Qualitative experiments
One purpose of this study is to show how adapted dynamic co-heating tests4 could more
advantageously (exhaustively, accurately, robustly, rapidly) identify the dynamic characteristics of a building envelope model thanks to their specificity [9] such as exciting the system with a broad dynamic content in order to emphasize the various time constants of the system, to reduce the measurement time, and avoiding high peaks in the residuals between the measured and predicted outputs of the identified model, thanks to the smooth characteristics of the data, which makes statistical analysis and validation easier and unbiased.
Next to this, the developed experimental infrastructure was also able to maintain homogeneous temperatures, especially during power control tests like the multi-sine test, and even during sunny days, thanks to an adaptive spread of the power in the different zones of the building, using pulse- width modulation on the individual relays that control the electric heaters.
Finally, in order to focus the analysis on the transmission part of the heat loss coefficient, which was the part of interest and supposed to be replicable across experiments whatever the weather/wind conditions, the contribution of the exfiltration part of it (obtained by multiplying the air change rate by the temperature gradient) has been continuously monitored thanks to tracer gases controlled by a multi-channel Bruël & Kjær unit, following a constant concentration scheme.
Descriptions of the design of thermal test sequences and of the infrastructure assembled to implement them were firstly given in [3]. We describe here several aspects of it but not exhaustively. The infrastructure of the experiments consists of “kits” installed in each zone and connected to the central control & acquisition unit. The communication between the main unit and the kits happens via a serial port (see Figure 2).
Figure 2: Electric fan, heater, decentralized module
4 The term « co-heating » is used here in the broadest sense and is not limited to measurements under steady (thermostatically-controlled) indoor temperature.
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The decentralized modules are responsible for controlling the heaters (pulse-width modulation), measuring the power of both the air mixing fan and heaters, and measuring the temperature of the zone. The communication happens continuously with instructions updated every 100 seconds. If the required power in a zone is e.g. half the installed nominal power in that zone, it will be on during half the cycle time. Several corrections are applied to account for the presence of the air mixing fan (constant power), for the non-linearity’s of the heaters, the potential drift in time of the nominal power, etc. A robust system is implemented through a hybrid closed-loop/open-loop control system. Other modules are used to monitor the outdoor environmental conditions, among others. All the boxes are eventually connected to the central control and acquisition node. A software user interface (Figure 3) is developed so that experiments can be parameterized, started or even adjusted, and that all running variables can be monitored remotely.
Figure 3: Front panel (user interface) of the NI LabView control & acquisition program
The developed infrastructure merely allows for dynamic and smooth heating sequences such as X- sines signals but it also significantly improved the temperature homogeneity within the building. In the example illustrated in Figure 4, the total targeted heating power is spread among the 7 zones of the measured building. The graph shows a complete (clear sky and sunny) day from midnight to midnight, where the daytime has a white background and night time a light blue one.
Measured powers (W) Targeted powers (W)
Figure 4: Heating power adaptive spread under dynamic measurement and correspondence between target (on the right) and measured (on the left) individual and global power
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We first see on the right graph a good superposition of the total measured power (in pink) with the total target power (in light green) that varies between 1000W and 4000W (right scale). Individual powers (left scale) are distributed adaptively in order to maintain a homogeneous temperature in the building. The zone n°1 (Pm1, in red) is the largest of the house and logically requires a relatively high heating power during the night, especially because of the large glazing. Nevertheless, these are oriented to the South. Hence almost no power is required in that zone during sunny days because of large solar gains. A major part of the remaining heating power is hence self adaptively directed to zone 5 (Pm5, in orange), a mid-size room oriented to the North-West which does not receive solar gains.
In Figure 5 and Figure 6, the evolution of the individual room’s temperature during two consecutive days can be seen. In Figure 5 the adaptive power spread function and the air circulation fans are turned off at the end of the morning of the first day. The circulation fans (only) are turned back on at the end of the morning of the second day. In Figure 6 the adaptive power spread function and the air circulation fans are always turned on.
Figure 5: Temperature spread in the building under static distribution of heating power when doorframe fans are cut-off (centre) and after their reactivation (right)
Figure 6: Temperature spread in the building under dynamic distribution of heating power with doorframe fans always activated
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It can clearly be seen by comparing both figures that the second experience yields more homogeneous temperatures. It can also be found from the first experience that circulation fans (including fans placed at doorframes) are much less effective in reaching homogeneous temperatures than the adaptive power spread.
Note that temperature heterogeneities then sometimes reached up to 4K between rooms, while the gradient between the “average” indoor and the outdoor environment was around 16K. In case such conditions persist during the test, the accuracy of the aggregated indoor temperature can be questioned as well as the accuracy of the identified heat loss coefficient to which errors could propagate. The amount of deviation in the results due to that issue has not well been quantified and requires one-to-one comparable data sets, either obtained through tests in laboratory conditions or through simulation. We nevertheless have proposed a global index to characterize temperature heterogeneities [3] and suggest limiting them as much as possible.
The building on which the experiments were implemented (see Figure 7) is a detached house located on a small hill (Lat. 50°41’ N, Long. 4°31’ E, at BBRI facilities) and exposed to the winds with ventilated attic and cellar. The effective internal surface area is 86m² and the ground floor height is 2.55m, the internal volume being 220m³. The external dimensions are 8.2m by 13.12m. The exterior walls are cavity walls insulated with 10cm of mineral wool. The ceiling is made from wooden rafters insulated with mineral wool. The floor is made from concreted and is weakly insulated. The living room is oriented SSW and is heavily glazed. The windows are equipped with double glazing and wooden frames.
Figure 7: General outside view of the building and inside view of a room
The U-values of the envelope components are expected to be about 0.38, 1.30, 0.21 and 0.39 W/m²K respectively for the vertical walls and windows, for the roof and for the floor. This leads to an overall UA-value (HD according to ISO 13789) of about 110W/K. Thermal bridges are estimated to be about
40W/K such that the transmission loss coefficient reaches 150W/K. It is important to note that this value is not extremely precise since several design parameters are unknown and the building is already 30 years old.
The ventilation system is sealed during the measurements. The air tightness of the building is 4.3 h-1
under 50 Pa. The standard ventilation loss coefficient hence reaches 21 W/K (0.34 V n50/15), which
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coefficient of about 170 W/K. The true heat loss coefficient of course depends on the actual rate of air change (and temperature gradient), which is continuously measured during the experiments as explained earlier.
Data sets acquired
The acquired data sets, illustrated in Figure 8, are a temperature set-point (Co-heating), a (smoothed) PRBS test sequence, a multi-sine (X-sines) test sequence, the hybrid (SMART) test and the transient (QUB) test. For all sets, 5min sample time data files are available. Outdoor climate sensors installed on or near the building include air temperature and relative humidity, wind speed and direction, vertical global solar radiation on the East, South and West façades, as well as horizontal global and diffuse solar radiation. Individual building zone temperatures are aggregated into a unique temperature.
Figure 8: Various measurement data sets. Temperature set-point co-heating (top), smoothed PRBS and X-sines (center), hybrid and transient (bottom)
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A unique variable has also been obtained from multi-zone measurements for the air change rate. The latter is illustrated in Figure 9, showing center moving 4h and 24 averages of the crude data relative to a hundred days of measurements in total.
Figure 9: measurement of air change rate (top), temperature gradient between the indoor and ambience (bottom), and calculated ventilation heat loss (middle)
It can be seen that, even based on daily averages, the heat losses associated to the exfiltrations (called here the ventilation heat losses) are varying over the time. While the standard air change rate is 0.3h-1 for our building, the actual one, based on the 4h averages varies between 0 and 3.1h-1, and
the global average value is 0.4h-1, which correspond to 29W/K or 17% of the total heat loss
coefficient. Notably, ventilation losses during periods with strongest infiltrations/winds are of the same order of magnitude that there are only transmission losses and can hence propagate errors towards the identified heat loss coefficient if not correctly accounted for.
Additionally data related to the measurement of the U-values and Λ-values (surface temperatures and density of heat flow) of three wall components (floor on cellar, external walls and ceiling under attic) are also available. Although these are not further developed here, we briefly note that results obtained in accordance to the WD/ISO 9869-1 standard are consistent with the expected characteristics given above and that standard deviation of results from the 5 experiments are small.
0 500 1000 1500 2000 2500
-2 0 2 4
4h and 24h average data
a ir ch a n g e r a te [ h -1 ] 4h 24h 0 500 1000 1500 2000 2500 -2000 0 2000 4000 ve n ti la ti o n h e a t lo ss [W ] 4h 24h 0 500 1000 1500 2000 2500 0 10 20 30 time [days] te m p e ra tu re g ra d ie n t [K ] 4h 24h time [hours]
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