Green-Roof Integrated Photovoltaic Canopy (GRIPV-c) is a study being conducted by Marc Perez, Christina Ho, and Nathaniel Wight with the support of Columbia University and Alfred E. Smith CTE High School students (Ashley Grant, Brandon Harvey, Michael Smith, William Alicea, Jared Hatcher, Marco Dwyer, Warrick Balfour). GRIP-c aims to evaluate data collected from four model houses, one housing a control roof, another with a green roof, a standard fixed photovoltaic system, and a GRIPV-c system. The study is ongoing and is looking at temperature, relative humidity, and solar insolation data to qualitatively and quantitatively assess the positive impact of a combined green roof and photovoltaic canopy system on the health of the green roof vegetation, the PV canopy system efficiencies, and the efficiency of roof mounted HVAC air handling units.

1. THE GREEN-ROOF INTEGRATED PHOTOVOLTAIC CANOPY (GRIPV-C)
Exploring Aesthetic and Environmental Synergies
EAEE E4006 Field Methods for Environmental Engineering
Columbia University, Spring 2011
Marc Perez
Christina Ho
Nathaniel Wight
ABSTRACT
In the urban environment, space is a premium. From micro-studios to multi-functional furniture,
optimizing usage of available space is a critical part of the city lifestyle. Finding purpose for
underutilized space is becoming increasingly important; further, as square foot of usable space
increases, so does value. In terms of real estate, no space can go unused and rooftops are fair game
particularly in the realm of energy efficiency. A variety of methods exist for use of roof top area for
energy offset purposes, but most have been studied independently of each other. Few examples of
research exist that study the synergistic effects of combining these elements.
Green-Roof Integrated Photovoltaic Canopy (GRIPV-c): Exploring Aesthetic and Environmental Synergies
aims to evaluate data collected from four model houses, one housing a control roof, another with a
green roof, a standard fixed photovoltaic system, and a GRIPV-c system. The study aims to use
temperature, relative humidity, and solar insolation data to qualitatively and quantitatively assess
the positive impact of a combined green roof and photovoltaic canopy system on the health of the
green roof vegetation, the PV canopy system efficiencies, and the efficiency of roof mounted HVAC
air handling units.
It is anticipated that for the GRIPV-c there is equivalent vegetation growth, and approximately 0.5%
improvement in PV performance due to improved cooling of 10 °F. Improvements in roof mounted
HVAC is roof area to building volume ratio dependant, but is anticipated to result in 35%
improvement on thermal performance for a building with a ratio similar to the enclosure.
BACKGROUND
Few studies have examined the synergies between green roofs and Photovoltaic (PV) arrays and
there remains a great need for further testing. Several notable examples provide a foundation and
justify the need for continued research.
For example, Brownson, Iulo and
Witmer of Penn State presented results
at ASES 2010 outlining the gains in
performance (both of the green roof
substrate and of a PV system atop it)
based on analysis of Penn State’s 2009
“Natural Fusion” home they designed
for the 2009 Solar Decathlon. The
Natural Fusion home employed deep
sedum trays on the roof with low-lying Solyndra/ Green-roof canopy atop the Natural Fusion home
mixed vegetation and a canopy several
inches above holding Solyndra^Tm CIGS (Cadmium-Indium-Gallium di-Selenide) PV cylinders.
The solyndra system is unique because each cylinder is coated completely on every surface with the

2. CIGS semiconductor material. The idea behind the cylinders that performance can be improved vis-
a-vis a traditional fixed-tilt PV array by always having some portion of the cylinder normal to the
sun’s position--and to use reflected radiation from the underlying roof surface that passes through the
spaces between the cylinders. Although the Natural Fusion GRIPV system described in the papers
by Brownson et. al provides a summary description of this interesting application of a novel PV
technology, a 2002 paper by Kohler et. al, presented at Rio ‘02, examines and identifies the key
synergies unique to GRIPV systems in much more thorough detail.
Furthermore, Kohler et. al examine somewhat un-
conventional GRIPV designs in that their green roof
incorporates plants growing up to a height of 40 cm
and required periodic (annual trimming) to maintain
height. The picture at left displays one of these
GRIPV-tested systems which features 1-axis tracking
and multi-crystalline PV modules.
The positive interactions measured and identified in
the study were:
1) Green roofs reduce operation temperature of the PV
system, thus increasing efficiency and energy yield
2) The PV array offers shading for the green roof, thus
improving growth of plants and increasing species
variety.
We also seek to measure this reduction in back-of-
module temperature, given the temperature drop in the local micro-climate from the green roof’s
evapotranspiration in our study and thereby simulate performance gains vis-a-vis a more traditional
PV system.
Both the GRIPV systems referenced above share the common shortcoming of not allowing for
synergistic use of space on the roof. On
buildings were point-loading considerations
are not an issue and green roofs provide the
potential for a recreational park-setting for
the occupants, the PV array would be better
situated at an elevation above head-height.
Our experimental study hopes to explore
and quantify the benefits outlined by Kohler
et. al under a new design paradigm: the
GRIPV-canopy. In addition to these
benefits, we will be simulating the reduced
burden on HVAC loads given reduction in
surface temperatures and addition of
The GRIPV system with intensive green roof and moncrystalline PV
in Kohler et al, 2002 photovoltaic generating capacity.
2

3. STUDY SITE
Bronx Design & Construction Academy1 is located in The South Bronx, one of the poorest
congressional districts in the United States. BDCA’s certified Career and Technical Educational
(CTE) programs allows economically disadvantaged students to get
unparalleled hand-on instruction in the trades, thereby provide a way
out of the poverty cycle. A majority of BDCA graduates will find jobs
upon graduation. BDCA high school offers endorsed diplomas in the
Building Trades including plumbing, carpentry, electrical practice and
installation, architectural drafting, and Heating, Ventilating, and Air
Conditioning. These diplomas enable graduates to obtain Master
Licenses from the NYC Department of Buildings. Once licensed,
graduates can open their own contracting firms.
Additionally, BDCA (formally Alfred E. Smith CTE HS) partners with
Edward J. Molloy for Initiative for Construction Skills that provides students the unique opportunity
to enter NYC Unions upon graduation. Since 2001 Alfred E. Smith has repeatedly helped place
over 20 percent of each graduating class in high-level union jobs,
including MTA, Metro North, Long Island Railroad, Smalls
Electrical Construction Inc., and New York City School
Construction Authority to name a few. Many others find
professional jobs in Plumbing, Electrical, Carpentry, Auto
Mechanics, HVAC as well as Pre Engineering. AES is associated
with New York Electrical Contracting Association, New York
Building Congress, New York Building and Construction Trades
Council of Greater New York, Building Trades Employers
Association, Architectural Construction and Engineering (ACE)
Mentoring program
Alfred E. Smith CTE HS also offers the training to put technical education to the test in regional and
National competitions. Year after year Smith
students practice what they've learned,
compete, and consistently take home trophies
from Skills USA and the National Automotive
Technology Competition.
BDCA provides CTE opportunities for special
needs students. Specifically, 20 percent of the
AES’ student body has an Individualized
Education Program (IEP). Smith is one of the
last standing schools in this city that provides
self-contained classes and integrated Career &
Technical Education shop classes for a large IEP population. Many of these IEP students have
excelled in their respective trades and have gone on to secure employment. In addition, Alfred E.
Smith CTE HS provides free adult classes at night for the community; Smith is not only an
educational facility for adolescents, but also for the community.
1
http://bxdca.com/director-letter/
3

4. For partial fulfillment of our grade in the course E4006 Field Methods for Environmental Engineers,
we collaborated with students on the Smith
campus to construct model homes. We are
currently monitoring the different rooftop
systems in an effort to demonstrate that solar
photovoltaic and green roof technology are not
mutually exclusive. With the help of BDCA
students we designed and built four model
homes, each with a different rooftop coverage
types: Control with gravel bed, Greenroof only,
Mock solar PV coverage only, Green roof with
mock solar PV coverage.
METHODOLOGY
1. Experimental Set Up
To assure that the data collected is consistent and comparable, specially designed monitoring
enclosures were constructed and co-located adjacent to each other. Four enclosures were designed to
collect the data used to calculate the performance and efficiency improvements of the GRIPV-c
system. Additionally, a stand to hold a pyranometer and ambient temperature and relative humidity
monitor was constructed and located with the monitoring enclosures.
The enclosures are designed to withstand the loading of the maximum roofing weights and to be
highly sealed to prevent air/energy leakage. They are approximately 2’ x 2’ x 4’ long and are
constructed from 2” x2” cedar dowels, 1” rigid insulation, and sealed with silicon sealant. They are
painted with a flat black paint so that the enclosures act as a black body to absorb maximum
radiation energy.
4

5. Enclosure 1 is the control with a standard gravel bed roof. Monitoring parameters for this enclosure
include internal temperature, and near surface roof temperature.
Enclosure 2 is the standard green roof installation. Monitoring parameters for this enclosure include
internal temperature, and near surface green roof temperature. Varietal sedum trays were used for
the green roof materials. The tray depths are approximately 4”.
Enclosure 3 is the standard solar roof installation. Monitoring parameters for this enclosure include
internal temperature, near surface roof temperature and rear solar panel temperature.
Enclosure 4 is the GRIPV-c system. Monitoring parameters for this enclosure include internal
temperature, near surface roof temperature and rear solar canopy temperature.
The below pictures show Smith students framing the model homes and calibrating and labeling the
sensors.
5

6. 2. Modeling Photovoltaic System Performance from Empirical Data
Silicon-based Photovoltaics are adversely affected (in terms of solar/electric conversion efficiency)
by elevated temperatures and to a lesser extent by decreased solar radiation. In this section, we
develop a modeling tool to interpret Irradiance and back-of-module temperature data from our
experimental setup and calculate expected energy generation and therefore, expected cost savings.
We have chosen to model the JAMS(L) 72-180 solar module as the physical basis for our modeling
because the manufacturer, JA Solar was as of Q1 2011 the dominant player in solar module sales
worldwide. It was thought that by using specifications from the best selling solar module as such,
our results would best allow themselves to be generalized.
2.1 Deriving Current/Voltage Characteristics from the Shockley Diode Equation:
To model the dependencies, we needed to estimate the current/voltage (I/V) characteristic curve for
this module using the Shockley diode equation. We model these I/V curves as a function of many
parameters (which will be detailed below) as such:
⎛ qV ⎞
IL − I0 ⎜e nkT −1⎟
⎝ ⎠
Imod =
⎛ qV ⎞ R I
1+ I0 ⎜e nkT −1⎟ s L
⎝ ⎠ nKT
In the equation above, Imod is the module current given the module voltage (V) and temperature (T).
(It is a modified version of the Shockley-Diode equation, solved for the module current instead of for
€
voltage as it is typically given.)
The light-induced current, IL , is calculated as such:
6
€

7. IL = ISC ⋅ E + K 0 (T t − TSTC )
Above, the ISC is the short-circuit current (a characteristic taken from the module’s specification
sheet,) E is the instantaneous radiation in terms of suns (at time ‘t’), Tt is the module temperature at
€
time ‘t’, TSTC is the “Standard Test Conditions” (STC) temperature at which modules are tested at
and Ko is a parameter that changes the temperature to reference the Normal Operating Cell
€
Temperature (NOCT) instead of STC. Ko is calculated as such:
ISC NOCT
− ISC STC
K0 =
(TNOCT − TSTC )
Above, ISC NOCT is the short-circuit current evaluated at the NOCT and ISC STC is the short-circuit
current evaluated at the STC. € Both of these values are given on module specification sheets.
I0 is known as the ‘dark saturation current’ and this is calculated as such:
€ 3 −qV ⎛ € ⎞
⎛ T ⎞ n nK g ⎜ T1 − T 1 ⎟
⎜ ⎟
I0 = I0 STC ⎜ t ⎟ ⋅ e ⎝ t STC ⎠
⎝ TSTC ⎠
In the equation above, Tt is the temperature at time ‘t’, TSTC is the STC temperature, ‘n’ is the diode
factor (which we assume to be 2), q is the intrinsic charge of an electron, Vg is the band-gap voltage
€
of mono-crystalline Silicon, k is Boltzmann’s constant, and I0 STC is the dark saturation current
evaluated at STC which we evaluate as such:
ISC
I0 STC = €qVOC
⎛ ⎞
⎜ e nkTSTC −1⎟
⎜ ⎟
⎝ ⎠
Note that the above is a fixed constant a function of module-specific parameters from the
specification sheet: VOC (open circuit voltage) and ISC (short circuit current.)
€
Finally, the shunt resistance, Rs is calculated as such:
∂V 1
RS = −
∂IVOC X V
∂V
We assume to be a fixed constant (-9 x 10-3) that is a function of the number of cells on the
∂IVOC
€
module (in the case of the JAMS(L), the number of cells is 72) and the VOC of the cell. XV is the
impedance at STC:
€ q⋅ I0 STC
qVOC
nkTSTC
XV = ⋅e
nkTSTC
From these equations, we can simulate the I-V curves for any combination of T, E by modulating
the appropriate parameters.
€
7

8. 2.2 Testing the Model: Synthesized Performance Curves:
Above is a plot showing the characteristic I/V curves for our chosen solar module, modeled as
previously outlined for various cell temperatures. Note how the warmer the cell temperature
becomes, the lower the open-circuit voltage (V0C). The drop in VOC is what has the detrimental effect
on conversion efficiency.
Modern inverters (DC/AC conversion) have built-in algorithms to calculate the voltage to set the
string of solar modules at in order to maximize the area under the I/V curve. This position is known
as the maximum power-point (MPP.)
As can be seen in the plot below, where we’ve calculated Power output as a function of module
voltage for the same set of temperatures, we can identify the MPP as the location where each curve
peaks.
As can also be deduced from the above plot, the maximum power output of the module decreases
with increasing temperature.
8

9. 2.3 Simplifying the Model for quickly calculating performance from Time-Series Data:
From this complex model, we can also modulate incident radiation at each temperature to determine
relationships between the two. In the following (surface) plot, we show how the module conversion
efficiency (from sunlight to DC electricity) as a function of both incident radiation and temperature.
Note that the only reason efficiency changes as a function of irradiance is because the maximum
power point will have to slightly shift—this is an artifact of the I/V curve shape. This yields a much-
simplified model for calculating efficiency for simple integration into our modeling of power
production:
η = ( mb E + bb ) + TB .O.M ( ms E + bs )
The factors mi and bi are derived from linear regression of the modeled I/V curves and E is in terms
of Suns (i.e. 1000 W/m2 = 1 sun)
€
Finally, our AC-side Power output is calculated as such:
PAC = E W ( m )⋅ ∂ ⋅ η
2 r T, E
where ∂r is the de-rate factor (we’ve assumed this to be ~78% as a result of losses from conversion
from DC/AC in the inverter, wiring, voltage mis-match, deviation from the name-plate power
output in the specification sheet, etc.) E is the radiation in W/m2. From this simplified formula, we
€
have a model to calculate power production (per m2 at any T and E) for our module.
€
3. Modeling Expected HVAC Loading from Empirical Data
9

10. In order to estimate the impact each individual roof design has on HVAC loads—in lieu of an actual
AC unit in operation—is to simulate the performance of one based on physical measurements and
empirical relationships. In this section, we discuss how we modeled these performances from
derived R-values of the different roof substrates based on exterior surface, interior and exterior
ambient temperature readings.
3.1 Modeling R-values from data:
From a recent report from the DOE2 Energy Efficiency and Renewable Energy division, we
extracted an empirical relationship between the temperature of an internal wall (TW,INT ), the
temperature of an external-facing wall (TW,EXT ) and the ambient external temperature (TEXT,A.)
Internal wall temperature we take as mean air temperature from our thermistor hanging in the center
of each box, but external wall temperature, we will need to calculate periodically using an IR
thermometer or equivalent as this is not a quantity we are directly measuring in real-time. External
temperature we are also measuring directly.
From this DOE report, which is reproduced by the North Dakota Department of Commerce in a
handy table3, we used multiple ordinary least squares regression to build empirical relationships for
any combination of TW,EXT TW,INT and TEXT,A. In fact the only quantity we care about at this point is
the difference between the temperatures of the internal facing wall and the external facing wall:
ΔT = TW ,EXT − TW ,INT
After examining the data roughly, a power-model of the form:
€ B ΔT
R T = AΔT ( ΔT )
Is determined to best fit R values (we fit the imperial R values in BTU to the ΔT readings. Using
o.l.s. regression, we fit the parameters AΔT and BΔT from empirical data with an R2 of over 99% for
€
each discrete exterior temperature given, (hence R T ). Following this, we examined the change in
both AΔT and BΔT with external temperature. The result being that€ ΔT can be approximated by
A
linear regression quite well and that BΔT shows no trend so we took the mean across all discrete
€ €
temperatures: €
€ € €
AΔT = mm (TEXT ,A ) + bm
€
BΔT = BΔT
In excel, extracting the 0th and 1st order moments from a regression is simple using a combination of
index() and linest() functions. € the end, we end up with a combined formula for the R value of:
In
€
[ ]
R = mm (TEXT ,A ) + bm ⋅ [ ΔT ]
B ΔT
2
€
U.S. Department of Energy. EERE. (2006, 0530). Insulation. Retrieved March 10, 2008,from Energy Savers, Tips on saving energy
and money at home: www1.eere.energy.gov/ consumer/tips/insulation.html
3
Pedersen, C., Hellevang, K.. NDSU. (2010, March.) Determining Insulation and Infiltration Levels Using an Infrared Thermometer:
10

11. ft 2 °F⋅ hr
The above form was derived such that R is in units of --it is not as easy to visualize in
BTU
metric units with the Temperatures in terms of °C. Below are the values:
€
Using these empirical relationships, we are now able to determine an R value for any combination of
the three key temperature readings (TW,EXT TW,INT and TEXT,A.) However, as there might be some
error in the measurements we take on the exterior wall surface, we will calculate an R value at each
time all pieces of data are available and take a moving cumulative average.
In the plot above, we see the results of modeling this empirical data from DOE. As ΔT increases,
the R-value experiences exponential decay, while the higher the ambient external temperature, the
expected R-value sees a linear decrease. Because the data from which these relationships were
derived only represents external temperatures from -40°C to 10°C, that is the only range of
€
temperatures at which we will calculate R.
3.2 Modeling Heat Transfer out of the Boxes:
Using these derived R values—which we will calculate for each interior surface of the boxes, we can
•
calculate the heat flux ( Q ) at any given time. Using an ASHRAE standard for thermal comfort (we
choose ~23°C for our model), we keep the internal temperature fixed at this level and measuring the
temperature difference between this value and the ambient external temperature. This gives us a
new ΔTop (op for operational), and along with the R-values, we calculate the flux as such:
€
11
€

12. • ΔTop
Q=
R
Fluxes are calculated for each surface within the boxes corresponding to their respective R-values
and a relative weight is given to each depending on the fraction (fi) of the interior surface area
covered by each: €
• i •
QTOT = ∑ Qi f i
1
Our model is flexible enough to take any house dimensions and calculate these weighting factors and
the corresponding fluxes for each surface (and net flux). These flux values are ostensibly in W 2 ,
€ m
and the final step is to convert these values—which are required HVAC loads—to the required
electrical load by dividing by the SEER rating (Btu/Wh) given as a badge of efficiency to HVAC
units. These can range widely—from near zero to over 26 for the best, most modern units—so we
have left it as a user parameter. Our total energy flux for a given time period (t = 0€ t = t ) is just
to
the integral of the instantaneous heat fluxes… in our discrete case it will be a sum of these fluxes
multiplied by the length of the recording interval:
t •
E tot,electrical = ∫Q
0 TOT dt
€
RESULTS & DISCUSSION
12

13. As limited data is available at the time of this report, simulated time series data has been used to
produce anticipated results for the following areas of concern.
1. Comparison of Simulated HVAC Loads
This plot shows expected results using our model to interpret the predicted time series data. This
model used data simulated from a Typical Meteorological Year from the National Solar Radiation
Database in New York State for solar radiation. Using the methods discussed previously for thermal
energy transfer, our model outputs estimated energy
loads for each house (on a /m3 basis) based on the
internal temperatures, solar radiation and estimated R-
value.
ASHRAE Standard 55 - Thermal Environmental
Conditions for Human Occupancy is used to define
indoor conditions. The envelopes, or shaded regions,
define a range of comfortable temperatures and
humidity levels for two reasonable insulation levels for
the occupants (0.5-1.0 CLO).
This allowed for calculation of the required energy to
bring the internal temperature into to the ASHRAE standard envelope.
The results below reflect the net energy load for a 1-story house (360 m3) with 120 m2 of roof space
from each of our individual houses. This house model was chosen as it most closely resembles the
relative dimensions of the monitoring enclosures.
The dramatic reduction in net energy for the PV based systems accounts for the offset in net energy
required due to the supply of energy from the PV system itself. As this graphic shows, there is a real
reduction in energy requirements for HVAC loads anticipated due to the insulating value of the
green roof itself. Coupled with the significant offset of net energy required for the system due to the
PV power generation, a combined system is anticipated to have excellent gains over a traditional
gravel roof.
One shortcoming of
Key: GRAV  gravel roof only, GR_1  Green roof only, GRAV-pv  gravel roof with PV, GRIPV-c  this model is that it
geen roof with pv canopy can only describes
13

14. savings for buildings with roof area to building volume ratios similar to the enclosure.
2. Comparison of Net Energy Costs
Based on the annual energy saving described in the pervious section, we have calculated the
estimated cost savings for each module type for a house with dimensions mentioned in the previous
section. Here, we have assumed the cost of electricity to be $0.12/kWh (NYPA’s average rate
throughout 2010), and net metering to occur at the same rate for the PV. PV system size is 11 kW
and considered to be flat for the purposes of testing the model. These costs are relative operating
costs; they do not take into account amortized capital outlays for the Green Roof or for the PV.
These will be an addition to the models over the course of the summer.
FUTURE STEPS
To improve data collection results, a number of changes to the experimental set up and data analysis
methods will be considered. These include:
• Calibrate probes periodically to ensure monitoring values are representative
• Relocation of the experimental monitoring enclosures to a location with a higher percentage
of daily solar insolation exposure
• Potential expansion of the data collection set up to other high schools/locations in the area
for additional data sets
• Evaluation of capital costs of installations
• Develop method to compare impact on HVAC system loads with varying roof area to
building volume ratios.
• Secure funding and construct GRIPV-c monitoring enclosure with real solar PV panels to
demonstrate the calculated performance levels
14

15. In the future we also plan to conduct a qualitative and quantitative assessment of Green Roof’s
health. Using an NDVI (Normalized Difference Vegetation Index) camera, we will use image
analysis to conduct various types of measurements, color quantification and classifications. A
Normalized Difference Vegetation Index camera will allow us to measure the diseased area on our
green roof sedum leaves, and quantify plant health and vigor.
Additionally, students at Bronx Design & Construction Academy & Alfred E. Smith Career &
Technical Education high school have already began planning to set up a blog site and Facebook
page in an effort to communicate and document their work and continued involvement in this
project.
Furthermore, teachers and students at Alfred E. Smith Career & Technical Education high school
will engage in activities that will strengthen their understanding of science, enhance school
curriculum, as well as provide content that meets below National Science Standards4.
Standard 1: Analysis, Inquiry, and Design
Students will use mathematical analysis, scientific inquiry, and engineering design, as appropriate, to
pose questions, seek answers, and develop solutions.
Standard 4: Science
Students will understand and apply scientific concepts, principles, and theories pertaining to the
physical setting and living environment and recognize the historical development of ideas in science.
Standard 5: Technology
Students will apply technological knowledge and skills to design, construct, use, and evaluate
products and systems to satisfy human and environmental needs.
Standard 6: Interconnectedness: Common Themes
Students will understand the relationships and common themes that connect mathematics, science,
and technology and apply the themes to these and other areas of learning.
Finally, future work on this project will provide students and staff at Bronx Design & Construction
Academy & Alfred E. Smith Career & Technical Education high school
1. Integrate Career and Technical Education and content-based curricula,
2. Project-based learning opportunities,
3. Students the opportunity to identify, evaluate and reflect on environmental issues in the
community and in our world,
4. Strengthen the ability of students to make predictions and decisions based on measurement,
observations, and calculations, and
5. Heightened awareness of environmental awareness and stewardships
ACKNOWLEDGMENTS
4
http://www.nap.edu/
15

16. Many thanks to Wade McGillis, Nadine Els, and Diana Hsueh for their continued advisement and
financial support, without which this project would not have happened. Additionally, thanks to all
the students who participated from the Bronx Design & Construction Academy & Alfred E. Smith
Career & Technical Education high school, specifically: Ashley Grant, Brandon Harvey, Michael
Smith, William Alicea, Jared Hatcher, Marco Dwyer, Warrick Balfour.
REFERENCES
Brownson, J.R.S. and L.D. Iulo. Upsetting the Balance Beam: System Integrative Photovoltaics as
Purposeful Manipulation of Energy Demand and Microclimate in the Built Environment. in proc. ASES
National Solar Conference. 2010. Phoenix, AZ: American Solar Energy Society.
Kohler, M., et al., Photovoltaic Panels on Greened Roofs: Positive Interaction Between two Elements of
Sustainable Architecture, in RIO '02 - World Climate & Energy Event. January 6-11, 2002: Rio de
Janiero. p. 151-158.
Sui, J. and J. Munemoto, Shape Study on a Green Roof Integrated Photovoltaic System for Bi-objective
Optimization of Investment Value and CO2 Emission. Journal of Asian Architecture and Building
Engineering, 2007. 6 (2): p. 307-314.
Witmer, L.T. and J.R.S. Brownson. System Integrative Design in the 2009 Penn State Solar Decathlon Net-
Zero Energy Home. in Proc. ASES National Solar Conference. 2010. Phoenix, AZ: American Solar
Energy Society.
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