technology through their behavior – in better insulated buildings, for example, people heat more uninhibitedly, as this is less expensive (or less harmful to the climate) than in an uninsulated old building (rebound effect).

In addition to indoor temperature, hot water use, ventilation behavior, night-time reductions and partial heating were identified as further factors through which human behavior influences real energy consumption.

Let us now turn to robustness analysis, with which the TU Munich takes a different approach to estimating the effects of fluctuations in the input factors – such as human behavior.

Robustness in construction

The term robustness is used in many disciplines – described, for example, as “fault tolerance” (computer science), “on-time production despite undesirable influencing variables” (production), “reproducible and standardizable results despite variability of the sample to be analyzed” (diagnostics), or even “evolutionary stability of a certain trait” in biology.

In its research series “Einfach Bauen” (“Simply Build”), the Technical University of Munich uses the term “robust optimization”, in which “a system in the sense of a design or process (technology, product) is said to be robust if it is minimally sensitive or insensitive to the fluctuations of the input factors”. It explains further that, “The graph [see title image, author’s note] shows the difference of a global and a robust optimum – reduced to two parameters: the uncertain input parameter (Δx) and the target variable of the function (Δy) influenced by it. The result of the global optimum (Δy1) is considerably influenced by the fluctuation of the input parameter. In contrast, the same scatter of the input parameter (Δx) in the robust optimum (x2) only slightly affects the result (Δy2).” (Einfach Bauen 1)

Uncertain constraints in robustness analysis

The TU Munich first determined the energy consumption for individual rooms in differently constructed buildings for a “best case”. The six construction variants considered are:

• Four Simply Build variants:
  • Masonry mono-material,
  • Lightweight concrete mono-material,
  • Timber hybrid, and
  • Timber mono-material (with solar protection),
• a standard variant according to EnEV 2014/16 (“standard”), as well as
• a low energy/passive house standard variant (“low energy”).

Subsequently, contrary to the usual (normatively correct) assumption, the following four input parameters were classified as “uncertain” and “extremely” modified for energy consumption determination:

• the climate,
• the user behavior,
• the internal gains or loads, and
• the solar protection. 

Even if the TU Munich speaks here of “extreme” changes, the scenarios seem quite realistic: milder weather due to climate change until 2045, people do not ventilate permanently, internal loads doubled to approx. 20W/m² (for example laptops and screens) failure of solar protection.

In terms of human behavior, the criteria “people do not ventilate / permanently”, but also “failure of the solar protection” are interesting, since “failure” is more or less synonymous with “overridden”. 

While – as expected – the heating required by continuous ventilation increases for all construction variants, the effects are highest for a “low-energy house”. This variant is equipped with a mechanical ventilation system and is therefore the most efficient in the normative calculation. However, an open window can disrupt this concept to such an extent that the energy consumption in this scenario exceeds all other variants. (See Figure 2) (Einfach Bauen 1)

In addition to energy consumption, the robustness analysis conducted by the Technical University of Munich also analyzed comfort in terms of indoor temperature in summer (“over-degree hours”) (see Fig. 3). Here, additional internal loads (e.g. in the form of electrical devices such as laptops and monitors) have the greatest impact. High-tech buildings are also most sensitive to this, just as they are to the failure of solar protection. (Einfach Bauen 1)

Application of robustness analysis in the building design process

Robustness analyses can provide a valuable contribution to sustainable building planning and design. In the context of thermodynamic building simulations, which are common for larger multi-family and non-residential buildings, many of the criteria considered here can be varied in a straightforward manner. When well prepared, the results enrich decision-making templates for building owners and can thus raise awareness of the subject.

Conclusion

Assumptions have to be made in the building design process. Many of these assumptions have a decisive impact on the basic construction method and selected technical equipment. The robustness analysis brings us one step closer to our goal of being able to safely maintain energy efficiency and comfort in buildings at a high level. At the same time, we can evaluate our assumptions or their possible deviations with regard to their effects and correct them if necessary.    

The effects of unconsidered human behavior on the energy consumption of buildings are statistically around +5%/-25%. The TU Munich shows that “extreme” behavior can lead to a doubling or halving of energy consumption, depending on the type, and that high-tech buildings are the most sensitive. A similar picture emerges when considering summer thermal insulation, one of the main criteria for comfort.

If one wants to compare the results of this empirical determination with those of the robustness analysis in detail, some inconsistent factors or criteria are noticeable. In the robustness analysis of the TU Munich, indoor temperature, hot water use, night-time reduction and partial heating are missing, while ifeu/Beuth did not consider internal loads and the failure or override of the solar protection. Completing these criteria could be part of further future studies.