2.1. Measurement Methodology
Using different measurements, we examined the twelve schools in order to compare the comfort parameters. In each school, we chose two exemplary classrooms with identical orientations and volumes. To measure the classrooms’ long-term thermal and hygienic comfort, we used instruments from the manufacturer IC-Meter, that recorded, in 5-minute intervals, indoor air temperature, humidity, CO2
concentration, and the sound level. In addition, they collected data about the outside air temperature from the nearest weather station. Although the measurement period for each of the schools studied is not identical, we were careful to collect data over several months from both cold and warm seasons in 2016. The sensors were mounted on the inner wall opposite the facade side at a measuring height of 1.2 m. It was possible to determine the periods of use by means of the recorded sound level data, which provided information about room occupancy. The included scatter plots in Figures 5,7,8 show five-minute values exclusively during the periods of occupancy. The CO2
concentration should provide objective information about the air quality, but it only serves as an indicator for other air contaminants: odors or volatile organic compounds, so-called VOCs, cannot be detected, or only with great difficulty. The CO2
concentrations typically measured indoors are not dangerous to humans, but they do correlate with the subjective perception of “stuffy, stale” air. To represent this, we use two graphics (Figures 5 and 6). The cumulative probability indicates the minimum CO2
concentration. The two lines indicate outside air temperatures above and below 12 °C. This distinction is based on the assumption that users no longer manually open the windows when it is below 12 °C, due to draught. The second graph shows the CO2
concentration in relation to the outside air temperature. You can also see the four categories of recommended limit values according to the German standard for ventilation of non-residential buildings DIN EN 13779 [4
Category I is up to 400 ppm above the outdoor CO2 concentration, which is uniformly set at 400 ppm in all schools. Category II is up to 600 ppm above the outdoor concentration, Category III is up to 1000 ppm above, and Category IV is greater than 1000 ppm above.
Air temperature and humidity provide information about thermal comfort. The radiant temperature (the average surface temperature of the enclosure surfaces) was disregarded. The mean of the ambient air temperature and radiant temperature gives the so-called operative temperature, which corresponds to what people perceive. Determining the radiant temperature would be very complex, and in any case, the difference between the air and radiant temperatures in the well-insulated schools is relatively low. This slight vagueness is, however, within an acceptable range because a proper consideration of the operative temperature would not change the evidence of the investigations. A complex determination of the radiant temperature would thus provide no additional knowledge in the context of our investigations. Two different graphs display thermal comfort: the first (Figure 7) shows the indoor air temperatures and presents these in relation to the prevailing outside air temperatures. The area between the lines is the recommended range of indoor air temperature, which increases to a certain extent as the outside air temperature increases. This area is based on the DIN EN 15251 [5
], even though, in this case, what is shown is not the operative air temperature, but the indoor air temperature we measured. In Figure 8, the second graph shows the indoor air temperature in relation to indoor air humidity. The recommended comfort range according to DIN 1946-6 [6
] is between the grey lines. Here, the recommended temperature is independent of the outside air temperature between 20 and 26 °C. By definition, the relative humidity should ideally be between 30% and 65% and should not exceed the absolute humidity value of 11.5 g/kg. Using the software program AkuCheck from the University of Wuppertal, we determined the reverberation time in each classroom. This provides information about speech intelligibility, which is particularly important in school construction. The recommended reverberation times at different frequencies, according to DIN 18041 [7
], depend on the volume of the classroom and therefore differ slightly from one school to another. Since we carried out the measurements in unoccupied rooms, we virtually added an occupancy of 25 people in the software. In the graphs, the determined reverberation time is represented by the black line and the gray area represents the recommended reverberation time.
2.2. Simulation Methodology
To evaluate and compare energy consumption, we originally planned to obtain information on electricity and heat consumption from all schools. During the course of the work, however, it turned out that the meter information is not comparable in terms of energy consumption. For example, our sample included all-day schools, as well as schools such as the Gymnasium Buchloe, which have a school kitchen with a canteen, and technologically oriented schools such as the Max-Born- Berufskolleg, where many of the teaching activities consume significant amounts of electricity. In order to be able to directly compare energy performance, we decided to thermally simulate the twelve schools under standardized boundary conditions using the building simulation software, TRNSYS version 17. It was a matter of determining which parameter is most suitable for comparing energy consumption in the “fairest” way possible. Above all, we wanted to make the structural qualities transparent rather than focus on technical parameters such as the efficiency of the municipality’s district heating supply.
Useful energy is principally a matter of structural quality, but it does not take into account the type of heating/cooling system or the power consumption of the system technology. A consideration of the end energy, in turn, would lead to a distorted picture for all buildings that use heat pumps for heating and cooling. As shown in Figure 3
, we redefined the system boundary and introduced the concept of “space energy demand”. This includes the values for heating, cooling, auxiliary energy, and artificial light in kWh/m²a. It simulates a classroom located on the top floor with the appropriate buildings components and building services systems, such as the heating/cooling system, while taking into account all the external energy consumers, such as the ventilation system.
shows an overview of the technical equipment of the schools. With this simulation, it is possible to work out structural and technical differences and, at the same time, to standardize user influences such as teaching times. To do this, we assumed both an ideal user and optimal measurement, control, and regulation technology (MCR), in accordance with the planner’s specifications. For example, we assumed that the user will open the window if the CO2
concentration is higher than 1400 ppm, even if this does not coincide with reality. Thus, naturally ventilated schools perform poorer at heating the space in the simulation than in reality because in the simulation, more heat is needed to warm all the fresh air. We also use an “ideal user” when calculating the electricity demand of artificial lighting. This ideal user only switches on artificial light when it is necessary. In reality, the electricity demand for artificial lighting is certainly higher, but this assumption makes it possible to compare the schools.
Winter thermal comfort was uniformly set to a minimum operating room temperature of 20 °C. The thermal comfort in the summer was not standardized but simulated in accordance with the realized system settings. The heating and cooling systems, including how they are regulated, are also represented in the simulation according to the planner’s specifications. In order to determine the efficiency of the energy supply systems, we used uniform parameters.
In order to include an evaluation of the energy sources, it is possible to account for either the primary energy or the CO2
emissions. Looking at the primary energy gives a distorted picture in the schools that are connected to district heating, because district heating networks often employ waste incineration, which leads to very good primary energy factors. This is why we decided to account for the CO2
equivalent (Table 2
). The factors come from the GEMIS database (Global Emission Model for integrated Systems), version 4.93 [8
]. For schools that are connected to a heating network, we either collected the values directly from the heating companies or determined them on the basis of the data provided by the municipal utilities themselves. Photovoltaic systems that produce electricity for their own use or to feed the grid were not included in the balance sheet for reasons of comparability.
The focus should be on the interaction between the building envelope and the systems technology. The third value for comparison, in addition to space energy demand and CO2
equivalence, makes it possible to consider the energy costs that are required to operate the building (heating, cooling, power supply, and artificial light). To simplify matters, we assumed a flat rate of 10 cents for every kilowatt hour of heat, and 20 cents for electricity. An economic energy analysis certainly shows a definite correlation with the “ecological value.” Both the CO2
equivalence and the operational costs are based on the previously determined simulation results of the space energy demand. To evaluate visual comfort, we performed a dynamic daylight simulation using the Radiance and Daysim programs. This was done in Honeybee, a plug-in for the Grasshopper software. These programs determine the value (sDA = Spatial Daylight Autonomy) of the continuous daylight autonomy (cDA) and provide information on how much of the classroom is lit by sunlight to an illuminance of 500 Lux, as required by DIN EN 15251 [5
]. In the case of school buildings, such as the Berufliche Oberschule Erding, which have an automatic sun protection system, the control system was implemented based on data from the planning office. For schools with manually controlled sun protection, such as the Wandermatte school system, the sun shade has been defined in the simulation to be closed by the user as soon as it is exposed to direct sunlight. Since the elementary school in Munich only has curtains, no sun protection was considered here.