Use of Lidar to Develop a Solar Potential Inventory for the

University of Minnesota­Twin Cities Campus

Ethan Mooar

Coleman Shepard

FNRM 3262/5262

Abstract

The objective of this project was to develop an inventory of total annual solar exposure

for buildings and parking areas at the University of Minnesota. Light Detection and Range

(Lidar) data was used to create a Digital Surface Model of the University’s Twin Cities Campus.

This was then used to perform an analysis of total annual insolation using the Solar Analyst

function of ArcGIS. Zonal statistics were run on the resulting raster output to create totals for

parking areas and individual building roof sections. This allowed for the identification of top

candidate sites for solar photovoltaic installation based on exposure that can be combined with

additional structural, architectural, and electrical interconnection information to further guide site

assessment and planning decisions.

Introduction

This project was intended to create an inventory of solar energy potential for the

University of Minnesota’s Twin Cities campus for use in evaluating area suitability and

developing plans for potential future solar development. The immediate purpose was to assist the

Solar University Network delegation, a student group of which we are both members, that is

working on developing a road map or implementation plan that can be presented to the

University to convince it to invest in at least one megawatt of solar photovoltaic generation.

Similar use of Lidar data and GIS analysis to determine solar suitability to guide development

and installation has been performed by others, including at least one other public university

(Rose, Cross, Daebel, Verderber, & Jo, 2015). To do this we focused on analyzing the solar

exposure of campus rooftops and parking lots, conventional mounting surfaces for photovoltaic

panels that are more likely to be available for solar development for practical and aesthetic

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reasons than other forms of open space like grass and plaza areas. (Figure 1 illustrates one form

of the less familiar parking canopy installation method. The amount of parking space and aisle

covered varies greatly by design and needs.) The intended output was a database table containing

theoretical maximum values for total insolation for every rooftop and parking lot on campus that

could be used for prioritizing site evaluation and presenting to stakeholders.

Figure 1: Canopy solar photovoltaic installation on a parking lot at Rutgers University (Rutgers, 2011)

Using such a table, it will be possible to quickly and easily evaluate the potential of

different candidate sites and either select or eliminate them before engaging in more time

consuming site evaluation like physical inspection or creation of financial models. The data table

will also enable potential sites to be identified that might be of lower profile and would go

unrecognized otherwise.

To create our inventory we used Lidar generated point clouds created by Fugro Horizons

Inc. on behalf of the Minnesota Department of Natural Resources (Lidar Elevation, Twin Cities

Metro Region, 2011). Lidar is an acronym for Laser Detection and Range and works as an

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integrated system where a laser connected to a computer, global positioning system, and

positional sensors emits pulses of light that strike the surface and return to the sensor. The

computer uses the time elapsed between emission and detection, the GPS coordinates of the

sensor, and the position, orientation, and altitude of the plane to create a point cloud that

indicates the elevation of each return (Campbell & Wynne, 2011). This point cloud can then be

used to create a digital surface model (DSM) in raster format, akin to a virtual “skin” connecting

points with different return values (in this case first return). Once this has been created, it is

possible to perform additional analyses on it including spatial analysis calculating solar exposure

which can then be tied to specific campus structures and locations.

Methods and Analysis

The first step we took was to identify the Lidar point cloud tiles that covered the area of

the Twin Cities campus. To do this we consulted the master tile index maintained by the

Minnesota Geospatial Information Office to identify the tile set that includes the University

(Q250K Tile Index, 2011) and, once we identified the master tile as Q4342, used its sub­tile

index file to identify the specific sections that we would need to acquire (4342 St. Paul, 2011).

Figure 2 helps illustrate the hierarchical tile organization that the Minnesota Geospatial

commons uses (Minnesota Geospatial Commons, 2013). Data directories are available to

navigate the tile system to only gather the data that is necessary. Keeping the data organized was

one of the primary objectives at this stage as with many tiles and sub­tiles, all with similar

numbers, there was a non­trivial chance of data being skipped or misplaced.

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Figure 2: Displaying part of the q4342 tile of the q250k Lidar tiles with green around rough location of

the tiles used for this project (Minnesota Geospatial Commons, 2011)

We acquired a geodatabase containing the University’s parking lots and parking ramps as

well as a geodatabase of building roof sections and their associated structural characteristics

(unnecessary for our inventory but information that will be helpful in evaluating and identifying

potential sites in the future and in eliminating promising sites that are structurally unsuitable)

from Dan Sward of University Services. This data was far superior to the building footprint data

we were initially intending to work with because it allowed us to conduct analysis at a finer scale

and to account for roof divisions, tiering, changes in pitch and aspect, and differential shading

that would otherwise be subsumed within the larger building footprint and create a potentially

4

misleading output. The segmentation within the fabric of the dataset was inherently important for

the analysis portion. There was a potentially serious limitation to some of the campus data

resulting from the digitizing process, but it primarily appeared to affect smaller segments and we

do not believe it compromised our final analysis. Specifically, the roof sections were created via

heads­up digitizing of .pdf and paper files of roof sections which may have led to the layering of

polygons over one another rather than drawing each one by its discrete boundaries. Area data

was manually entered later as an attribute and not generated from the polygon itself

(Conversation with Dan Sward, 2015, December 7). This is explored in greater depth below in

the Discussion section.

We planned to use two major software packages in our research: Esri’s ArcGIS Pro and

rapidlasso’s LAStools. Each of these packages had significant roles in the processing of the data.

The Lidar point cloud data in its compressed form(laz.) was not directly compatible with ArcGIS

Pro, but it is compatible with the LAStools toolset. Utilizing the blast2dem tool within the

software package, the Lidar point cloud data was processed into a Digital Surface Model. The

DSM was generated by using the first returns from the Lidar dataset, representing the point of

initial contact between the laser and the surface. Other classifications, such as noise and overlap

were also excluded from processing at this stage. Not omitting these could have led to a less

accurate and more noisy final output. This tool also has the capability of merging tiles together to

form larger raster files. This was beneficial when utilizing ArcGIS Pro as the main workspace.

Blast2dem contains several parameters that needed to be altered to tailor the processing

to our dataset. We set the blast2dem tool’s Step parameter to .25­meter instead of the standard

1­meter default because the available Lidar point cloud data had a density that supported this

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resolution. The pixel size was reduced by four across all of the raster tiles. Utilizing some of the

options that the tool presented, the input files were merged into four large tiles. The converted

tiles were separated based on their organizational numbering within the q4342 tile block. By

selecting only a few of the tiles to run at a time the total processing time was greatly reduced. In

the end, four Digital Surface Models were generated in a Tagged Image Format File (.tif) file

format which is compatible with most software. Figure 3 was created using the laszip tool within

the software package to display the Lidar in its original point cloud form. A portion of the DSM

output is displayed in Figure 4 to display the changes in the landscape display between the stages

of processing.

Figure 3: Lidar data tile (File: 4342­01­25_b_a.las) displaying elevation of the landscape

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Figure 4: .25­meter accuracy Digital Surface Model of TCF Bank Stadium

Access to the .25­meter Lidar data made a significant difference when generating the DSM.

Once all the DSMs were created, the tiles then were transferred into ArcGIS Pro for further

processing.

ArcGIS Pro was used to process all Digital Surface Models through the Area Solar

Radiation tool. We entered each raster tile into the tool with the same parameters to maintain

consistent results. The tool contains a number of parameters that greatly affect the outcome, and

understanding these settings is essential for using this tool. After reviewing the help files, we

deemed the default parameters to be suitable for our objective. Two parameters ­ Time

configuration and Latitude ­ were the only ones that needed to be manually changed. Latitude is

arguably the most important because it determines the relationship between the study area and

the sun throughout the year. The Time configuration parameter is responsible for the number of

intervals over which the solar exposure (insolation) is calculated. The Latitude was set at

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44.9765282279243 degrees North of the Equator by extracting that information from the file

projection metadata. The Time configuration was set at a monthly interval throughout one year.

Several of the Topographic and Radiation parameters have a large impact on the tool

output, but the default options are suitable for even complex topography (Esri, 2008). Sky

size/Resolution determine the size of the viewshed, and the default setting is 200 by 200 cells.

Essentially, this setting determines the area, in pixels, that acts as the source of solar radiation.

This parameter determines pixel resolution of the output raster file. Within Topographic

parameters, a few are of particular importance. Slope and aspect were set to be calculated from

the Digital Surface Model, and the Calculation directions were set at 32. Zenith and azimuth

divisions both help determine the number of sky sectors in the map and the default parameters

are acceptable for a wide range of topography, including urban landscapes (Esri, 2008). These

affect the angles used to calculate insolation at the designated time intervals.

It was also necessary to select a Diffuse model for radiation. The UNIFORM_SKY

parameter was set as the default which assumes radiation that is the same from all directions. The

proportion of global radiation flux is set through the Diffuse proportion parameter. In simple

terms, radiation flux can be described as the amount of radiation received by an object from a

source of energy. The setting ranges from zero to one with .3 as the default value. A .3 value

represents relatively clear sky conditions which are favorable for solar insolation. The fraction of

radiation that passes through the atmosphere is represented in the Transmissivity parameter. This

value ranges from zero to one with 0.5 representing a relatively clear sky, zero representing no

transmission, and one representing complete transmission as if there were no atmosphere. While

it would be ideal to have settings that could vary seasonally within one iteration of the program,

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that is not currently available and the single settings were sufficient for our purposes of

generating a rough inventory value.

To summarize what we did with the ArcGIS Pro Area Solar Radiation tool: we

sequentially ran our DSM tiles through it, simulating solar exposure from a 200 by 200 section

of the sky over the course of a year based on atmospheric and diffusion assumptions we selected.

It calculated the path of the sun over the course of days and months and used the DSM to create a

viewshed, slowly generating an annual solar exposure value for each point (it results in a 1­meter

resolution raster output with a value representing total watt­hours per square meter). Once this

result was calculated, we were ready to proceed with further analysis.

After all the Digital Surface Models were processed, the output was comprised of four

raster (.tif) files representing total annual insolation within the study area. Figure 5 shows a

small portion of the Area Solar Radiation tool output to provide an example of the level of detail

(an image of the entire study area is visible on the cover sheet). In Figure 5 , blue represents areas

that do not receive a significant amount of solar radiation while the color red displays the areas

that receive a large an amount of solar radiation. The other colors progress from orange through

yellow and green in order from highest to lowest insolation values.

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Figure 5: Solar Insolation Output for TCF Bank Stadium generated from .25­meter resolution Lidar data

The output from the Area Solar Radiation tool presented a visually appealing display, but only

information on points of solar radiation could be derived from it. Using the roof section and

parking facility data, zonal statistics were then calculated for each polygon extent.

We used ArcGIS Pro’s Zonal Statistics toolkit to create a statistical summary of the Solar

Radiation Raster for each of the polygons in our geodatabases. This allowed us to determine the

total insolation value in watt­hours per square meter for each roof and parking section. For the

parking areas this was fairly straightforward, the parking polygons were all uniquely numbered

or named and directly correlated one to one. For the roof sections it was a little more

complicated. We were interested in the building name as well as the roof section information so

we needed to join the zonal statistics table to the building container table to make roof

identification, conceptualization, and display easier. We accomplished this by using the object

ID field as a key. Finally, we filtered out all features with a total area less than 700 square

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meters, which we established as the minimum space required to host a 100 kW array (Rose et at.,

2015) without taking account for roof dimensions, protrusions, safety setback requirements, or

canopy mount spacing that may increase the minimum necessary size.

Once we had full zonal statistics reports for the campus areas of interest, we exported the

raw results to Microsoft Excel for manipulation, clean up, and display. In Excel, we removed

duplicative and extraneous fields such as object numbers, geometry classifiers, and certain

dimensional characteristics. We also converted the total annual insolation results from watt­hours

per square meter to a more standard and familiar kilowatt­hours per square meter. This allowed

us to generate cleaner tabular data and sort results in a more accessible way for interpretation.

Once we had created all of the tables, it was a simple procedure to use Excel’s data sort function

to create a ranked list based on the total insolation value.

Data and Results

While there were a few buildings that lay outside the area of our DSM, we had almost

total coverage of the Twin Cities campus area and were able to determine annual insolation

values and relative solar suitability for almost every target site. We determined that the total

insolation for campus parking structures (lots, garages, ramps, and individually maintained

spaces) over the 700 square meter threshold was 3,599,568,714 kWh/m2, the total for campus

roof sections of the same size was 919,744,847 kWh/m2, and the grand total for all target

structures over 700 square meters was 4,519,313,561 kWh/m2. We also identified the ten 1

1 For reference, the average residential customer of a United States electric utility uses 10,932 kWh per month (USEIA, 2015). Photovoltaic cells for commercial use are typically fifteen percent efficient though at least one major installer is working on twenty­two percent efficient panels for deployment (Murphy, 2011; Cardwell, 2015). Based on these numbers, our inventory estimate shows a potential to generate between 539,935,307 kWh and 791,905,117 kWh per year from arrays of 100 kW capacity or larger. This does not include area lost to setbacks, obstructions. or structural limitations and assumes accuracy and validity of the model used for solar analysis..

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parking areas and fifteen roof sections that have the best annual solar exposure (Table 1 and

Table 2 ). While these determinations do not take into account the structural capacity, electrical

interconnection issues, microgrid capacity, historical designation issues, or financing and

installation concerns, they do provide a good starting point for evaluating initial target areas. For

complete campus results, please see the Appendices.

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Discussion

Based on our data and analysis, we would recommend making the State Fair parking lots

and the TCF Bank Stadium lots the primary targets for solar development at the University. They

dominated our rankings of the parking structures and had by far the best insolation value of any

structure we examined. For buildings, while there were some we were not expecting to see,

primarily because they are not high profile, inspecting their characteristics revealed a few traits

that they tended to share: large area, general east­west orientation, and a flat or southern aspect.

They also tended to be the tallest structure in their immediate area. While these might seem

obvious, shadows can track in unanticipated ways and roofs can have surface obstacles,

obstructions, and parapets that can cast shadows not apparent from ground observation. They can

also be divided or tiered in ways that are not apparent to a ground viewer. For further

investigation, we would recommend taking an insolation meter to test sites to evaluate the

accuracy of the model’s predictions. This is particularly important because there were some

issues, discussed below, with generating zonal statistics for some sections.

There might have been some small errors or assumptions in the choices we made in

setting the Solar Analyst parameters including accepting the defaults or choosing our own, but

these are unlikely to have a huge impact on the majority of our analysis. The same assumptions

were used on the entire study area, making the data resulting from it consistent with respect to

potential errors or bias. Furthermore, we were primarily concerned with establishing a ranking

based on gross suitability and not fully verified and 100% accurate estimates. Site values can

also be confirmed by field survey, which is a necessary part of a full scale solar development

project workflow anyway. While setting choices for the transmissivity factor and the diffuse

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proportion can have large effects, especially in climates like Minnesota that have strong seasonal

variability in cloud cover, the overall output is a fairly good estimate, and may even understate

the actual insolation because Solar Analyst does not calculate ray reflectance, indirect irradiation,

or use other ray tracing­based evaluations (Jakubiec & Reinhart, 2012).

We did encounter some data irregularities that had varying amounts of influence on our

analysis. The most important of these errors is the age of the Lidar dataset. At this point, the

Lidar data is four years old and there have been a number of buildings completed or begun since

then, most notably the new recreation center and part of the biomedical complex. These would

both introduce new roof areas to the DSM (the Recreation Center is included in the roof tile data

but currently performs a calculation on the insolation of the construction site) and also affect the

shading of nearby facilities, particularly the Aquatic Center.

There were also a few problems with buildings and roof information. Some buildings

were located outside the boundaries of the DSM area we were using, but there were only a few

of substantial enough size to be considered missing, including the Community University Health

Care Center on Franklin Avenue and the St. Anthony Falls Laboratory. Some roof sections were

not completely aligned with the building area, but were close enough for a rough estimate. For a

more comprehensive site study with more time and resources these could be digitized and/or

adjusted to more closely align with the DSM. Some buildings also had layered or overlapping

roof segmentation that may have introduced unexpected artifacts into the zonal statistics

analysis, but this would need to be confirmed by examining the zonal statistics tool and the roof

tile database more closely. Finally, some buildings, such as Williams Arena, are considered one

roof section but, due to the roof shape, are only suitable on one face and thus would be better

14

considered separately. Some of the tile­roofed buildings on the St. Paul campus had similar

issues. Again, this could be resolved with more time by manually dividing the roof sections, but

was not significant enough at this stage of analysis to be of outsize concern.

Finally, there were some instances where the Lidar data introduced discrepancies or

apparent errors in the return measurements. These were particularly notable in the river, where

there were data holes and strange reflectance issues, probably due to laser interactions with the

water surface. Other notable problems were seen with McNamara (the image has an unexpected

and irregular dark spot, possibly due to surface type and angular features) and some buildings

that were determined to have glass roofs or skylights (Figure 6 ).

Figure 6: Solar Analyst output showing rec center construction site and McNamara abnormality.

There were some issues with the zonal statistics tool when generating area sums for

individual roof sections. The first concerned the way small roof sections processed, revealing

15

implausibly large insolation values. What were particularly notable were the values for Sheep

Research roof section 392­C, a 3 square meter building connector that appeared in the top fifteen

roof section rankings when all roof areas were considered together. Another flag was the value

for Moos Health Science Tower 142­AT, an 86 square meter north facing section that also

appeared in the top fifteen list (Table 3 ). This could be an artifact of how the polygons were

constructed or layers or there may be a series of sequential additions that the zonal statistics tool

performed when generating the totals. Upon investigating some of the other discrepant entries

and speaking with Dan Sward, it is likely that these errors result from the digitization process. It

is likely (but not confirmed) that large polygons were initially created and then others layered on

top leaving the “small” sections exposed rather than tracing small sections individually. This

would explain why most of the problems involved small sections that were part of complicated

roof structures with considerable overlap. We believe that this is not a problem for our final

output because we elected to focus on roof sections with at least 700 square meters of surface

area which resulted in fewer sections with overlap or complicated arrangements.

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There was also a suspicious result at the top of our buildings list in Table 1 , with

University Stores North showing a surprisingly high value. It is a large, east­west oriented,

unobstructed building but even considering all that, a value of over 111 million annual kilowatts

per square meter struck us as implausible. However, comparing it to the values for parking lots

shows that unobstructed lots of similar size do have similar values. Without more information we

did not feel it was appropriate to remove it from our results. We did, however, remove the

Terrace Co­op housing roof from our final results because, while it has a large area, it consists of

many small roof sections that are kept as part of one feature in the University’s records. Thus,

while on paper it appears to exceed the 700 square meter cutoff, in reality it does not.

A larger concern for accuracy in the final analysis would involve structures like the

Washington Ave. Bridge that appear to be a continuous roof section but are actually maintained

in multiple segments by the University. In the case of the bridge, some of the sections were

under our 700 square meter cutoff and were excluded, though an exception could potentially be

made for treating the bridge and a few other roofs as one entity for analytical purposes. Treating

the entire bridge as one section yields a total annual insolation of 46,693,555 kWh/m2. This

would place the Washington Ave. Bridge in second place in our roof analysis.

Future work in this area that would assist the University and the SUN Delegation in its

work would be to combine the insolation data with additional building and lot features such as

surface type, structural information (underlayment and reinforcement), historical designation,

and which campus microgrid each unit was a part of. It would also be useful to add the mean

elevation of each roof section, derived from the DSM, to the attribute table because lifting solar

panels above a certain height for installation may make a project financially or physically

17

infeasible. The actual electric consumption values for buildings beneath or near potential host

sites could also be added to the database records allowing for the comparison between generation

capacity and demand. This could all then be combined into a single database or app for online

display and querying by interested users looking to quickly determine the status of campus sites.

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References Campbell, J. B., Wynne, R. H. (2011). Lidar. In Campbell, J. B., Wynne, R. H. (Eds.),

Introduction to Remote Sensing (5th ed.) (pp. 243­256). New York, NY: The Guilford Press.

Cardwell, D. (2015, October 3). Solar company reports a production breakthrough. The New

York Times , B2. ESRI. (2008). Area solar radiation. Retrieved 11/17, 2015, from

http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=Area_Solar_Radiation How much electricity does an American home use? (2015, October 21). In United States Energy

Information Office, Frequently Asked Questions . Retrieved from https://www.eia.gov/tools/faqs/faq.cfm?id=97&t=3.

Jakubiec, J. A., & Reinhart, C. F. (2012). Towards validated urban photovoltaic potential and

solar radiation maps based on lidar measurements, GIS data, and hourly daysim simulations. Proceedings of SimBuild, Madison, Wisconsin, 1­10.

Lidar Elevation, Twin Cities Metro Region. (2011). Retrieved from

http://www.mngeo.state.mn.us/chouse/metadata/Lidar_metro2011.html. Lidar Tile Index 4342 St. Paul. (2011). Retrieved from

ftp.lmic.state.mn.us/pub/data/elevation/Lidar/q250k/q4342/Lidar_tile_index__4342.pdf. Minnesota Department of Natural Resources. (2011). Lidar elevation, twin cities metro region,

minnesota, 2011. Retrieved 10/29, 2015, from http://www.mngeo.state.mn.us/chouse/metadata/Lidar_metro2011.html#Distribution_Information

Minnesota Geospatial Commons. (2013). Lidar elevation data for minnesota. Retrieved 11/06,

2015, from http://www.mngeo.state.mn.us/chouse/elevation/Lidar.html Murphy, T. (2011, September 21). Don’t be a PV efficiency snob [web log comment]. Retrieved

from http://physics.ucsd.edu/do­the­math/2011/09/dont­be­a­pv­efficiency­snob/. Q250K Tile Index. (2011). Retrieved from

ftp://ftp.lmic.state.mn.us/pub/data/elevation/Lidar/q250k/q250k_tile_index.pdf.

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Rose, Z., Cross, J., Daebel, E., Verderber, A., & Jo, J. (2015). A Geographic Systems Approach to Developing a Distributed Generation System in the Town of Normal, Illinois . Retrieved from http://www.aashe.org/files/resources/student­research/2009/gis_solar_iradiance_study_ton_solar_final.pdf.

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project. Retrieved from http://news.rutgers.edu/news­releases/2011/04/rutgers­board­of­gov­20110405#.VmzIofkrJ1g.

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Appendix A: All Roof Sections over 700 Square Meters

Building Name Full ID Roof Section Area

(m2) Annual Insolation

(kWh/m2)

UNIVERSITY STORES NORTH 121­A 6,487 111,446,649

CENTER FOR MAGNETIC RESONANCE

RESEARCH 180­B 2,898 45,785,065

CANCER CARDIO BUILDING 153­A 8,010 36,472,371

TRACK AND FIELD STORAGE BUILDING 157­A 957 27,145,166

REUSE PROGRAM AND AHC

WAREHOUSE 154­A 4,484 26,202,908

FERGUSON HALL 215­A 1,197 26,196,522

SIEBERT STADIUM 138­A 980 24,354,747

COMO MARRIED STUDENT HOUSING 151­D 1,093 23,602,002

UNIVERSITY AQUATIC CENTER 167­A 5,239 22,987,087

THOMPSON CTR FOR

ENVIRONMENTAL MGMT 177­A 3,713 22,309,996

POULTRY TEACHING AND RESEARCH

FACILITY 463­A 2,439 21,061,829

UNIVERSITY STORES SOUTH 098­A 3,067 18,163,337

PRINTING SERVICES BUILDING 134­A 6,871 17,303,952

MARIUCCI ARENA 176­C 2,585 16,877,725

LIND HALL 031­A 938 15,456,618

RAPSON HALL, RALPH 112­B 980 15,426,538

COMO MARRIED STUDENT HOUSING 151­J 996 15,210,391

SHEEP RESEARCH 392­B 1,316 15,017,520

SMITH HALL 035­C 2,192 14,777,686

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FOLWELL HALL 022­A 1,742 14,233,941

LIONS RESEARCH BLDG/MCGUIRE TR

FACILITY 174­A 1,071 14,098,865

SWINE RESEARCH FACILITY 455­A 942 13,938,414

MARIUCCI ARENA 176­A 1,457 13,215,721

UNIVERSITY RECREATION CENTER 169­B 4,178 12,932,734

ANIMAL SCIENCE/VETERINARY

MEDICINE 416­A 2,102 10,986,036

SANFORD HALL 028­I 2,050 10,889,890

COMO COMMUNITY CENTER 150­A 884 10,676,838

CATTLE FEEDING SHED NO 2 333­A 715 10,367,404

VETERINARY MEDICAL CENTER NORTH 427­E 821 9,486,814

MIDDLEBROOK HALL 208­Q 802 8,847,980

WEST BANK OFFICE BUILDING 218­A 1,073 8,771,773

PLANT GROWTH FACILITIES ­ WEST 369­F 887 8,399,295

MOLECULAR AND CELLULAR BIOLOGY 186­A 1,974 8,238,247

TCF BANK STADIUM 196­A 11,695 7,428,893

ARMORY BUILDING 011­E 1,839 7,091,797

UNIVERSITY STORES SOUTH 098­C 1,611 6,792,144

MAYO BUILDING 074­V

V 992 6,720,762

COMO MARRIED STUDENT HOUSING 151­H 1,804 6,611,470

BIERMAN FIELD ATHLETIC BUILDING 139­A 1,786 6,531,976

LARGE ANIMAL HOLDING 417­A 1,043 6,356,599

HUMPHREY SCHOOL OF PUBLIC

AFFAIRS 216­A 3,799 6,118,958

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UNIVERSITY RECREATION CENTER 169­A 3,581 6,066,388

COMO MARRIED STUDENT HOUSING 151­E 1,545 5,345,955

WALTER LIBRARY 042­D 867 5,259,219

AGRICULTURAL CHEMICAL STORAGE

BUILDING 478­A 879 5,207,242

PIONEER HALL 052­A 1,929 5,193,575

SNYDER HALL 352­A 763 5,081,793

WEAVER ­ DENSFORD HALL 147­D 809 4,928,155

CARLSON SCHOOL OF MANAGEMENT 249­A 935 4,551,530

VETERINARY MEDICAL CENTER NORTH 427­G 1,290 4,535,728

CARLSON SCHOOL OF MANAGEMENT 249­B 3,750 4,109,576

RIDDER ARENA & BASELINE TENNIS

FACILITY 181­B 5,548 3,883,340

ROY WILKINS HALL 030­A 1,801 3,875,365

WEST BANK SKYWAY 212­A 755 3,871,718

TATE LABORATORY OF PHYSICS 049­A 1,125 3,638,645

WMBB­WALLIN MEDICAL BIOSCIENCES

BLDG 197­A 2,224 3,612,249

HANSON HALL, HERBERT M., JR. 250­K 746 3,605,808

ANIMAL WASTE TREATMENT CENTER 431­A 1,329 3,567,813

MCNEAL HALL 338­K 1,351 3,541,741

UNIVERSITY OFFICE PLAZA 192­A 2,318 3,538,827

COMO MARRIED STUDENT HOUSING 151­K 1,427 3,239,272

<Null> 299­A 1,051 3,079,671

WASHINGTON AVE PED BRIDGE 123­H 1,474 2,991,368

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FACILITIES MANAGEMENT 379­B 1,078 2,917,740

FORD HALL 071­A 1,037 2,881,154

PILLSBURY HALL 002­A 1,450 2,789,053

MARIUCCI ARENA 176­B 1,492 2,759,902

FOOD OPERATIONS BUILDING 113­E 717 2,715,637

ENGINEERING AND FISHERIES

LABORATORY 335­B 872 2,460,423

LEARNING AND ENVIRONMENTAL

SCIENCES 426­A 1,738 2,371,515

MOLECULAR AND CELLULAR BIOLOGY 186­B 932 2,128,391

VETERINARY MEDICAL CENTER SOUTH 371­C 820 2,112,915

CHILD DEVELOPMENT CENTER 173­A 1,667 2,072,581

DAIRY CATTLE TEACHING & RESEARCH

CENTER 430­A 2,594 1,889,832

TED MANN CONCERT HALL 273­D 821 1,881,522

FLEET SERVICES FACILITY 185­A 2,367 1,866,841

YUDOF HALL, MARK G. 189­A 1,241 1,845,328

ST. PAUL GYMNASIUM 342­A 1,239 1,831,487

COMO MARRIED STUDENT HOUSING 151­M 882 1,771,366

COOKE HALL 056­A 1,065 1,763,252

RUTTAN HALL (FORMER CLASSROOM

OFFICE BLDG) 412­C 1,391 1,659,767

BOYNTON HEALTH SERVICE 070­A 735 1,642,853

EDUCATION SCIENCES BUILDING 041­A 1,447 1,594,555

ST. PAUL GYMNASIUM 342­B 1,244 1,588,428

FACILITIES MANAGEMENT 379­A 1,936 1,581,308

24

HAECKER HALL 350­E 1,114 1,575,103

LIONS RESEARCH BLDG/MCGUIRE TR

FACILITY 174­G 982 1,535,863

COMO MARRIED STUDENT HOUSING 151­G 1,604 1,530,443

ANIMAL ARENA 422­A 778 1,477,525

PLANT GROWTH FACILITIES ­ EAST 485­A 1,093 1,432,253

COMO MARRIED STUDENT HOUSING 151­A 1,437 1,429,081

COMO MARRIED STUDENT HOUSING 151­L 1,085 1,412,979

MARIUCCI ARENA 176­F 2,642 1,271,111

ANIMAL SHELTER 456­A 835 1,232,177

PATTEE HALL 003­A 1,001 1,223,757

DWAN VARIETY CLUB CARDIO

RESEARCH CENTER 143­J 855 1,204,300

17TH AVENUE RESIDENCE HALL 129­A 3,537 1,186,150

ELLIOTT HALL 020­C 746 1,163,134

FRONTIER HALL 110­A 2,359 1,130,703

RAPSON HALL, RALPH 112­A 2,301 1,125,866

TATE LABORATORY OF PHYSICS 049­C 1,756 1,084,328

ENGINEERING AND FISHERIES

LABORATORY 335­A 1,640 1,075,490

CONTINUING EDUCATION CENTER,

EARLE BROWN 420­A 742 996,721

JACKSON HALL 032­A 1,940 990,968

MECHANICAL ENGINEERING BUILDING 265­I 911 950,714

CROPS RESEARCH BUILDING 389­B 1,100 901,773

FITCH AVE UTILITY BLDG 445­A 1,567 872,250

25

PHYSICS AND NANOTECHNOLOGY 155­A 3,219 867,923

MAYO BUILDING 074­TT 1,327 860,636

WEST BANK OFFICE BUILDING 218­F 1,054 827,773

COFFEY HALL 322­D 2,582 813,932

REGIS CENTER FOR ART ­ WEST 242­C 743 787,467

BLEGEN HALL 203­A 1,355 784,258

PLANT GROWTH FACILITIES ­ WEST 369­A 827 777,990

WASHINGTON AVE PED BRIDGE 123­G 887 712,115

CENTER FOR MAGNETIC RESONANCE

RESEARCH 180­M 1,388 699,518

GIBSON/NAGURSKI FOOTBALL

PRACTICE FCLTY 159­E 6,974 676,112

MORRILL HALL 046­F 759 654,652

WILLIAMS ARENA 050­A 924 635,657

CROP SERVICE BUILDING 388­B 1,000 632,221

SOUTHEAST STEAM PLANT 059­A 4,772 616,437

COFFMAN MEMORIAL UNION 064­A 738 614,762

KAUFERT LABORATORY 387­E 773 594,887

PEIK GYMNASIUM 268­A 800 562,600

PIONEER HALL 052­E 1,951 544,740

MN CROP IMPROVEMENT CTR 474­A 886 524,843

VETERINARY MEDICAL CENTER NORTH 427­B 2,674 524,010

PETERS HALL 372­I 752 521,582

MINNESOTA MOLECULAR AND

CELLULAR THERAPY 436­A 1,110 519,767

26

JOHNSTON HALL 073­C 780 508,176

PLANT GROWTH FACILITIES ­ WEST 370­A 1,223 499,798

CARLSON SCHOOL OF MANAGEMENT 249­C 882 499,749

BAILEY DINING CENTER 390­A 1,102 485,685

DIEHL HALL LABORATORIES 111­D 1,426 465,316

KELLER HALL, KENNETH H. 165­B 1,381 462,898

COMO MARRIED STUDENT HOUSING 151­B 1,695 457,715

FOOD SCIENCE AND NUTRITION 381­A 2,714 454,862

FOOD OPERATIONS BUILDING 113­B 1,092 446,320

GIBSON/NAGURSKI FOOTBALL

PRACTICE FCLTY 159­A 1,645 435,515

VETERINARY SCIENCE 374­B 767 414,473

FRASER HALL 051­B 1,118 412,315

VETERINARY DIAGNOSTIC

LABORATORY 385­D 1,071 398,865

WASHINGTON AVE PED BRIDGE 123­F 1,778 383,305

MCNEAL HALL 338­A 839 376,628

ALDERMAN HALL 394­A 1,538 373,245

TERRITORIAL HALL 105­B 1,543 343,890

PEIK HALL 267­A 1,195 343,850

TERRITORIAL HALL 105­G 758 334,114

INFORMATION TECHNOLOGY 184­A 1,933 331,132

ANDERSEN LIBRARY, ELMER L. 220­C 1,172 317,669

APPLEBY HALL 037­A 817 309,014

VETERINARY MEDICAL CENTER SOUTH 371­G 802 306,123

27

POMEROY STUDENT­ALUMNI

LEARNING, BEN 326­A 759 302,680

KOLTHOFF HALL 122­A 728 301,301

RESEARCH ANIMAL RESOURCES

BUILDING 419­A 851 279,325

MONDALE HALL 211­LL 948 260,268

CROP SERVICE BUILDING 388­A 978 260,079

COMMONWEALTH TERRACE COMM

CTR 418­A 773 230,637

DONHOWE BUILDING 044­A 1,276 215,740

WILLIAMS ARENA 050­C 8,817 208,097

NORTHROP MEMORIAL AUDITORIUM 053­D 2,329 201,217

MIDDLEBROOK HALL 208­J 775 200,714

MONDALE HALL 211­G

G 1,450 193,683

REGIS CENTER FOR ART ­ EAST 241­D 1,195 192,438

GREEN HALL 357­A 833 191,630

WALTER LIBRARY 042­C 1,036 190,495

MAYO BUILDING 074­Q

Q 844 174,801

CARGILL BLDG­MICROBIAL & PLANT

GENO 439­A 1,918 162,309

MARIUCCI ARENA 176­D 1,400 155,169

FOOD OPERATIONS BUILDING 113­A 1,004 137,398

REGIS CENTER FOR ART ­ EAST 241­G 745 120,897

NICHOLSON HALL 005­A 1,730 118,767

NILS HASSELMO HALL 178­E 1,908 116,150

28

FOOD OPERATIONS BUILDING 113­C 1,255 111,948

717 DELAWARE BUILDING 193­A 2,484 103,496

MARIUCCI ARENA 176­E 1,428 96,643

INFORMATION TECHNOLOGY 184­E 756 93,050

UNIVERSITY OF MINNESOTA FIELD

HOUSE 067­A 7,615 90,061

HANSON HALL, HERBERT M., JR. 250­J 871 85,554

ROBBIE STADIUM, ELIZABETH LYLE 481­A 740 78,342

BIOLOGICAL SCIENCE GREENHOUSE 414­A 1,102 73,586

ELLIOTT HALL 020­H 1,557 72,492

BEEF AND CATTLE 302­A 1,174 60,150

RAPSON HALL, RALPH 112­C 1,276 52,974

COMO MARRIED STUDENT HOUSING 151­F 1,895 52,954

BIOSYSTEMS AND AGRICULTURAL

ENGINEERING 334­A 1,371 48,603

COMO MARRIED STUDENT HOUSING 151­I 1,111 39,187

MECHANICAL ENGINEERING BUILDING 265­R 956 30,564

RIDDER ARENA & BASELINE TENNIS

FACILITY 181­A 5,794 26,263

FOOD OPERATIONS BUILDING 113­D 1,365 23,537

CHILD DEVELOPMENT BUILDING 019­D 903 20,984

DIEHL HALL LABORATORIES 111­A 707 17,723

EQUINE CENTER, LOUIS AND DOUG

LEATHERDALE 486­A 5,768 15,970

COMO RECYCLING FACILITY 099­A 1,660 14,825

CENTER FOR MAGNETIC RESONANCE 180­N 854 12,518

29

RESEARCH

PLANT GROWTH FACILITIES ­ WEST 369­E 889 5,884

RESEARCH ANIMAL RESOURCES

BUILDING 419­B 811 1,024

Roof Total 919,744,847

30

Appendix B: All Parking Sections over 700 Square meters

Structure Name Facility Type Parking Area (m2) Annual Insolation (kWh/m2)

Lot SC108 Lot 13,277 231,652,439

Maroon Lot Lot 12,684 213,102,561

Oak Street Parking Ramp Ramp 12,359 186,669,450

Ski­U­Mah Lot Lot 9,703 170,404,557

Lot S101 Lot 8,969 153,203,079

Victory Lot Lot 7,445 131,760,297

Lot 33 Lot 7,385 126,283,409

Minnesota Lot Lot 6,502 114,745,831

Gold Lot Lot 6,809 113,556,764

Lot S104 Lot 6,930 111,808,880

Lot C50 Lot 6,933 102,008,755

Washington Avenue Parking

Ramp Ramp 6,512 100,258,466

Gortner Avenue Parking Ramp Ramp 6,054 100,108,211

Fourth Street Parking Ramp Ramp 5,888 98,781,143

East River Road Garage Garage 7,306 97,066,093

West Bank Office Building Lot Lot 5,523 88,306,903

Lot C86 Lot 4,657 77,173,603

Lot C58 Lot 4,956 74,201,317

Lot C57 Lot 4,340 72,424,589

Gopher Lot Lot 4,364 67,914,557

Twenty­First Avenue Ramp Ramp 4,265 66,845,589

Lot SC102 Lot 3,810 63,464,968

31

Lot SC150 Lot 3,887 62,031,511

Lot SC153 Lot 3,822 60,886,345

Gateway Lot Lot 3,148 53,998,709

Lot SC100 Lot 3,075 53,774,316

Discovery Lot Lot 3,041 51,063,591

Lot C74 Lot 3,226 48,920,661

19th Avenue Parking Ramp Ramp 3,110 47,881,542

University Avenue Parking

Ramp Ramp 3,066 46,334,075

Lot SC175 Lot 3,080 40,719,775

Nolte Center Garage ­ Ncg Garage 2,769 38,772,922

Northrop Memorial Auditorium

Garage Garage 2,599 38,132,721

717 Delaware St. Se Garage Garage 2,628 37,786,843

Lot C59 Lot 2,242 36,768,089

Lot C55 Lot 2,341 36,620,166

Church Street Garage Garage 2,820 34,242,785

Lot SC171 Lot 2,004 32,867,889

University Office Plaza Garage Garage 2,045 31,863,656

Lot SC109 Lot 1,746 29,146,165

West Bank Office Building

Parking Ramp Ramp 1,694 21,318,706

Lot SC169 Lot 1,424 21,114,712

Lot C89 Lot 1,822 19,789,181

Lot C78 Lot 1,544 19,512,417

32

Weisman Art Museum Garage ­

Amg Garage 1,329 19,171,803

Lot S106 Lot 1,121 18,723,593

Lot C61 Lot 1,103 15,116,008

Lot SC190 Lot 808 14,323,163

Lot C56 Lot 785 13,313,142

Lot C65 Lot 888 12,825,505

Lot SC157 Lot 842 12,732,695

Lot C25 Lot 1,489 11,914,444

Lot SC172 Lot 754 11,348,196

Lot SC156 Lot 924 10,781,469

Lot C44 Lot 732 9,730,753

Lot C43 Lot 1,024 9,328,108

Lot C2 Lot 740 8,773,706

Lot 94 Lot 4,476 3,471,061

Lot C95 Lot 975 2,726,830

Parking Total 3,599,568,714

33