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Predicted changes in energy demands for heating and cooling due to climate change
Physics and Chemistry of the Earth 35 (2010) 100–106
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Physics and Chemistry of the Earth journal homepage: www.elsevier.com/locate/pce
Predicted changes in energy demands for heating and cooling due to climate change Mojca Dolinar a,*, Boris Vidrih b, Lucˇka Kajfezˇ-Bogataj c,1, Sašo Medved d,2 a
Environmental Agency of the Republic of Slovenia, Meteorological Office, Vojkova 1 b, 1000 Ljubljana, Slovenia Knauf insulation d.d., Trata 32, 4220 Škofja Loka, Slovenia c University of Ljubljana, Biotechnical Faculty, Jamnikarjeva 101, 1000 Ljubljana, Slovenia d University of Ljubljana, Faculty of Mechanical Engineering, Aškercˇeva 6, 1000 Ljubljana, Slovenia b
a r t i c l e
i n f o
Article history: Available online 6 March 2010 Keywords: Heating simulation Cooling simulation Climate change impact Test Reference Year
a b s t r a c t In the last 3 years in Slovenia we experienced extremely hot summers and demand for cooling the buildings have risen significantly. Since climate change scenarios predict higher temperatures for the whole country and for all seasons, we expect that energy demand for heating would decrease while demand for cooling would increase. An analysis for building with permitted energy demand and for low-energy demand building in two typical urban climates in Slovenia was performed. The transient systems simulation program (TRNSYS) was used for simulation of the indoor conditions and the energy use for heating and cooling. Climate change scenarios were presented in form of ‘‘future” Test Reference Years (TRY). The time series of hourly data for all meteorological variables for different scenarios were chosen from actual measurements, using the method of highest likelihood. The climate change scenarios predicted temperature rise (+1 °C and +3 °C) and solar radiation increase (+3% and +6%). With the selection of these scenarios we covered the spectra of possible predicted climate changes in Slovenia. The results show that energy use for heating would decrease from 16% to 25% (depends on the intensity of warming) in subalpine region, while in Mediterranean region the rate of change would not be significant. In summer time we would need up to six times more energy for cooling in subalpine region and approximately two times more in Mediterranean region. low-energy building proved to be very economical in wintertime while on average higher energy consumption for cooling is expected in those buildings in summertime. In case of significant warmer and more solar energy intensive climate, the good isolated buildings are more efficient than standard buildings. TRY proved not to be efficient for studying extreme conditions like installed power of the cooling system. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction In the last 5 years in Slovenia we experienced extremely hot summers and demand for cooling the buildings had risen significantly. Climate change scenarios predict higher temperatures for the whole country and for all seasons. According to the expected climate change it is presumed, that power demand for heating would decrease, while power demand for cooling would increase. There were several studies about possible change of energy demand due to climate change already made for some parts of the world (Adelard et al., 1999; Aguiar et al., 1999; Levermore and Chow, 2003). Since the changes in climate are predicted to be very variable in different regions, it is necessary, to study the impacts * Corresponding author. Tel.: +386 1 478 40 88; fax: +386 1 478 40 52. E-mail addresses: [email protected] (M. Dolinar), [email protected] (B. Vidrih), [email protected] (L. Kajfezˇ-Bogataj), [email protected] (S. Medved). 1 Tel.: +386 1 423 11 61. 2 Tel.: +386 1 477 12 10; fax: +386 1 251 85 67. 1474-7065/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2010.03.003
regionally. The purpose of our study was to access the rate of possible change in energy demand for heating and cooling in two typical urban climates in Slovenia. There are many variables influencing the energy demand for heating and cooling the buildings and the two main are climatological conditions and building architecture and structure properties. To study the influence of climate change on energy demand, two characteristic buildings were selected for simulation.
2. Methodology and data 2.1. Climate change scenarios Projections of climate change in the future are usually related to the term scenario and not to the term prediction. They are related to a lot of uncertainties that have to be considered during their interpretation. For quantitative assessment of climate change impact on building performance, the regional climate change scenarios are needed.
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The basic source of uncertainty in climate change studies is the assumption about future socio-economic development and related emission scenarios. Not knowing the exact response of the climate system to the changes of atmospheric composition, additionally contributes to the uncertainty of future climate change estimates, even on a large-scale. This is illustrated with an example of air temperature change projection across Europe based on climate simulations with four general circulation models (GCM) using SRES A2 and B2 marker emission scenarios (Houghton et al., 2001). The results of simulations were additionally scaled (Mitchell, 2003) to A1T, A1B, A1F, and B1 marker emission scenarios. The expected increase till the end of 21st century is between 1.5 °C and 5 °C in comparison to the 1961–1990 value. To bridge the gap between large-scale and regional scale, where GCM results are not representative, empirical downscaling was used for the estimation of local near-ground air temperature, precipitation and solar radiation at two locations in Slovenia (Bergant, 2003; Bergant and Kajfezˇ-Bogataj, 2004, 2005). As the air temperature will change significantly in the future, its values will exceed the range of values used for empirical model development leading to the problem of extrapolation as an additional source of uncertainty. All the projections of GCM results show an increase in temperature in the next century for the entire area of Slovenia. The expected range of changes is wide on account of the different response of GCM on changes in the composition of the atmosphere. The projections are less uniform in the size and in the sign of precipitation changes. The estimated change at locations Ljubljana and Portorozˇ is between 1.5 and 7 °C for temperature, between 20% and +20% for precipitation and from +3% up to 9% in solar radiation till the end of 21st century in comparison to the 1990 values. In our study we limited our simulation to the changes, predicted for the first 50 years of 21st century. We used four different combinations of possible temperature and solar radiation changes, with which we covered the whole spectra of possible change of climate in Slovenia in next 50 years: Moderate temperature increase (MODERATE): Average temperature rise for 1 °C. Moderate temperature and solar radiation energy increase (MODERATE+): Average temperature rise for 1 °C and average solar radiation energy increase for 3%. Significant temperature increase (SIGNIFICANT): Average temperature rise for 3 °C. Significant temperature and solar radiation energy increase (SIGNIFICANT+): Average temperature rise for 3 °C and average solar radiation energy increase for 6%. A lot of assumptions have to be made in regional climate change impact studies, and all contribute to the uncertainty in final results. Some of them will probably be reduced in the future by gaining new knowledge, but at least the problem of unpredictable changes of climate boundary conditions will always remain as a source of the uncertainty.
2.2. Simulation of energy demand and indoor conditions The transient systems simulation program (TRNSYS, 2000) was used for simulation of energy use for heating and cooling of the buildings. TRNSYS is well suited to detailed analyses of any system whose behaviour is dependent on the passage of time (typical meteorological data). Main applications are: thermal response of buildings, solar systems, renewable energy systems, heat storage, cogenerations, etc. There are two main variables influencing the energy demand for heating and cooling of the building (Umbeger, 2004):
– Climatological conditions. – Building architecture and structure properties. The case study building has two flats with a greenhouse in the southwest and unheated garage in ground floor. Living area on ground floor is 81.70 m2 and in first floor 110.90 m2. Heat transfer coefficient (U) of building envelopment (walls and roof) was defined by maximum allowed specific energy use for heating (Qmax). According to the Slovenian regulation of energy conservation, Qmax is calculated by:
Q max ¼ 45 þ 40 fo ½kWh=m2 a
ð1Þ
where fo is a building shape factor, representing the ratio of building envelopment area and it’s heated volume. Analysis was preformed for two buildings with the same architecture, but different envelopment constructions (U): Standard building: Standard building with permitted specific energy for heating ðQ ¼ Q max Þ. Low-energy building: Energy efficient building, where specific energy for heating is half of permitted ðQ ¼ Q max =2Þ. The U values of building envelopment were calculated using criteria for Q in baseline climate conditions and are summarized in Table 1. Other common building properties are described in Table 2. Four people were considered to occupy the house every day between 18:00 and 7:00 h next day. Necessary indoor air quality was provided with mechanical ventilation and heat recovery in winter time (g = 0.7). When building was occupied, the edition ventilation of 0.5 h1 was taken into account. Heat gain from luminaries was set to 5 W/m2 every day from 5:30 to 7:00 in the morning and from 18:00 to 22:00 (winter time) or 20:00 to 23:00 (summer time) in the evening. Exterior shades with shading factor Sf ¼ 0:7 were used on all windows and green house when solar radiation on horizontal plane was larger than 300 W/m2. Computer simulation tool TRNSYS was verified by measuring indoor temperature in the house. The measures were taken from 17th to 25th August 2004. The room was located in southeastern part of the building. Solar radiation on horizontal surface and air temperature were measured outdoor. Both windows (south and east) were shaded by overhang. Additionally, external blinds were
Table 1 Heat transfer coefficient (U) for building envelopment (W/m2 K). Ljubljana
Portorozˇ
Exterior wall Standard Low-energy
0.43 0.16
1.30 0.69
Roof Standard Low-energy
0.32 0.11
0.89 0.59
Table 2 Construction properties for standard and low-energy building. Dimension
m
U – inner wall U – floor to ground U – wall to unheated room U – glazing g of glazing Infiltration as air exchange per hour Heating set point Cooling set point
0.426 W/m2 K 0.172 W/m2 K 0.342 W/m2 K 1.4 W/m2 K 0.622 0.3 h1 20 °C 25 °C
M. Dolinar et al. / Physics and Chemistry of the Earth 35 (2010) 100–106
used ðSf ¼ 1Þ on the east-oriented window and internal curtain ðSf ¼ 0:7Þ on the south-oriented window. In Fig. 1 the harmony of simulated and measured indoor air temperature are shown. Predicted Mean Vote (PMV) was used as an indicator of thermal comfort. Thermal comfort calculation (Fanger comfort model) is considered to include clothing factor, metabolic rate and relative air velocity. No influence of direct or diffuse solar radiation on the occupants is considered in calculating PMV. At 25 °C clothing rate was 1.5 CLO and relative air velocity 0.2 m/s. Both clothing and relative air velocity were linearly changing with indoor temperature. Metabolic rate was set to 1.2 m.
specific year, while the weather condition of specific year may not represent those of long-term periods or even less for climate change scenario. Accordingly, there is a need to construct a meteorological data set on hourly basis, which would represent the average climate or future climate condition for selected location. There are many different methods for construction of TRY. Our objective was to generate a TRY that fulfils the following criteria: 1. TRY should represent the climate zone: Mean values of main climate variables should be as close as possible to a long-term mean values and climate change scenario mean values respectively. 2. Realistic dynamics of TRY: Hourly sequences and variation during single days and series of days typical for climate zone should be realistic. 3. True correlation between different variables, especially between temperature and radiation.
2.3. Meteorological data The study was performed for two typical climates in Slovenia, where most of the urban settlements are located. Mediterranean climate is represented with meteorological time series of Portorozˇ; subalpine climate is represented with meteorological time series of Ljubljana. The simulation of energy consumption and temperature conditions in the building was performed on hourly basis. The meteorological data input into the model was in a form of Test Reference Year (TRY) with hourly data for temperature, humidity, wind velocity, global radiation energy and precipitation. It is not easy to find observed long multivariate hourly time series, representative of true climate of the region. Long-term time series of temperature and humidity are usually available, while the shortness of radiation series is usually the main problem. In the past, hourly radiation data were calculated out of sunshine duration data, but it was only very rough approximation. In the beginning of nineties the automatic weather stations (AWS) were set up in Slovenia and on the majority of AWS also global radiation sensors were connected. The availability of global radiation data was the main reason to choose AWS data as the main data source for construction of TRY. For both selected stations we had 12 years long time series of homogenous hourly data for temperature, humidity, wind velocity, global radiation energy and precipitation. The gaps were filled with spatial–temporal interpolation using spline functions (de Boor, 2002; Schumaker, 1981) on classical measurements at climatological terms (7 a.m., 2 p.m. and 9 p.m.) at same location and AWS data from closest stations. Basically, hourly climatological data are obtained from the weather stations. For simulation of energy consumption in buildings it is, however, undesirable to use climatological data for one
The historical approach was used fulfilling all three above-mentioned criteria. Three climate variables were identified as critical, according to climate change scenarios: temperature, precipitation and global radiation. They were weighted equally, 33.3% each. Representative weather months were selected according to their closeness to long-term or future cumulative distribution functions. The selection was performed using daily climatological variables, which are available from January 1961 on. The TRY is made up of representative months selected out of the 12-years long time-series (period 1992–2003) of AWS hourly data. Cumulative distribution functions (CDF) were obtained by first sorting the data in increasing order, than, using the following equation (Levermore and Chow, 2003):
CDFðpi ; m; yÞ ¼
ð2Þ
where CDFðpi ; m; yÞ is the cumulative distribution function of climate variable pi, month m and year y, K the Kth value in order of magnitude of the climatological series and N is the total number of terms in the climatological series. CDF is monotonously increasing step function, bounded by 0 and 1. To calculate long-term CDF, all the average daily climate variable values over 30 years (1961–1990) of selected month are sorted in increasing order, and then the CDF for every value of average climate variable is calculated using above equation. To calMeasured indoor temperature Measured solar horizontal radiation
38
1008
36
936
34
864
32
792
30
720
28
648
26
576
24
504
22
432
20
360
18
288
16
216
14
144
12
72
10
2
Simulated indoor temperature Measure ambient temperature
Temperature [°C]
K Nþ1
Solar radiation [W/m]
102
0 17
5
17
5
17
5
17
5
17
5
17
5
17
5
17
Day hour [h] Fig. 1. Harmony of measured and simulated indoor temperature based on the measured outdoor temperature and soar radiation.
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M. Dolinar et al. / Physics and Chemistry of the Earth 35 (2010) 100–106 Table 3 Normalised values of the FS statistic for Ljubljana and Portorozˇ TRY, separately for cold period, warm period and the whole year. TRY
Portorozˇ
Ljubljana
BASELINE MODERATE MODERATE+ SIGNIFICANT SIGNIFICANT+
FS statistic cold period
FS statistic warm period
FS statistic year
FS statistic cold period
FS statistic warm period
FS statistic year
0.58 1.05 0.84 0.75 0.62
0.69 1.09 1.61 1.18 0.81
0.63 1.07 1.16 0.93 0.70
0.98 – 3.00 – –
1.17 – 1.93 – –
1.06 – 2.55 – –
culate CDF for a selected month, mean daily climate variable values of this month are sorted and CDF is calculated. For future climate, long-term CDF was linearly shifted, according to the scenario. After determining the cumulative distribution functions, Fienkelstein– Schafer statistics (FS) (Levermore and Chow, 2003) are calculated for selected three climate variables (temperature, precipitation and global radiation). FSðp; m; yÞ Statistic for a given climate variable pi, month m and year y, is the average difference between the monthly CDFðpi ; m; yÞ and long-term CDFðpi ; m; N i Þ:
FSðp; m; yÞ ¼
Nm 1 X jCDFðpi ; m; yÞ CDFðpi ; m; Ny Þj N m i¼1
ð3Þ
Nm is the total number if different values of climate variable. The matching of certain climate variables is sometimes more important than matching of other climate variables. Their importance could be expressed by weighting factors of the climate variable. Since we had studied the impact of climate change on energy consumption, all three variables that would possibly change, had the same importance (Wp = 0.333). The weighted sum of FS(m, y) statistics for all three climate variables was calculated, using:
FSðm; yÞ ¼
3 X
W p FSðp; m; yÞ
ð4Þ
struct reference years for all different climate change scenarios. The TRY were constructed only for those scenarios, where the sum of FS statistics for all 12 month was lower than 24. The threshold of 24 was chosen empirically, according to the magnitude of difference between average and demanded values of climate variables. Since energy consumption was simulated separately for cooling (the warm period extends from the beginning of May till the end of September) and for heating (cold period extends from the beginning of October till the end of April) we examined the sum of FS statistics separately for warm and cold period. Since the periods are of different length, FS statistics, normalised with number of month within each period, were compared. They are summarized in Table 3. Only statistics for the reference period (BASELINE) and for those climate scenarios, when normalised FS values were less than threshold value (2), are presented. The only exception is MODERATE + scenario for Portorozˇ, where representativeness of TRY for warm period is good, while it is not representative for the cold period. Since Mediterranean climate is less variable, out of the 12 years of hourly data, we could construct only the TRY for one climate change scenario. The TRY that we could construct with available data sets for both climates are summarized in Table 4.
p¼1
The month/year with lowest value of FS statistic was selected for a construction of reference year. Since the pool for months for the selection was relatively small (12 years), we could not con-
Table 4 The indications of TRY that could be constructed from available data for both climates. TRY
Ljubljana
Portorozˇ
Reference period 1961–1990 Moderate temperature increase Moderate temperature and solar radiation energy increase Significant temperature increase Significant temperature and solar radiation energy increase
BASELINE MODERATE MODERATE+
BASELINE – MODERATE+
SIGNIFICANT SIGNIFICANT+
– –
3. Results The results are presented in form of specific energy use, separately for heating in cold period and separately for cooling in warm period. For warm period also the number of hours above 25 °C in case of no mechanical cooling and Predicted Mean Vote (PMV) are presented. The results for energy use in different climate conditions were also compared to the simulated energy use in the extremely warm year 2003. 3.1. Subalpine climate (Ljubljana) Climate conditions and climate change scenarios as indicated in Table 4 were used for simulation the energy use for heating and cooling in subalpine region. The results of simulation the energy use for heating are presented in Table 5 and Fig. 2. The change in
Table 5 Specific energy use and the rate of change of energy use for heating and cooling according to baseline for Standard and low-energy building in Ljubljana. TRY
Heating
Cooling
Standard building
BASELINE MODERATE MODERATE+ SIGNIFICANT SIGNIFICANT+ YEAR2003
Low-energy building
Standard building
Low-energy building
Energy (kW h/m2 a)
Rate of change (%)
Energy (kW h/m2 a)
Rate of change (%)
Energy (kW h/m2 a)
Rate of change (%)
Energy (kW h/m2 a)
Rate of change (%)
79 66 68 59 59 64
– 17 14 25 26 19
40 31 33 28 28 30
– 22 17 30 32 26
0.9 0.8 2.1 2.5 4.5 8.8
– 3 143 190 418 916
1.1 1.7 2.3 2.9 4.4 8.0
– 56 113 165 299 629
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Low-energy building
Standard building
2
Heating energy [kWh/m a]
80
60
40
20
0 BASELINE
MODERATE
MODERATE+ SIGNIFICANT SIGNIFICANT+
YEAR2003
10 Low-energy building
2
Cooling energy [kWh/m a]
Fig. 2. Specific energy use for heating for Standard and low-energy building in Ljubljana.
Standard building
8 6 4 2 0 BASELINE
MODERATE MODERATE+ SIGNIFICANT SIGNIFICANT+ YEAR2003
Fig. 3. Specific energy use for cooling for Standard and low-energy building in Ljubljana.
2400
Number of hours
Low-energy building
Standard building
2000 1600 1200 800 400 0 BASELINE
MODERATE MODERATE+ SIGNIFICANT SIGNIFICANT+ YEAR2003
Fig. 4. Number of hours with indoor temperature above 25 °C in case of no mechanical cooling for Standard and low-energy building in Ljubljana.
1.0 Low energy building
Standard building
PMV
0.8 0.6 0.4 0.2 0.0 BASELINE
MODERATE
MODERATE+ SIGNIFICANT SIGNIFICANT+
YEAR2003
Fig. 5. Predicted Mean Vote (PMV) in Standard and low-energy building in Ljubljana.
energy use for space heating due to climate change is evident for both types of building, especially where significant warming and solar energy intensifying were predicted. The results of simulation the energy use for cooling are presented in Table 5 and Fig. 3. Solar radiation energy change has more influence on increased cooling
energy demand than warming. In case of moderate warming the low-energy building would use more cooling energy due to reduced heat flux from building during the night, but if there would be significant warming, low-energy building already has the advantage. The energy use for cooling in extremely warm summer
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M. Dolinar et al. / Physics and Chemistry of the Earth 35 (2010) 100–106
2003 (YEAR2003) was significantly higher than in the worst climate scenario (SIGNIFICANT+). The results of simulation of indoor temperatures in case of no mechanical cooling in warm part of the year are presented in Fig. 4. The rate of change in duration of hot indoor condition is significant due to climate warming and solar radiation intensifying. In extremely warm summer 2003 (YEAR2003) the indoor temperature conditions were more aggravating as in the worst climate change scenario (SIGNIFICANT+). The impact of climate warming on the indoor thermal comfort in warm part of the year is presented with PMV (Fig. 5). Thermal discomfort would rise significantly in case of climate change, in the summertime 2003 we already experienced even worse thermal stress as it is predicted in the worst climate scenario. 3.2. Mediterranean climate (Portorozˇ) Climate conditions and climate change scenarios as indicated in Table 4 are used for simulation the energy use for heating and cooling in Mediterranean region. The results of simulation the energy use for heating are presented in Table 6 and Fig. 6. There is no significant change in heating energy use in slightly warmer climate with moderate increased solar radiation. In warm winter
2003 (YEAR2003) the difference between energy use for standard and low-energy building had decreased, because the influence of the thermal insulation became less important. The results of simulation the energy use for cooling in Mediterranean climate are presented in Table 6 and Fig. 7. The increase in energy use due to climate warming is much more pronounced in low-energy building than in standard building. The energy consumption in extremely warm summer 2003 (YEAR2003) was significantly higher than in reference climate conditions (BASELINE). The results of simulation of indoor temperatures in case of no mechanical cooling in warm part of the year in Mediterranean climate are presented in Fig. 8. The rate of change in duration of hot indoor conditions is not significant. In extremely warm summer 2003 (YEAR2003) the indoor temperature conditions were worse as in the moderate warmer climate. The impact of climate warming on the indoor thermal comfort in warm part of the year in the Mediterranean region is presented with PMV (Fig. 9). Thermal discomfort is already quite high in Mediterranean area even in the present climate (BASELINE) and it would be significantly higher in case of climate warming. In summer 2003 the average PMV was very close to 1 in standard building and was even higher than 1 in low-energy building.
Table 6 Specific energy use and the rate of change of energy use for heating and cooling according to baseline for Standard and Low-energy building in Portorozˇ. TRY
Heating
Cooling
Standard building
Standard building
Low-energy building
Energy (kW h/ m2 a)
Rate of change (%)
Energy (kW h/ m2 a)
Rate of change (%)
Energy (kW h/ m2 a)
Rate of change (%)
Energy (kW h/ m2 a)
Rate of change (%)
75 75 71
– 0 –6
40 34 43
– 113 11
9 9 18
– 0 90
10 15 19
– 61 102
2
Heating Energy [kWh/m a]
100
Low-energy building
Standard building
80 60 40 20 0 BASELINE
MODERATE+
YEAR2003
Fig. 6. Specific energy use for heating for Standard and low-energy building in Portorozˇ.
20
Cooling energy [kWh/m2 a]
BASELINE MODERATE+ YEAR2003
Low-energy building
Low-energy building
Standard building
16 12 8 4 0 BASELINE
MODERATE+
YEAR2003
Fig. 7. Specific energy use for cooling for Standard and low-energy building in Portorozˇ.
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M. Dolinar et al. / Physics and Chemistry of the Earth 35 (2010) 100–106
Low -energy building
2
Cooling energy [kWh/m a]
20
Standard building
16 12 8 4 0 BASELINE
MODERATE+
YEAR2003
Fig. 8. Number of hours with indoor temperature above 25 °C in case of no mechanical cooling for Standard and low-energy building in Portorozˇ.
1.2
Low-energy building
Standard building
1.0
PMV
0.8 0.6 0.4 0.2 0.0 BASELINE
MODERATE+
YEAR2003
Fig. 9. Predicted Mean Vote (PMV) in Standard and low-energy building in Portorozˇ.
4. Conclusions The energy use for heating would decrease from 16% to 25% (depends on the level of climate change) in subalpine region, while in Mediterranean region the rate of change would not be significant. On the national level the influence of the climate change on energy use would be even more emphasized, while the existing building fond in the country has worse thermal isolation protection than it was presumed in the study as standard building. In summer time we would need up to six-times more energy for cooling in subalpine region, but the absolute scale of cooling energy is still much smaller than the scale of heating energy. Like in winter time, the rates of changes in energy consumption for cooling are not that large for Mediterranean region, but nevertheless, we could expect higher demand for cooling energy also in that region. The extension of the duration of the overheated conditions will result in increasing number of space cooling systems, something that we already experienced in the summer 2003. Energy efficient building is very economical in wintertime, when energy consumption is much higher than in summer. On average we could expect higher energy consumption for cooling in energy efficient building, but in case of significant warmer and more solar energy intensive climate, better-isolated buildings proved to be more economical than standard buildings. This indicates the importance of natural cooling strategies in such buildings. Several techniques should be implemented besides shading. Hourly temperature extremes are very important for designing the installed power of the cooling systems. TRNSYS is dynamical model that simulates energy fluxes on hourly bases, and could calculate the maximal power in extreme condition. The problem is the input climatological data series in the form of TRY, while with
methodology for construction TRY, we could not control hourly and daily extremes. To study energy use in extreme conditions, maximal power and extreme thermal conditions in case of no mechanical cooling, the construction of representative extreme day and week, would be necessary.
References Adelard, L., Thierry, M., Boyer, H., Gatina, J.C., 1999. Elaboration of a new tool for weather data sequences generation. In: Proceedings of IBPSA Conference, Kyoto, Japan. Aguiar, A., Camelo, S., Goncalves, H., 1999. Assessing the value of Typical meteorological years built from observed and from synthetic data for building thermal simulation. In: Proceedings of IBPSA Conference, Kyoto, Japan. Bergant, K., 2003. Projections of global climate simulations to local level and their use in agrometeorology. Ph.D. Thesis, University of Ljubljana, Biotechnical Faculty, Ljubljana, 170 pp. Bergant, K., Kajfezˇ-Bogataj, L., 2004. Priprava scenarijev podnebnih sprememb za Slovenijo, Pol stoletja Slovenskega meteorološkega društva, Slovensko meteorološko društvo, Ljubljana, pp. 155–171. Bergant, K., Kajfezˇ-Bogataj, L., 2005. Kakšno bo podnebje v Sloveniji v tem stoletju? = What might the climate of Slovenia look like in this century? Ujma 19, 218– 223. de Boor, C., 2002. A Practical Guide to Splines, second ed. Springer, New York. Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A., 2001. Climate Change 2001: The Scientific Basis. Cambridge University Press, Cambridge. 752 pp. Levermore, G., Chow, D., 2003. Climate change test reference years for buildings and urban environment. In: Proceedings of ICUC Conference, Lodz, Poland. Mitchell, T.D., 2003. Pattern scaling: an examination of the accuracy of the technique for describing future climates. Climatic Change 60, 217–242. Schumaker, L., 1981. Spline Functions: Basic Theory. John Wiley and Sons, New York. TRNSYS, 2000. A Transient Systems Simulation Program, version 15. Solar Energy Laboratory, University Of Wisconsin, USA.
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Physics and Chemistry of the Earth journal homepage: www.elsevier.com/locate/pce
Predicted changes in energy demands for heating and cooling due to climate change Mojca Dolinar a,*, Boris Vidrih b, Lucˇka Kajfezˇ-Bogataj c,1, Sašo Medved d,2 a
Environmental Agency of the Republic of Slovenia, Meteorological Office, Vojkova 1 b, 1000 Ljubljana, Slovenia Knauf insulation d.d., Trata 32, 4220 Škofja Loka, Slovenia c University of Ljubljana, Biotechnical Faculty, Jamnikarjeva 101, 1000 Ljubljana, Slovenia d University of Ljubljana, Faculty of Mechanical Engineering, Aškercˇeva 6, 1000 Ljubljana, Slovenia b
a r t i c l e
i n f o
Article history: Available online 6 March 2010 Keywords: Heating simulation Cooling simulation Climate change impact Test Reference Year
a b s t r a c t In the last 3 years in Slovenia we experienced extremely hot summers and demand for cooling the buildings have risen significantly. Since climate change scenarios predict higher temperatures for the whole country and for all seasons, we expect that energy demand for heating would decrease while demand for cooling would increase. An analysis for building with permitted energy demand and for low-energy demand building in two typical urban climates in Slovenia was performed. The transient systems simulation program (TRNSYS) was used for simulation of the indoor conditions and the energy use for heating and cooling. Climate change scenarios were presented in form of ‘‘future” Test Reference Years (TRY). The time series of hourly data for all meteorological variables for different scenarios were chosen from actual measurements, using the method of highest likelihood. The climate change scenarios predicted temperature rise (+1 °C and +3 °C) and solar radiation increase (+3% and +6%). With the selection of these scenarios we covered the spectra of possible predicted climate changes in Slovenia. The results show that energy use for heating would decrease from 16% to 25% (depends on the intensity of warming) in subalpine region, while in Mediterranean region the rate of change would not be significant. In summer time we would need up to six times more energy for cooling in subalpine region and approximately two times more in Mediterranean region. low-energy building proved to be very economical in wintertime while on average higher energy consumption for cooling is expected in those buildings in summertime. In case of significant warmer and more solar energy intensive climate, the good isolated buildings are more efficient than standard buildings. TRY proved not to be efficient for studying extreme conditions like installed power of the cooling system. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction In the last 5 years in Slovenia we experienced extremely hot summers and demand for cooling the buildings had risen significantly. Climate change scenarios predict higher temperatures for the whole country and for all seasons. According to the expected climate change it is presumed, that power demand for heating would decrease, while power demand for cooling would increase. There were several studies about possible change of energy demand due to climate change already made for some parts of the world (Adelard et al., 1999; Aguiar et al., 1999; Levermore and Chow, 2003). Since the changes in climate are predicted to be very variable in different regions, it is necessary, to study the impacts * Corresponding author. Tel.: +386 1 478 40 88; fax: +386 1 478 40 52. E-mail addresses: [email protected] (M. Dolinar), [email protected] (B. Vidrih), [email protected] (L. Kajfezˇ-Bogataj), [email protected] (S. Medved). 1 Tel.: +386 1 423 11 61. 2 Tel.: +386 1 477 12 10; fax: +386 1 251 85 67. 1474-7065/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2010.03.003
regionally. The purpose of our study was to access the rate of possible change in energy demand for heating and cooling in two typical urban climates in Slovenia. There are many variables influencing the energy demand for heating and cooling the buildings and the two main are climatological conditions and building architecture and structure properties. To study the influence of climate change on energy demand, two characteristic buildings were selected for simulation.
2. Methodology and data 2.1. Climate change scenarios Projections of climate change in the future are usually related to the term scenario and not to the term prediction. They are related to a lot of uncertainties that have to be considered during their interpretation. For quantitative assessment of climate change impact on building performance, the regional climate change scenarios are needed.
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The basic source of uncertainty in climate change studies is the assumption about future socio-economic development and related emission scenarios. Not knowing the exact response of the climate system to the changes of atmospheric composition, additionally contributes to the uncertainty of future climate change estimates, even on a large-scale. This is illustrated with an example of air temperature change projection across Europe based on climate simulations with four general circulation models (GCM) using SRES A2 and B2 marker emission scenarios (Houghton et al., 2001). The results of simulations were additionally scaled (Mitchell, 2003) to A1T, A1B, A1F, and B1 marker emission scenarios. The expected increase till the end of 21st century is between 1.5 °C and 5 °C in comparison to the 1961–1990 value. To bridge the gap between large-scale and regional scale, where GCM results are not representative, empirical downscaling was used for the estimation of local near-ground air temperature, precipitation and solar radiation at two locations in Slovenia (Bergant, 2003; Bergant and Kajfezˇ-Bogataj, 2004, 2005). As the air temperature will change significantly in the future, its values will exceed the range of values used for empirical model development leading to the problem of extrapolation as an additional source of uncertainty. All the projections of GCM results show an increase in temperature in the next century for the entire area of Slovenia. The expected range of changes is wide on account of the different response of GCM on changes in the composition of the atmosphere. The projections are less uniform in the size and in the sign of precipitation changes. The estimated change at locations Ljubljana and Portorozˇ is between 1.5 and 7 °C for temperature, between 20% and +20% for precipitation and from +3% up to 9% in solar radiation till the end of 21st century in comparison to the 1990 values. In our study we limited our simulation to the changes, predicted for the first 50 years of 21st century. We used four different combinations of possible temperature and solar radiation changes, with which we covered the whole spectra of possible change of climate in Slovenia in next 50 years: Moderate temperature increase (MODERATE): Average temperature rise for 1 °C. Moderate temperature and solar radiation energy increase (MODERATE+): Average temperature rise for 1 °C and average solar radiation energy increase for 3%. Significant temperature increase (SIGNIFICANT): Average temperature rise for 3 °C. Significant temperature and solar radiation energy increase (SIGNIFICANT+): Average temperature rise for 3 °C and average solar radiation energy increase for 6%. A lot of assumptions have to be made in regional climate change impact studies, and all contribute to the uncertainty in final results. Some of them will probably be reduced in the future by gaining new knowledge, but at least the problem of unpredictable changes of climate boundary conditions will always remain as a source of the uncertainty.
2.2. Simulation of energy demand and indoor conditions The transient systems simulation program (TRNSYS, 2000) was used for simulation of energy use for heating and cooling of the buildings. TRNSYS is well suited to detailed analyses of any system whose behaviour is dependent on the passage of time (typical meteorological data). Main applications are: thermal response of buildings, solar systems, renewable energy systems, heat storage, cogenerations, etc. There are two main variables influencing the energy demand for heating and cooling of the building (Umbeger, 2004):
– Climatological conditions. – Building architecture and structure properties. The case study building has two flats with a greenhouse in the southwest and unheated garage in ground floor. Living area on ground floor is 81.70 m2 and in first floor 110.90 m2. Heat transfer coefficient (U) of building envelopment (walls and roof) was defined by maximum allowed specific energy use for heating (Qmax). According to the Slovenian regulation of energy conservation, Qmax is calculated by:
Q max ¼ 45 þ 40 fo ½kWh=m2 a
ð1Þ
where fo is a building shape factor, representing the ratio of building envelopment area and it’s heated volume. Analysis was preformed for two buildings with the same architecture, but different envelopment constructions (U): Standard building: Standard building with permitted specific energy for heating ðQ ¼ Q max Þ. Low-energy building: Energy efficient building, where specific energy for heating is half of permitted ðQ ¼ Q max =2Þ. The U values of building envelopment were calculated using criteria for Q in baseline climate conditions and are summarized in Table 1. Other common building properties are described in Table 2. Four people were considered to occupy the house every day between 18:00 and 7:00 h next day. Necessary indoor air quality was provided with mechanical ventilation and heat recovery in winter time (g = 0.7). When building was occupied, the edition ventilation of 0.5 h1 was taken into account. Heat gain from luminaries was set to 5 W/m2 every day from 5:30 to 7:00 in the morning and from 18:00 to 22:00 (winter time) or 20:00 to 23:00 (summer time) in the evening. Exterior shades with shading factor Sf ¼ 0:7 were used on all windows and green house when solar radiation on horizontal plane was larger than 300 W/m2. Computer simulation tool TRNSYS was verified by measuring indoor temperature in the house. The measures were taken from 17th to 25th August 2004. The room was located in southeastern part of the building. Solar radiation on horizontal surface and air temperature were measured outdoor. Both windows (south and east) were shaded by overhang. Additionally, external blinds were
Table 1 Heat transfer coefficient (U) for building envelopment (W/m2 K). Ljubljana
Portorozˇ
Exterior wall Standard Low-energy
0.43 0.16
1.30 0.69
Roof Standard Low-energy
0.32 0.11
0.89 0.59
Table 2 Construction properties for standard and low-energy building. Dimension
m
U – inner wall U – floor to ground U – wall to unheated room U – glazing g of glazing Infiltration as air exchange per hour Heating set point Cooling set point
0.426 W/m2 K 0.172 W/m2 K 0.342 W/m2 K 1.4 W/m2 K 0.622 0.3 h1 20 °C 25 °C
M. Dolinar et al. / Physics and Chemistry of the Earth 35 (2010) 100–106
used ðSf ¼ 1Þ on the east-oriented window and internal curtain ðSf ¼ 0:7Þ on the south-oriented window. In Fig. 1 the harmony of simulated and measured indoor air temperature are shown. Predicted Mean Vote (PMV) was used as an indicator of thermal comfort. Thermal comfort calculation (Fanger comfort model) is considered to include clothing factor, metabolic rate and relative air velocity. No influence of direct or diffuse solar radiation on the occupants is considered in calculating PMV. At 25 °C clothing rate was 1.5 CLO and relative air velocity 0.2 m/s. Both clothing and relative air velocity were linearly changing with indoor temperature. Metabolic rate was set to 1.2 m.
specific year, while the weather condition of specific year may not represent those of long-term periods or even less for climate change scenario. Accordingly, there is a need to construct a meteorological data set on hourly basis, which would represent the average climate or future climate condition for selected location. There are many different methods for construction of TRY. Our objective was to generate a TRY that fulfils the following criteria: 1. TRY should represent the climate zone: Mean values of main climate variables should be as close as possible to a long-term mean values and climate change scenario mean values respectively. 2. Realistic dynamics of TRY: Hourly sequences and variation during single days and series of days typical for climate zone should be realistic. 3. True correlation between different variables, especially between temperature and radiation.
2.3. Meteorological data The study was performed for two typical climates in Slovenia, where most of the urban settlements are located. Mediterranean climate is represented with meteorological time series of Portorozˇ; subalpine climate is represented with meteorological time series of Ljubljana. The simulation of energy consumption and temperature conditions in the building was performed on hourly basis. The meteorological data input into the model was in a form of Test Reference Year (TRY) with hourly data for temperature, humidity, wind velocity, global radiation energy and precipitation. It is not easy to find observed long multivariate hourly time series, representative of true climate of the region. Long-term time series of temperature and humidity are usually available, while the shortness of radiation series is usually the main problem. In the past, hourly radiation data were calculated out of sunshine duration data, but it was only very rough approximation. In the beginning of nineties the automatic weather stations (AWS) were set up in Slovenia and on the majority of AWS also global radiation sensors were connected. The availability of global radiation data was the main reason to choose AWS data as the main data source for construction of TRY. For both selected stations we had 12 years long time series of homogenous hourly data for temperature, humidity, wind velocity, global radiation energy and precipitation. The gaps were filled with spatial–temporal interpolation using spline functions (de Boor, 2002; Schumaker, 1981) on classical measurements at climatological terms (7 a.m., 2 p.m. and 9 p.m.) at same location and AWS data from closest stations. Basically, hourly climatological data are obtained from the weather stations. For simulation of energy consumption in buildings it is, however, undesirable to use climatological data for one
The historical approach was used fulfilling all three above-mentioned criteria. Three climate variables were identified as critical, according to climate change scenarios: temperature, precipitation and global radiation. They were weighted equally, 33.3% each. Representative weather months were selected according to their closeness to long-term or future cumulative distribution functions. The selection was performed using daily climatological variables, which are available from January 1961 on. The TRY is made up of representative months selected out of the 12-years long time-series (period 1992–2003) of AWS hourly data. Cumulative distribution functions (CDF) were obtained by first sorting the data in increasing order, than, using the following equation (Levermore and Chow, 2003):
CDFðpi ; m; yÞ ¼
ð2Þ
where CDFðpi ; m; yÞ is the cumulative distribution function of climate variable pi, month m and year y, K the Kth value in order of magnitude of the climatological series and N is the total number of terms in the climatological series. CDF is monotonously increasing step function, bounded by 0 and 1. To calculate long-term CDF, all the average daily climate variable values over 30 years (1961–1990) of selected month are sorted in increasing order, and then the CDF for every value of average climate variable is calculated using above equation. To calMeasured indoor temperature Measured solar horizontal radiation
38
1008
36
936
34
864
32
792
30
720
28
648
26
576
24
504
22
432
20
360
18
288
16
216
14
144
12
72
10
2
Simulated indoor temperature Measure ambient temperature
Temperature [°C]
K Nþ1
Solar radiation [W/m]
102
0 17
5
17
5
17
5
17
5
17
5
17
5
17
5
17
Day hour [h] Fig. 1. Harmony of measured and simulated indoor temperature based on the measured outdoor temperature and soar radiation.
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M. Dolinar et al. / Physics and Chemistry of the Earth 35 (2010) 100–106 Table 3 Normalised values of the FS statistic for Ljubljana and Portorozˇ TRY, separately for cold period, warm period and the whole year. TRY
Portorozˇ
Ljubljana
BASELINE MODERATE MODERATE+ SIGNIFICANT SIGNIFICANT+
FS statistic cold period
FS statistic warm period
FS statistic year
FS statistic cold period
FS statistic warm period
FS statistic year
0.58 1.05 0.84 0.75 0.62
0.69 1.09 1.61 1.18 0.81
0.63 1.07 1.16 0.93 0.70
0.98 – 3.00 – –
1.17 – 1.93 – –
1.06 – 2.55 – –
culate CDF for a selected month, mean daily climate variable values of this month are sorted and CDF is calculated. For future climate, long-term CDF was linearly shifted, according to the scenario. After determining the cumulative distribution functions, Fienkelstein– Schafer statistics (FS) (Levermore and Chow, 2003) are calculated for selected three climate variables (temperature, precipitation and global radiation). FSðp; m; yÞ Statistic for a given climate variable pi, month m and year y, is the average difference between the monthly CDFðpi ; m; yÞ and long-term CDFðpi ; m; N i Þ:
FSðp; m; yÞ ¼
Nm 1 X jCDFðpi ; m; yÞ CDFðpi ; m; Ny Þj N m i¼1
ð3Þ
Nm is the total number if different values of climate variable. The matching of certain climate variables is sometimes more important than matching of other climate variables. Their importance could be expressed by weighting factors of the climate variable. Since we had studied the impact of climate change on energy consumption, all three variables that would possibly change, had the same importance (Wp = 0.333). The weighted sum of FS(m, y) statistics for all three climate variables was calculated, using:
FSðm; yÞ ¼
3 X
W p FSðp; m; yÞ
ð4Þ
struct reference years for all different climate change scenarios. The TRY were constructed only for those scenarios, where the sum of FS statistics for all 12 month was lower than 24. The threshold of 24 was chosen empirically, according to the magnitude of difference between average and demanded values of climate variables. Since energy consumption was simulated separately for cooling (the warm period extends from the beginning of May till the end of September) and for heating (cold period extends from the beginning of October till the end of April) we examined the sum of FS statistics separately for warm and cold period. Since the periods are of different length, FS statistics, normalised with number of month within each period, were compared. They are summarized in Table 3. Only statistics for the reference period (BASELINE) and for those climate scenarios, when normalised FS values were less than threshold value (2), are presented. The only exception is MODERATE + scenario for Portorozˇ, where representativeness of TRY for warm period is good, while it is not representative for the cold period. Since Mediterranean climate is less variable, out of the 12 years of hourly data, we could construct only the TRY for one climate change scenario. The TRY that we could construct with available data sets for both climates are summarized in Table 4.
p¼1
The month/year with lowest value of FS statistic was selected for a construction of reference year. Since the pool for months for the selection was relatively small (12 years), we could not con-
Table 4 The indications of TRY that could be constructed from available data for both climates. TRY
Ljubljana
Portorozˇ
Reference period 1961–1990 Moderate temperature increase Moderate temperature and solar radiation energy increase Significant temperature increase Significant temperature and solar radiation energy increase
BASELINE MODERATE MODERATE+
BASELINE – MODERATE+
SIGNIFICANT SIGNIFICANT+
– –
3. Results The results are presented in form of specific energy use, separately for heating in cold period and separately for cooling in warm period. For warm period also the number of hours above 25 °C in case of no mechanical cooling and Predicted Mean Vote (PMV) are presented. The results for energy use in different climate conditions were also compared to the simulated energy use in the extremely warm year 2003. 3.1. Subalpine climate (Ljubljana) Climate conditions and climate change scenarios as indicated in Table 4 were used for simulation the energy use for heating and cooling in subalpine region. The results of simulation the energy use for heating are presented in Table 5 and Fig. 2. The change in
Table 5 Specific energy use and the rate of change of energy use for heating and cooling according to baseline for Standard and low-energy building in Ljubljana. TRY
Heating
Cooling
Standard building
BASELINE MODERATE MODERATE+ SIGNIFICANT SIGNIFICANT+ YEAR2003
Low-energy building
Standard building
Low-energy building
Energy (kW h/m2 a)
Rate of change (%)
Energy (kW h/m2 a)
Rate of change (%)
Energy (kW h/m2 a)
Rate of change (%)
Energy (kW h/m2 a)
Rate of change (%)
79 66 68 59 59 64
– 17 14 25 26 19
40 31 33 28 28 30
– 22 17 30 32 26
0.9 0.8 2.1 2.5 4.5 8.8
– 3 143 190 418 916
1.1 1.7 2.3 2.9 4.4 8.0
– 56 113 165 299 629
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Low-energy building
Standard building
2
Heating energy [kWh/m a]
80
60
40
20
0 BASELINE
MODERATE
MODERATE+ SIGNIFICANT SIGNIFICANT+
YEAR2003
10 Low-energy building
2
Cooling energy [kWh/m a]
Fig. 2. Specific energy use for heating for Standard and low-energy building in Ljubljana.
Standard building
8 6 4 2 0 BASELINE
MODERATE MODERATE+ SIGNIFICANT SIGNIFICANT+ YEAR2003
Fig. 3. Specific energy use for cooling for Standard and low-energy building in Ljubljana.
2400
Number of hours
Low-energy building
Standard building
2000 1600 1200 800 400 0 BASELINE
MODERATE MODERATE+ SIGNIFICANT SIGNIFICANT+ YEAR2003
Fig. 4. Number of hours with indoor temperature above 25 °C in case of no mechanical cooling for Standard and low-energy building in Ljubljana.
1.0 Low energy building
Standard building
PMV
0.8 0.6 0.4 0.2 0.0 BASELINE
MODERATE
MODERATE+ SIGNIFICANT SIGNIFICANT+
YEAR2003
Fig. 5. Predicted Mean Vote (PMV) in Standard and low-energy building in Ljubljana.
energy use for space heating due to climate change is evident for both types of building, especially where significant warming and solar energy intensifying were predicted. The results of simulation the energy use for cooling are presented in Table 5 and Fig. 3. Solar radiation energy change has more influence on increased cooling
energy demand than warming. In case of moderate warming the low-energy building would use more cooling energy due to reduced heat flux from building during the night, but if there would be significant warming, low-energy building already has the advantage. The energy use for cooling in extremely warm summer
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2003 (YEAR2003) was significantly higher than in the worst climate scenario (SIGNIFICANT+). The results of simulation of indoor temperatures in case of no mechanical cooling in warm part of the year are presented in Fig. 4. The rate of change in duration of hot indoor condition is significant due to climate warming and solar radiation intensifying. In extremely warm summer 2003 (YEAR2003) the indoor temperature conditions were more aggravating as in the worst climate change scenario (SIGNIFICANT+). The impact of climate warming on the indoor thermal comfort in warm part of the year is presented with PMV (Fig. 5). Thermal discomfort would rise significantly in case of climate change, in the summertime 2003 we already experienced even worse thermal stress as it is predicted in the worst climate scenario. 3.2. Mediterranean climate (Portorozˇ) Climate conditions and climate change scenarios as indicated in Table 4 are used for simulation the energy use for heating and cooling in Mediterranean region. The results of simulation the energy use for heating are presented in Table 6 and Fig. 6. There is no significant change in heating energy use in slightly warmer climate with moderate increased solar radiation. In warm winter
2003 (YEAR2003) the difference between energy use for standard and low-energy building had decreased, because the influence of the thermal insulation became less important. The results of simulation the energy use for cooling in Mediterranean climate are presented in Table 6 and Fig. 7. The increase in energy use due to climate warming is much more pronounced in low-energy building than in standard building. The energy consumption in extremely warm summer 2003 (YEAR2003) was significantly higher than in reference climate conditions (BASELINE). The results of simulation of indoor temperatures in case of no mechanical cooling in warm part of the year in Mediterranean climate are presented in Fig. 8. The rate of change in duration of hot indoor conditions is not significant. In extremely warm summer 2003 (YEAR2003) the indoor temperature conditions were worse as in the moderate warmer climate. The impact of climate warming on the indoor thermal comfort in warm part of the year in the Mediterranean region is presented with PMV (Fig. 9). Thermal discomfort is already quite high in Mediterranean area even in the present climate (BASELINE) and it would be significantly higher in case of climate warming. In summer 2003 the average PMV was very close to 1 in standard building and was even higher than 1 in low-energy building.
Table 6 Specific energy use and the rate of change of energy use for heating and cooling according to baseline for Standard and Low-energy building in Portorozˇ. TRY
Heating
Cooling
Standard building
Standard building
Low-energy building
Energy (kW h/ m2 a)
Rate of change (%)
Energy (kW h/ m2 a)
Rate of change (%)
Energy (kW h/ m2 a)
Rate of change (%)
Energy (kW h/ m2 a)
Rate of change (%)
75 75 71
– 0 –6
40 34 43
– 113 11
9 9 18
– 0 90
10 15 19
– 61 102
2
Heating Energy [kWh/m a]
100
Low-energy building
Standard building
80 60 40 20 0 BASELINE
MODERATE+
YEAR2003
Fig. 6. Specific energy use for heating for Standard and low-energy building in Portorozˇ.
20
Cooling energy [kWh/m2 a]
BASELINE MODERATE+ YEAR2003
Low-energy building
Low-energy building
Standard building
16 12 8 4 0 BASELINE
MODERATE+
YEAR2003
Fig. 7. Specific energy use for cooling for Standard and low-energy building in Portorozˇ.
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Low -energy building
2
Cooling energy [kWh/m a]
20
Standard building
16 12 8 4 0 BASELINE
MODERATE+
YEAR2003
Fig. 8. Number of hours with indoor temperature above 25 °C in case of no mechanical cooling for Standard and low-energy building in Portorozˇ.
1.2
Low-energy building
Standard building
1.0
PMV
0.8 0.6 0.4 0.2 0.0 BASELINE
MODERATE+
YEAR2003
Fig. 9. Predicted Mean Vote (PMV) in Standard and low-energy building in Portorozˇ.
4. Conclusions The energy use for heating would decrease from 16% to 25% (depends on the level of climate change) in subalpine region, while in Mediterranean region the rate of change would not be significant. On the national level the influence of the climate change on energy use would be even more emphasized, while the existing building fond in the country has worse thermal isolation protection than it was presumed in the study as standard building. In summer time we would need up to six-times more energy for cooling in subalpine region, but the absolute scale of cooling energy is still much smaller than the scale of heating energy. Like in winter time, the rates of changes in energy consumption for cooling are not that large for Mediterranean region, but nevertheless, we could expect higher demand for cooling energy also in that region. The extension of the duration of the overheated conditions will result in increasing number of space cooling systems, something that we already experienced in the summer 2003. Energy efficient building is very economical in wintertime, when energy consumption is much higher than in summer. On average we could expect higher energy consumption for cooling in energy efficient building, but in case of significant warmer and more solar energy intensive climate, better-isolated buildings proved to be more economical than standard buildings. This indicates the importance of natural cooling strategies in such buildings. Several techniques should be implemented besides shading. Hourly temperature extremes are very important for designing the installed power of the cooling systems. TRNSYS is dynamical model that simulates energy fluxes on hourly bases, and could calculate the maximal power in extreme condition. The problem is the input climatological data series in the form of TRY, while with
methodology for construction TRY, we could not control hourly and daily extremes. To study energy use in extreme conditions, maximal power and extreme thermal conditions in case of no mechanical cooling, the construction of representative extreme day and week, would be necessary.
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