Milan Janak, Slovak Technical University, Bratislava, Slovakia
Currently there are many initiatives in Slovakia both at national and local levels which aim to decrease energy consumption of existing residential multifamily buildings. Careful planning is essential to identify and select a suitable retrofit option or technology for utilisation by a particular building and climate type.
Within the PHARE program for the west-east regional collaboration in the urban energy efficiency, the energy efficiency retrofit of the typical Slovak multifamily building (see figure 1 and figure 2) has been carried out. The building is located in the small town Dolny Smokovec in the northern part of Slovakia.
Figure 2 North view of the building.
VVUPS-NOVA,The City of Bratislava has commissioned a study to provide the following supporting information for their design:
Based on the geometry, material and operation data provided by the client, two principal building models of a representative section of the building were prepared, (1) a base case - as it is today (see figure 3 and figure 4) and, (2) after the retrofit (see figure 5 and figure 6).
A
B
Figure 3. The visualisation of the base case simulation model geometry. A - west facade with surrounding site obstructions;B - west facade of the model; C - east facade of the model.
Figure 4 The wireframe model showing a typical floor of the the base case geometry (local and site obstructions not shown).
A
B
Figure 5. The visualisation of the retrofit case geometry. A - west facade with surrounding site obstructions; B - west facade of the model; C - east facade of the model.
Figure 6 The wireframe model of the attic extension (upper) and typical floor (lower) of the retrofit case geometry (local and site obstructions not shown).
A Test Reference Year climate data set was developed from hourly measurements for Strbske Pleso station. The various simulation periods used are reported in the simulation results. Table 1 sumarises monthly values of the some important climate parameters.
Table 1 Monthly mean and extreme values for the Test Refrence Year used in the simulation study.
|
Parameter |
January |
February |
March |
April |
May |
June |
July |
August |
Sept |
Oct |
Nov |
Dec |
|
Tmean (oC) |
-6.1 |
-6.7 |
-0.6 |
2.4 |
7.9 |
10.3 |
11.8 |
12.9 |
8.5 |
6.4 |
-1.1 |
-3.3 |
|
Tmin (oC) |
-17.0 |
-17.6 |
-13.0 |
-7.2 |
-1.5 |
2.0 |
3.9 |
3.2 |
-1.0 |
-7.4 |
-9.0 |
-15.5 |
|
Tmax (oC) |
4.8 |
4.8 |
10.6 |
17.0 |
19.4 |
24.6 |
25.3 |
25.6 |
23.9 |
21.1 |
10.2 |
5.7 |
|
RH (%) |
81 |
80 |
78 |
76 |
70 |
77 |
74 |
73 |
72 |
68 |
85 |
80 |
|
v (m.s-1) |
3.3 |
2.7 |
3.0 |
3.1 |
2.7 |
2.4 |
2.9 |
2.4 |
3.1 |
3.1 |
4.2 |
3.3 |
|
SD (h) |
78 |
85 |
110 |
137 |
213 |
151 |
229 |
238 |
194 |
181 |
73 |
81 |
A number of simulations were carried out to identify the optimal level for additional insulation within the external walls, roof and ceiling. Figure 7 shows results for the external wall. Similar analyses were conducted to evaluate an optimal glazing system (see figure 8).
Both figures show a set of curves representing a flat's relative position within the building geometry (i.e. external or internal section) which are indicative of the relation between thermal performance and heating energy savings.
As can be seen from figure 7 the optimal level for additional insulation will be somewhere around a thermal resistance of 3.0 m2.K.W-1. Any further increase in the thickness of the insulation has no apparent performance benefit.
As can be seen from figure 8 the glazing system with a U-value of 1.5 W.m-2.K-1 and Tsol = 0.72 will deliver the largest energy savings.
Figure 7 Relative annual heating energy savings for different amounts of the additional external wall insulation cladding.
Figure 8 Relative annual heating energy savings for the different glazing systems.
The use of tightly-fitting windows usually results in inadequate natural ventilation of the building. Thus, possible levels of air exchange rates after window replacement were predicted. (see figure 9). As can be seen from figure 9 the tightly fitting windows (air permeability of 0.2 .10-4 m2.s-1.Pa-0.67) will drastically decrease air exchange to the unacceptable level of less than 0.2 ach. Figure 9 also shows the predictions of the corresponding air exchange rates for the different air permeability values of the suggested window trickle ventilators.
Figure 9 Prediction of air exchange rates for the different levels of window air permeability. Results are shown for March which happens to represent average ambient air temperature and wind speed for the location.
Finally, the issue of summer overheating of the attic extension was investigated. Simulations were carried out to predict the inside air temperature over the summer period from June to September (see figure 10).
As can be seen from figure 10 there is real danger for overheating to occur in summer with more then 70% of the summer time operative temperatures in the range of 25 oC to 26 oC. Taking into consideration the simulation uncertainties and the fact that the climate model represents average summer conditions, shading devices are recommended (e.g. venetian blinds) for the windows with south facing orientation which have been identified as the main source of the room's heat load.
Figure 10 Prediction of the operative temperature frequency distribution for the summer period (June to September).
In this case study, building energy simulation has been used to predict relative annual heating energy savings as a function of the thermal characteristics of the building envelope (i.e. external wall, glazing).
Two issues related to occupancy comfort levels were examined. Firstly, The expected levels of natural ventilation for tight-fitting windows; and the need for purpose-design background ventilation. Secondly, summer thermal comfort in the attic extension was predicted along with the recommendation for use of movable solar control devices.
