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Roofenvelope ratio impact on green roof
energy performance
Ryan Martens &Brad Bass &Susana Saiz Alcazar
Published online: 15 April 2008# Springer Science + Business Media, LLC 2008
Abstract This paper addresses the impact of roof-to-envelope ratio on overall energy savings of
a green roof design over conventional roof designs. Simulations were performed using a
modified version of the Environmental System Performance program simulator, developed at the
University of Strathclyde. The modified design employed a model developed by Columbia
University and the Goddard Institute of Space Science which models the evapotranspiritive
effect of a green roof calculated using the Bowen ratio; that is, the ratio of sensible heat flux to the
surrounding air to the latent heat flux resulting from evapourative energy losses. The resultingheat flux term is proportional to the external surface convection, but inversely proportional to the
surface Bowen ratio, which is held constant and chosen to match experimental results obtained
for a given roof design. The present study performed simulations for the month of July in a
Toronto climate on square warehouse style one, two, and three-story buildings, with windows
occupying 10% of the area of each wall. For the first set of simulations, the internal building load
of each story was set to zero, and the roofenvelope ratio was increased by increasing the
building width and length. For the final simulations, several roofenvelope ratios were chosen,
and the internal load of each story was increased from 0 to 50,000 W. As the roofenvelope ratio
increases, the cooling load of the upper floor for multi-story designs approaches the entire
building cooling load. This indicates the importance of upper zone cooling in total building
energy reductions. Furthermore, the total energy savings of a green-roofed building over a
conventional roofed building were far more significant for single-story structures. A 250 250 m
green-roof design with 50,000 W internal loading was found to have percentage energy savings
of 73%, 29%, and 18%, for a one, two, and three-story design, respectively.
Keywords Green roof. Building energy simulation. ESP-r. Building envelope . Latent flux
Urban Ecosyst (2008) 11:399408
DOI 10.1007/s11252-008-0053-z
R. Martens (*) :B. Bass :S. S. Alcazar
Adaptation and Impacts Research Division, Environment Canada, 33 Willcocks Street, Toronto, ONM5S 3E8, Canada
e-mail: [email protected]
B. Bass
e-mail: [email protected]
S. S. Alcazar
e-mail: [email protected]
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Introduction
Historically green roofs have been used as a medium for providing insulation and protection in
cold climates, mainly in northern parts of Europe, while in hot dry climates they have been used
to cool the indoor air and increase its moisture content through evaporative processes.Nowadays, green roofs are being implemented not only due to their thermal insulation
properties, but also because they are considered as a way of recovering the benefits provided by
the lost green space of cities. Various studies which have analyzed the effects of green roofs have
focused on a number of key benefits, including reduction in energy consumption, reduction in
greenhouse gas emissions, reduction in heat island effect, improvement in air quality, reduction
of urban noise, storm water management, and various social and recreational opportunities.
Green roofs reduce the heat transfer through the roof, thus reducing the heating and
cooling energy consumption in the building. The heat transfer processes in a common flat
roof, that is, convection, conduction and radiation, are affected by the green roof not only
through a change in the thermal characteristics of the materials and surface properties, butalso through the evapotranspiration and the plant metabolic processes occurring within the
plant system. It is therefore necessary to analyze the thermal performance of both the soil
and vegetation layers added to a conventional roof design.
Niachou et al. (2001) conducted a measurement of surface and air temperatures on green
roofs to examine their thermal properties and potential energy savings. The authors
analyzed the effect of green roofs with different levels of insulation, reporting reductions in
energy consumption ranging from 40% for the non-insulated roof to 2% for the well
insulated roof (considering roof conductance values of 0.4 W/m2C). This work employed
the green roof mathematical model developed by Palomo del Barrio (1997) to evaluate if agreen roof could provide a cooling effect to the buildings. The conclusion of this study was
that green roofs increase the insulating value of the roof, but do not provide any additional
cooling effect on the building.
In another study, several experiments were conducted comparing insulated and non
insulated green roofs (Eumorfopoulou and Aravantinos 1998). It was concluded that green
roofs must be used as a complementary system to the common insulated roof, and cannot
substitute the insulation layer to achieve thermal comfort inside the buildings. The study
identified the shading effect of the green roof as the main factor in the improvement of the
thermal performance.
Wong et al. (2002) analyzed the effect of different types of green roofs on the energyconsumption of a five storey commercial building in Singapore. They compared different
types of vegetation layers (e.g., shrubs, trees and turfing) and various soil thickness. They
reported total energy savings of 15% in relation to the energy consumption of the building
with a common flat roof when a green roof composed by shrubs and 300 mm of clay soil with
40% moisture content was installed in a building with a roof to wall surface ratio of 0.2.
Although green roof technology has been under development for some time, a reliable and
general-purpose model does not yet exist for performing energy-based simulations of a generic
green roof. Such a model would allow simulations to be performed on a variety of design
alternatives, allowing intelligent design decisions to be made without the high cost of obtaining
experimental data on a given design alternative. In many fields of study, computer modeling
and simulation has proven to be a low-cost means of obtaining a wealth of information quickly
and efficiently. What would previously require considerable effort and cost in construction,
instrumentation, and time for monitoring data, can now be performed and analysed in minutes
on a typical computer of today. In order to perform this work, however, a numerical model must
be developed and fully validated under a variety of circumstances.
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Although this study does not develop a general-purpose model of a green roof for use with
energy-based building simulation, it does demonstrate the ease in which a great deal of data
may be obtained efficiently and easily using a building energy simulation tool, Environmental
System Performance program (ESP-r; Energy Systems Research Unit (ESRU)). In this study, a
rooftop energy balance model for a green roof is integrated into an existing full building-energy simulation tool in order to determine the performance of a simple green roof model.
In a study conducted by Gaffin et al. (2005, 2006), based on experimental results
obtained from test buildings at Pennsylvania State University (DeNardo 2003), a rooftop
energy balance model was used to model the thermal characteristics of a green roof. The
evaporative heat loss from a green roof, or latent heat, was modeled as proportional to the
convective heat transfer from the rooftop surface to the surrounding air. This factor of
proportionality, the Bowen ratio, is the ratio of the sensible heat flux to the latent heat flux.
Thus the latent heat flux from a green roof, may be expressed as:
Qlatent Qconvectionb
whereis the Bowen ratio. For a control roof, this term is assumed to be zero. Unlike the
previous study, however, no equilibrium conditions have been assumed, resulting from the
ability of ESP-r to simulate transient heat transfer. Furthermore, the entire test-building was
modeled in ESP-r, rather than just the roof.
The base-case model was then extended to model a larger warehouse-style building in a
Toronto climate. Various sized buildings were simulated, with varying internal loading, in
order to determine various trends in the overall green roof energy savings over a
conventional roof design. It is important to note that not all components of a green roof,such as heat storage and insulation from the soil medium, were accurately modeled, as the
green roof improvements were captured simply by adding the latent heat flux term
containing the pre-determined Bowen ratio value.
Implementation in ESP-r
ESP-r is a general-purpose, transient building simulation tool developed at the University of
Strathclyde in Glasgow, Scotland, which accurately models the heat, air, moisture, and
electrical power flows of a building under the control of a plant system (ESRU). This multi-domain problem is solved at user-configurable time-steps. ESP-r is an open-source project,
which allows the implementation of new features, as well as a full understanding of the
theory and assumptions made in the simulation. Further information on the theoretical
formulation of ESP-r is documented by Clarke (Clarke 2001).
In order to perform the present study, the simple one-dimensional model employed in
(Gaffin et al. 2005,2006) was integrated in ESP-r. A full validation was performed using
data obtained from (Gaffin et al. 2005, 2006), as well as experimental data used in
(DeNardo 2003). Several complications arose in this comparison, namely due to
inconsistent climate data. A larger set of climate data was required for the full building
model used by ESP-r, as compared to the one-dimensional model of the roof. Not all of the
required data was logged at the Pennsylvania State test site. As a result, it was necessary to
obtain some of the required solar radiation data from logging performed at a nearby site.
This inconsistency, as well as the inability to obtain full thermal properties of all portions of
the building modeled, proved problematic, and many assumptions were made in the
comparison. The building modeled in this study maintains only the same roof properties as
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those used in the Pennsylvania State test building, which were in fact known. The wall
properties were changed to account for requirements of the new building design, discussed later.
Furthermore, the climate data used was that for the city of Toronto (43 N, 79 W). This climate
is similar to the Pennsylvania State (40 N, 77 W) climate, and all quantities in this data set are
in fact consistent, eliminating the problems previously discussed. The climate for Toronto andPennsylvania State are shown in Tables1 and2, respectively (Weatherbase2007).
The convection model employed was the same as used in (Gaffin et al. 2005, 2006),
based on a model by Terjung and ORourke (Terjung and ORourke1980b), but modified
at low wind speeds to prevent the convection term from vanishing.
Qconvection g1 u0:8 TsurfTair ; u >1:75
Qconvection +2 Tsurf Tair ; u e1:75
In the above,g1and g2were set to 6.5 and 10.3, respectively, as obtained for the control
roof using statistical analysis (Gaffin et al. 2005,2006). Convective heat-transfer from the
green roof was assumed to be similar to that of the control roof. Any changes in heat-
transfer for the green roof will be caused by the latent transfer, which as previously
discussed, is proportional to the convective transfer.
The zone air temperature was assumed well-mixed and held constant at 22C through
the use of an ideal air-conditioning and heating model. A control time-step of 2 min was
chosen to ensure that the zone air temperature remained constant at all times. As the
simulations were performed during the month of July, the focus of analysis was on total
cooling loads. It should be noted that on some nights, a small amount of heating wasrequired to maintain this fixed internal air temperature. The effect of heating on building
energy consumption was ignored in this study.
Model of a simple warehouse
A simple warehouse building was modeled using ESP-r. As previously noted, the roof
properties of the green roof used were identical to that of the Pennsylvania test site to
maintain compatibility with the parameters used in the model. All other properties were
Table 1 Average climate for Toronto, ON
Quantity Units Annual Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Average temperature C 7 6 5 6 12 17 21 20 15 8 3 2
Average high
temperature
C 12 2 1 3 11 18 22 26 25 20 13 6
Average low
temperature
C 2 9 9 4 1 7 12 15 14 10 3 6
Average precipitation Cm 76 4 4 5 6 6 6 7 8 7 6 6 6Average relative
humidity (morning)
% 83 83 83 82 78 76 78 79 86 89 87 86 84
Average relative
humidity (evening)
% 64 76 73 68 58 55 54 54 57 61 65 74 78
All simulations in this study were performed using climate data from Toronto. A summary of the Toronto
climate is provided for comparison with State College, PA, USA
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modified, but were consistent between the green-roofed building and the control building.
For all cases, the height of each story was fixed to 3.0 m, and windows were assumed to
occupy 10% of the total area of each wall, except the north wall, which contained a single
door.
Table 3 shows the material properties used in the simulated buildings. The individual
materials for each construction are specified from the outside to the inside. With the
exception of the roof, which is based on Pennsylvania test site constructions, the properties
were obtained from the ESP-r construction database. As a result, some of the materials usedmay not be familiar to North American readers.
It is important to note that the green roof model does not contain any material for
increasing the thermal capacitance. The sole effect of the green roof is obtained through the
latent heat loss term, resulting from water vapour evaporation. Further experimental
comparisons should be performed to examine the effect of including thermal capacitance in
the model. Since the soil medium employed at the test site studied was quite thin, the
overall effect is expected to be small. Although simple to add in the simulations performed,
this was not modeled to maintain consistency with the parameters obtained at the
Pennsylvania State test site.
The study was performed for the month of July in a Toronto climate. It should be notedthat the chosen Bowen ratio, 0.12, is highly dependent on the green roof itself, as well as
the climate in which the roof is implemented. The previous study (Gaffin et al. 2006) used
Bowen ratios ranging from 0.21 to 0.35. The lower value was chosen as the simulated
model is already somewhat conservative in nature. As mentioned previously, no thermal
capacitance was modeled for the roof. Also, the model calculates the temperature of the
roof-top surface, not the temperature below the soil surface in contact with the roof
insulation, which is slightly cooler. The combination of the above factors justifies the
selection of 0.12 for the Bowen ratio. Although the Bowen ratio employed was determined
for a Pennsylvania climate, the Toronto climate should result in similar values. A Toronto
climate was chosen for several reasons. First, and most important, is the fact that the Penn-
state data obtained was pieced together from several sources. Inconsistencies in data sets
proved problematic in ESP-r simulations. In addition, the Toronto climate proved of
immediate interest for other research, although it would be beneficial to repeat the study
under other climates. Recall, however, that the Bowen ratio is strongly dependant on
humidity, and other climatic factors.
Table 2 Average climate for State College, PA, USA
Quantity Units Annual Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Average temperature C 9 2 2 2 8 14 19 21 20 16 10 4 1
Average hightemperature
C 15 1 2 7 14 21 25 27 26 22 16 8 2
Average low
temperature
C 4 6 6 2 3 8 13 16 15 11 5 5
Average precipitation cm 98 7 6 8 8 10 10 9 8 7 7 6 6
Average relative
humidity
% 74 77 73 72 68 72 72 74 76 76 73 75 77
Previous studies developed a green roof model using a test site in Pennsylvania. Due to inconsistencies in
available PA data, and a requirement for results in the city of Toronto, simulations were conducted for a
Toronto climate
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Roof
envelope ratio analysis
In order to assess the impact of roofenvelope ratio on overall energy savings resulting
from a green roof, a variety of simulations were performed on the previously described
warehouse-type building. One, two, and three story buildings were examined in order to
determine the drop in energy savings with increasing building height. Building-envelope
ratio was varied by changing the length of each wall, and the internal loading was assumed
to be zero, unless noted otherwise. The benefit of a green roof over a conventional-roof was
obtained by comparing the total building cooling loads required for each case. The
percentage savings is defined as the ratio of the difference in cooling load required for a
green roof to the total cooling load of the control roof.
Figure1shows the total cooling load for both green and control roofs as a function of
building roofenvelope ratio, for a constant internal loading of both 0 and 5,000 W. It
should be noted that the latter load employed is somewhat low for the building sizes
investigated in this study. Nevertheless, the increase in cooling load required is still
significant, and provides further evidence of the advantage gained through the use of a
Table 3 Summary of simulated building material properties
Thickness Conductivity Density Specific
heat
Longwave
emissivity
Solar
absorptivity
Diffusion
resistance
(m) (W/mC) (kg/m3) (J/kgC)
Wall
Brick 0.100 0.960 2000 650 0.90 0.70 25
Glasswool 0.075 0.040 250 840 0.90 0.30 4
Air 0.050 0.000 0 0 0.99 0.99 1
Breeze block 0.100 0.440 1500 650 0.90 0.65 15
Roof
Aluminum 0.003 210.0 2700 880 0.22 0.20 19200
Air 0.025 0.000 0 0 0.99 0.99 1
Glass fiber quilt 0.080 0.040 12 840 0.90 0.65 30
Aluminum 0.003 210.0 2700 880 0.22 0.20 19200
Green roofPlywood 0.019 0.200 560 1000 0.90 0.70 576
Fiberglass 0.089 0.049 300 1000 0.90 0.50 5
OSB 0.006 0.110 600 1210 0.80 0.65 12
Ground floor
Earth 0.250 1.280 1460 879 0.90 0.85 5
Gravel 0.150 0.520 2050 184 0.90 0.85 2
Heavy-mix concrete 0.150 1.400 2100 653 0.90 0.65 19
Air 0.050 0.000 0 0 0.99 0.99 1
Chipboard 0.019 0.150 800 2093 0.91 0.65 96
Wilton 0.006 0.060 186 1360 0.90 0.60 10
Suspended floor
Wilton 0.006 0.060 186 1360 0.90 0.60 10
Chipboard 0.019 0.150 800 2093 0.91 0.65 96
Air 0.050 0.000 0 0 0.99 0.99 1
Heavy-mix Concrete 0.140 1.400 2100 653 0.90 0.65 19
Steel 0.004 50.000 7800 502 0.12 0.20 19200
Roof properties used in the simulation are identical to those of the Pennsylvania test site. All other properties
used in the simulation are taken from the ESP-r construction database
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green roof. The reduction in cooling load for the 5,000 W case decreases steadily for both
the green and control roof due to the increased total building size required to obtain larger
roofenvelope ratios. What is more important is the increase in savings obtained by a green
roof at these higher ratios.
Figures2and 3extend these findings to a two and three story building, respectively. In
addition, these figures break down the cooling load required into that required to cool only
the upper floor of the building, as well as that required to cool the total building. Again, for
small roofenvelope ratios, the green roof provides no benefit, as expected. At the oppositeend of the spectrum, however, the reduction in cooling load with the use of a green roof is
significant. More importantly, it becomes clear how important a roof is in dictating the
overall energy load of a building. As is clearly shown in Figs. 2and3, the cooling load of
the upper floor alone approaches the total cooling load at higher roofenvelope ratios. In
fact, above a roofenvelope ratio of 0.7 for a two-story building, the upper floor value
equals the total building cooling load. This effect is slightly less pronounced in the three-
story design. In both cases, the increase in cooling load with increasing roofenvelope ratio
Fig. 1 Variations in cooling load
for a single-story building with
the control and green roofs. The
simulations were performed with
a modified version of the ESP-r
model. Each simulation is run
with a constant internal loadingof 0 and 5,000 W, with 0 repre-
senting a baseline situation where
cooling is the only demand for
electricity in an unoccupied
building
Fig. 2 Variations in cooling loadfor a two-story building with the
control and green roofs. Cooling
load is shown for the entire
building, and also for the upper
floor alone, demonstrating that
for large roofenvelope ratios,
cooling of the upper floor
accounts for the entire building
cooling load. Green roofs provide
significant savings over the con-
trol roof with increasing roof
envelope ratios
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is due to the larger building sizes of the high roofenvelope ratio buildings. Of more
importance is the energy savings of the green roof case over the conventional roof design.
Figure 4 summarizes the percentage savings in cooling load of a green roof over a
conventional roof for all cases previously considered. The reduction in performance for
multi-story buildings quickly becomes apparent, although the gain in performance for high
roofenvelope ratios is significant in all cases.
The results obtained for the single story building represent an extreme case. The highroofenvelope ratio was obtained for a building of 250 250 m. Although this is not
uncommon with some warehouse building constructions, the convection model employed
may not provide accurate results for a rooftop of this size. Further studies should investigate
the use of other convection models in order to fully understand the flow over such a large
surface. Furthermore, a building with no internal loading does not represent a practical case.
The next section will deal with the addition of internal loading, and the overall effect on
reducing the performance gain of a green roof.
Fig. 3 Variations in cooling load
for a three-story building with the
control and green roofs. Cooling
load is shown for the entire
building, and also for the upper
floor alone. Slightly larger roof
envelope ratios are required forthe upper floor cooling load to
reach the total building cooling
load, when compared to the two-
story building
Fig. 4 Percentage savings in
building energy usage of greenroof design over the control roof
for a one, two, and three-story
structure. Overall savings from a
green roof are reduced for an
increasing number of stories, but
become more substantial for
higher roofenvelope ratios
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Internal load analysis
In addition to examining the impact of roofenvelope ratio, the effect of internal loading
was also examined. For several representative roofenvelope ratios, the internal loading
was varied from 0 to 50,000 W, a load more representative of buildings of this dimension.
Figure5 shows the effect of increasing the internal load for each of the test buildings, with
a roof area of 100 100 m. As shown in the plot, the net energy savings increases
approximately linearly with increased internal loading. This increase, however, reaches amaximum value, after which an increase in internal loading results in a drop in net energy
savings. For a 100100 m square roof, the effect is not noticed for two and three story
buildings.
Similar plots may be generated for larger roof areas. Although the energy savings are
larger, notably a net energy saving of up to 13,000 kW h for the three story building with a
roof area of 250250 m, the plots are considerably more flat, indicating the reduced effect
of total internal load on such a large building. Furthermore, the peak in energy savings is
not present in the case of larger buildings.
Fig. 5 Net energy savings
obtained from a green roof as a
function of internal loading for a
one, two, and three-story ware-
house structure measuring 100
100 m. Energy savings increase
approximately linearly with in-creasing internal load. A maxi-
mum value is shown in the
single-story case, after which net
energy savings decrease with in-
creasing internal load
Fig. 6 Percentage energysavings for a single-story with
varied internal loading. Increased
internal loading results in an
overall reduction in energy
savings, although savings are still
substantial
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Figure 6 demonstrates the percentage energy savings of a single story building with
varying internal loading. As shown in the figure, more realistic values for energy savings
are obtained when realistic internal loading is assumed. The values approaching 100%
obtained for all cases with 0 W internal loading are clearly not obtainable in a practical
structure. For loadings more typical of a practical structure, however, the savings are stillsignificant. A 250250 m green-roof design with 50,000 W internal loading was found to
have percentage energy savings of 73%, 29%, and 18%, for a one, two, and three-story
design, respectively.
In addition to addressing the issue of improved convection modeling, future studies
should investigate several further issues. This model accounts for the evapotranspiration
present on a green roof. The model does not directly account for changes in convection
resulting from the green roof. Furthermore, a green roof with a larger mass should also be
studied using the present model, in order to investigate the effect of thermal capacitance on
energy savings. The present study has assumed a constant value for the Bowen ratio.
Additional studies should identify the Bowen ratio as a function of climate conditions, as
well as any additional required green roof parameters. This would allow an investigation of
green roof performance at other sites and under other climates.
References
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