Reducting indoor temperature in summer using natural
night cooling
Climatic Cooling Potential in Tampere during July-August 2013
The Climatic Cooling Potential (CCP) for one night is defined
by Artmaan et al. (2007) as:
ℎ𝑓
𝐶𝐶𝑃 =
𝑚ℎ 𝑇𝑏,ℎ − 𝑇𝑒,ℎ
ℎ=ℎ𝑖
𝑚 = 1 h if 𝑇𝑏 − 𝑇𝑒 ≥ 3 𝐾
𝑚 = 0 if 𝑇𝑏 − 𝑇𝑒 < 3 𝐾
where ℎ is the time, 𝑇𝑏 and 𝑇𝑒 are the interior and exterior air
temperatures. There can be night ventilation between the
times ℎ𝑖 = 19.00 and ℎ𝑓 = 7.00 .
Figure 1 and Table 1 show the CCP during the summer 2013
for the measured and periodic (24.5 ± 2.5°C over 24h) interior
temperatures. For efficient night cooling the CCP shall be at
least 80 Kh. E.g. for a 12 h night cooling the average
temperature difference should be 6,7 K.
Night ventilation by open windows
160
140
120
100
80
60
40
20
0
Periodic building temperature
Measured building temperature
Figure 1. CCP for periodic and measured building temperatures in TUT during summer 2013
Table 1. CCP for the measured nights in TUT during summer 2013
Building
temperature
Periodic
Measured
Nights > 80Kh
Nights < 80Kh
Average [Kh]
33
25
6
14
115
88
Dynamic Building Energy Simulation
The experiment was conducted in two computer rooms of
similar size and with similar internal gains. The windows were
facing west and there was no mechanical cooling. The
ventilation air change rates were 3 h-1 running from 6.30 to
17.00. In the room 2 only, windows were open during nighttime. The temperatures were measured in both rooms at 8
different locations.
Temperatures in the rooms from 8.7. to 14.7.
26
24
22
Air temperature [°C]
180
1.7.
2.7.
3.7.
4.7.
5.7.
6.7.
7.7.
8.7.
9.7.
10.7.
11.7.
12.7.
13.7.
14.7.
15.7.
16.7.
17.7.
18.7.
19.7.
20.7.
21.7.
22.7.
23.7.
24.7.
25.7.
26.7.
27.7.
28.7.
29.7.
30.7.
31.7.
1.8.
2.8.
3.8.
4.8.
5.8.
6.8.
7.8.
8.8.
9.8.
10.8.
11.8.
12.8.
13.8.
14.8.
15.8.
16.8.
17.8.
18.8.
19.8.
20.8.
21.8.
22.8.
23.8.
24.8.
25.8.
26.8.
27.8.
28.8.
29.8.
30.8.
31.8.
Climatic Cooling Potential
Climitic Cooling Potential [Kh]
200
20
18
16
14
DBES is a hourly simulation tool developed with Matlab for the
thermal performance of buildings. It was tested successfully
according to EN15255 and EN15265 and it includes:
• Weather data
• Transient heat balance of structures (response factors)
• Heat balance of the air
• Detailed solar gain from the windows
• Radiative and convective internal heat gains
• Mechanical ventilation and infiltration
• Connections between rooms
• Air/operative temperature control
• Air/radiator space heating, air space cooling.
DBES was used to simulate the measured case and to
estimate the equivalent cooling effect provided by the night
ventilation. The comparison of the calculated and measured air
temperatures in the room 1 are compared on Figure 3.
Night ventilation
12
Measured and calculated air temperatures
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
28
8.7. 16:00
8.7. 19:00
8.7. 22:00
9.7. 1:00
9.7. 4:00
9.7. 7:00
9.7. 10:00
9.7. 13:00
9.7. 16:00
9.7. 19:00
9.7. 22:00
10.7. 1:00
10.7. 4:00
10.7. 7:00
10.7. 10:00
10.7. 13:00
10.7. 16:00
10.7. 19:00
10.7. 22:00
11.7. 1:00
11.7. 4:00
11.7. 7:00
11.7. 10:00
11.7. 13:00
11.7. 16:00
11.7. 19:00
11.7. 22:00
12.7. 1:00
12.7. 4:00
12.7. 7:00
12.7. 10:00
12.7. 13:00
12.7. 16:00
12.7. 19:00
12.7. 22:00
13.7. 1:00
13.7. 4:00
13.7. 7:00
13.7. 10:00
13.7. 13:00
13.7. 16:00
13.7. 19:00
13.7. 22:00
14.7. 1:00
14.7. 4:00
14.7. 7:00
14.7. 10:00
14.7. 13:00
14.7. 16:00
14.7. 19:00
14.7. 22:00
10
Measured outside
Figure 2. The measured interior air temperatures in the two rooms and the exterior air
temperature during one week in July
From the 2/7 until the 14/8, night ventilation was performed
during 28 nights in the room 2. The averaged measured air
temperatures during each day following night ventilation
from 8.00 to 17.00 were compared between the two rooms
for each sensor. These average temperature differences are
presented in Table 2. During the day following the night
ventilation, the average air temperature was 1.5 – 2 K lower.
Figure 2 presents the exterior air and the measured air
temperatures in the two rooms during one week in July.
Table 2. Average measured temperature differences during the occupied period (from 8.00 to
17.00) between the sensors of the two rooms, one with and one without the night ventilation.
Sensor
Window wall up
Window wall floor
Centre roof
Centre 1.6m
Temperature
difference [°C]
-1,8
-1,9
-1,9
-1,3
Sensor
Door floor
Exhaust
Back wall
Temperature
difference [°C]
-2,1
-1,8
-1,4
22
20
18
16
14
12
10
2.7.
3.7.
4.7.
4.7.
5.7.
6.7.
7.7.
7.7.
8.7.
9.7.
10.7.
10.7.
11.7.
12.7.
13.7.
13.7.
14.7.
15.7.
16.7.
16.7.
17.7.
18.7.
19.7.
19.7.
20.7.
21.7.
22.7.
22.7.
23.7.
24.7.
25.7.
25.7.
26.7.
27.7.
28.7.
28.7.
29.7.
30.7.
31.7.
31.7.
1.8.
2.8.
3.8.
3.8.
4.8.
5.8.
6.8.
6.8.
7.8.
8.8.
9.8.
9.8.
10.8.
11.8.
12.8.
12.8.
13.8.
14.8.
Measured room 2
24
Air temperature [°C]
Measured room 1
26
Measured room 1
Calculated room 1
Measured Outside
Figure 3. Comparison of the measured and calculated air temperature in the room 1 (no night
ventilation). Note the connection problem from the measurement devices in room 1 during the
beginning of July, when the temperature stays steady.
The results of the simulation for each night and in average were:
• induced cooling effect/floor-area: 15 W/m2
• total cooling energy: 162 Wh/m2
• induced airflow from windows: 137 dm3/s
M.Sc. Maxime Viot
Prof. Timo Kalema