HOW RAPID ENERGY MODELING CAN HELP DESIGNERS IMPROVE BUILDING PERFORMANCE IN EXISTING BUILDINGS

8/19/2019 HOW RAPID ENERGY MODELING CAN HELP DESIGNERS IMPROVE BUILDING PERFORMANCE IN EXISTING BUILDINGS

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Victor Okhoya November 2014

CARNEGIE MELLON UNIVERSITY

DOCTOR OF PROFESSIONAL PRACTICE

AREAS OF PRACTICE ASSIGNMENT

HOW RAPID ENERGY MODELING CAN HELP DESIGNERS IMPROVE BUILDING PERFORMANCE IN

EXISTING BUILDINGS

ABSTRACT

This paper introduces the concept of Rapid Energy Modeling as a possible solution to the

perceived difficulty of performing energy analysis studies on existing buildings. It begins by

describing the problem and suggests that traditional approaches like energy audits are time

and resource intensive. It then defines the concept of Rapid Energy Modeling. Three

approaches to energy analysis of existing buildings, including Rapid Energy Modeling, are

discussed and compared. Finally a comparative return on investment analysis is made by

comparing Rapid Energy Modeling to traditional energy analysis software tools.

INTRODUCTION

There is a perception in the design community that building performance analysis (BPA) for

existing buildings is complex, time consuming and difficult to master. For example, according1

to the Canadian Industry Program for Energy Conservation (CIPEC) , a traditional method of2

energy analysis of existing buildings, the energy audit, consists of the following ten steps:

1. Conduct a condition survey

2. Establish the audit mandate

3. Establish the audit scope

4. Analyse energy consumption and costs

5. Compare energy performance

6. Profile energy use patterns

7. Inventory energy use

8. Identify Energy Management Opportunities

9. Assess the benefits

10. Report for action

These processes can take months to complete, are expensive and require a high level of

expertise. This problem of perceived difficulty of BPA in existing buildings has began being

addressed by the digital design technology community. Solutions are being sought that bring

the benefits of BPA without excessive overhead in terms of learning and using the tools. Onesuch promising solution is Rapid Energy Modeling (REM).

1 Stumpf et al., 2011, p2.2 See Natural Resources Canada , 2011.

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RAPID ENERGY MODELING

According to an Autodesk white paper Streamlining Energy Analysis of Existing Buildings with

Rapid Energy Modeling

Rapid Energy Modeling refers to a streamlined and scalable3

approach to performing energy assessments of existing buildings. Autodesk presents a three

step process for performing such assessments:

● Capturing existing building conditions

● Developing a 3D model of the building

● Performing analysis on the building

Existing conditions can be captured using digital photographs, aerial and satellite images,

laser scanning or, as we will see, thermal imaging technology.

The 3D model can be prepared using 3D modeling software that leverages captured digital

data as a background for model development. Reality capture tools like Insight3D, Agisoft

Photoscan and Autodesk Imagemodeler can also be used to help convert photo images into3D models.

Once a 3D model has been prepared energy analysis simulations can be run using an

appropriate BPA tool. Outputs from such analyses include energy use intensities, annual

energy consumption by fuel type, heating and cooling loads, carbon emissions among others.

The key benefit of REM is time and, therefore, cost savings. Users report that the REM

exercise for an average sized building (such as a typical three story office building) can be

performed in hours and days rather than the weeks and months of current processes.

The accuracy of analysis results from REM correlates favorably with real measurements as

discussed in the Department of Defence study below. Accuracy of building geometry

generated from reality capture methods is also quite high with modeled areas being within 7%

of the actual area according to the Autodesk study .4

In this paper we will look at three approaches to energy analysis of existing buildings:

● Energy Audits

● Traditional Software Approaches

● Rapid Energy Modeling by using Thermal Imaging as well as by using Autodesk

Software Workflows

3 See Autodesk, 2011.4 See Autodesk, 2011.

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We will compare these approaches in terms of cost, training time, time to perform an analysis

and accuracy of results. Finally, we will perform a return on investment comparison between

traditional software approaches and REM methods.

We will use the following studies as our case study references :5

● Rapid Energy Modeling Workflow Demonstration

, a US Department of Defence study.

● Rapid 3D Energy Performance Modeling of Existing Buildings using Thermal and

Digital Imagery, a study done at Virginia Polytechnic Institute and State University.

● Streamlining Energy Analysis of Existing Buildings with Rapid Energy Modeling

, an

Autodesk study.

APPROACHES TO ENERGY ANALYSIS OF EXISTING BUILDINGS

Energy Audits

An energy audit is an inspection, survey and analysis of energy

flows for energy conservationin a building, process or system to reduce the amount of energy input into the system without

negatively affecting the output(s) . According to the US DoD study, three levels of energy6

audits are typically used based on the American Society of Heating, Refrigerating and

Air-Conditioning Engineers ( ASHRAE) standard: walk through (ASHRAE Level 1), general

(ASHRAE Level 2), and investment grade (ASHRAE Level 3) .7

● Level 1, which is a rapid assessment of building energy systems done using a

walkthrough as well as energy benchmarking. This takes 1 -2 days and costs 500.00 -

700.00 per day.

● Level 2, which is a more detailed building survey of systems and operations. It

includes a breakdown of energy uses and sources, identification of energy

conservation measures and savings and identification of operational discrepancies.

This takes 3 - 10 days and costs 500.00 - 700.00 per day or 1500.00 - 7000.00 per

building.

● Level 3, which focuses on a whole building computer simulation and models the way

the building would respond to proposed energy saving measures. It requires longer

term data processing, computer models calibrated with field data and bid-level

construction cost estimating. This takes 10 - 50 days at a cost of 500.00 - 700.00 per

day.

5 See the References section at the end of the paper.6 Energy Audit. In Wikipedia. Retrieved in December 2014 from http://en.wikipedia.org/wiki/Energy_Audit .7 Rupnow, J. & Sullivan, J., 2013, p80.

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The DoD does not feel that REM workflows correspond directly to any of the levels of audit.

However, its outputs are a closer match to a level 2 audit and we will use this as a basis of

comparison.

Traditional Software Processes

Traditional Software approaches involve modeling the existing building conditions in a pieceof software and then running energy analysis exercises on this model. Several software

solutions exist for performing these analyses. More established tools include DOE,

Energyplus and Equest. These are typically free but have older interfaces and some are even

MS-DOS based. Newer commercial tools include Designbuilder, Aecosim and IES VE.

Traditional software approaches can also be used as part of the energy auditing process.

Traditional software approaches, when used on existing buildings involve gathering existing

building condition data and then inputting this data into the energy analysis tool. This usually

takes the form of as-built or record drawings in CAD or PDF formats being redrawn in the

energy analysis tools. Sometimes, building measurements need to be undertaken first. Oncethe existing conditions have been input, analysis simulations can be run and results reported.

Traditional software approaches provide the following challenges to the analysis of existing

buildings. First, the commercial versions carry a cost as high as thousands of dollars per

license. Second, these tools take time to learn with the learning curve being in weeks and

months for the more complex interfaces. Third, modeling existing conditions can take a long

time. Depending on the quality of existing conditions data this can range from days to weeks.

Finally, the time to perform analysis can also be lengthy depending on the complexity of the

energy simulation being run and the specifications of the hardware running it.

Rapid Energy Modeling using Thermal Imagery

We will refer to the Virginia Tech study conducted by Ham and Golparvar-Fard as an8

example of REM using thermal imagery. The study uses digital and thermal imagery to rapidly

create a thermal model. The study notes that there are several challenges with existing

approaches to the modeling process for analyzing energy performance of existing buildings.

These challenges include:

1. Current energy modeling practices are time consuming and labor intensive. The

process of constructing models often takes weeks or months making it suitable mainly

for high-budget projects.

2. These energy models make assumptions that do not capture the variances of reality.

Sometimes these variances can be significant.

3. Creating these models requires skill and expertise that many designers simply do not

possess.

8 Ham, Y. & Golparvar-Fard, M., 2012.

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In order to address these challenges the study proposes to use image-based 3D modelling

techniques in conjunction with thermal imagery to rapidly create an existing conditions thermal

model.

This study is interesting for two reasons. First, it use REM techniques to generate both

geometric as well as a thermal imagery which are then superimposed to create a compositethermal model. Secondly, the composite model is itself a thermal analytical model. This

means that no further analysis of the model is required to produce results that can be used for

design decision making.

In this way, the authors’ note, the process is rapid both in its approach to generating the

analytical model but also in the fact that it eliminates the need for a separate analysis

process.

Data capture for the study was performed in an office room of an existing instructional building

at Virginia Tech. The visual data was collected in the morning under natural daylightingconditions.

The digital and thermal images were captured using an E60 thermal camera from FLIR

Systems which has a built in digital camera.

The process began by capturing 429 unordered and uncalibrated digital and thermal images.

A streamlined image-based 3D reconstruction algorithm was then used to generate a dense

3D point cloud of the scene. The density of the thermal point cloud was 2,064,662 points

while the density of the building geometry cloud was 8,488,888 points.

In order to generate a superimposed model it was necessary to co-register the thermal and

digital point clouds. Essentially it was necessary to correlate pairs of digital and thermal

points. However, because thermal images use gradient color coding which smooths over

surface intensities no distinctive features could be found for correlation. A novel approach had

to be devised.

Ordinary point cloud laser scanners are calibrated using calibration rigs like a checkerboard

located within the scene. However, low resolution thermal cameras cannot detect such a rig.

Therefore a thermal calibration rig was created using 42 small LED lights.

Using this thermal rig together with known parameters like the camera location and orientation

the thermal image was calibrated and registered to the digital model and a superimposed

point cloud created.

Finally, an augmented reality model viewer was used to enable visualization of the digital and

thermal models. A pair of the resulting images is shown below in Fig. 1.

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Fig 1. REM using thermal images case study results.

Rapid Energy Modeling using Autodesk Workflows9

We will refer to the US DoD study as an example of REM using Autodesk Workflows. Thisstudy was undertaken by the Environmental Security Technology Certification Program

(ESTCP) of the US Department of Defense. Broadly speaking, the goal of the study was to

evaluate REM in order to determine if the workflow is capable of producing useful, rapid and

cost effective estimates of DoD buildings.

The study was conducted over a one year period using a population of 35 buildings and an

analyzed sample of 23 buildings. The buildings were spread across 8 locations and

represented 7 different building types. Below is a summary of the test facility locations and

types:

● 3 office buildings at the US Army Construction Engineering Research Laboratory in

Champaign, Illinois

● 1 office, 3 barracks and 1 gym at Fort Leonard Wood Army Base in Fort Leonard

Wood, Missouri

● 2 offices and 2 barracks at Joint Base Lewis McChord in Tacoma, Washington

● 2 offices and 1 barracks at the Naval Surface Warfare Centre in Panama City, Florida

● 4 offices at Peterson Air Force Base in Colorado Springs, Colorado

● 4 offices at Port Hueneme in Los Angeles, California

● 1 Barracks at Portsmouth Naval Shipyard in Kittery, Maine

● 1 Office, I Cafeteria, 1 School, 1 Fire Station and 1 Automotive Facility at SeymourJohnson Air Force Base in Goldsboro, North Carolina

● 1 Office, 1 Automotive Facility and 1 Cafeteria at Naval Weapons Station Earle in

Colts Neck, New Jersey and Middleton, New York

● 2 barracks and 1 drill hall at Naval Station Great Lakes in North Chicago, Illinois

9 Rupnow, J. & Sullivan, J., 2013

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The study was done in a series of test phases namely Reality Capture phase, Modelling

phase, Analysis phase, Technology Transfer phase and the Reporting phase.

In the Reality Capture phase test sites and buildings were identified and building background

information was gathered using an installation energy questionnaire. In the Modeling phase,

Building Information Models (BIMs) were created. First, conceptual energy models werecreated in Autodesk Formit and Autodesk Vasari and then energy models were generated

from the conceptual models and run using Autodesk’s Green Building Studio web service.

During the Analysis phase modeled result data was compared to actual metered utility data.

The time and cost of the REM process was also compared to traditional energy audits. In the

Technology Transfer phase workshops, webinars and curriculum development took place and

in the Reporting phase the final report was developed.

The technology used for the study was mainly Autodesk software. Autodesk Formit was used

for on site modeling, Autodesk Vasari was used for modeling and analysis, Autodesk Revitwas used for additional model refinements and additional analysis tasks and Autodesk Green

Building Studio was the web based analysis engine and interface that did the actual

calculation and reporting.

The results of the study were both quantitative as well as qualitative.

Quantitative Performance Results

● Correlation of REM with annual energy electricity and fuel intensity

Results were within 10% error on 7 out of 25 buildings for electricity. Two buildings

were within +/-10% for natural gas. Overall there was 81.88% average accuracy forelectricity and 58.2% average accuracy for natural gas. This likely because electricity

typically runs on a schedule based on building use while natural gas varies more

based on user preferences.

The DoD felt that the results for electricity and natural gas were within good to

reasonable prediction levels as defined in the literature.

● Correlation of REM with overall annual energy use intensity

14 out of 25 buildings were within +/- 25% compared to baseline historical utility data.

Average accuracy was 77.56%. DoD felt that the REM EUI predictions were within

good to reasonable levels defined in the literature.

● Variance in monthly consumption

Results were within 15% of the target for 3 buildings using billing history and cost as

metrics. An additional 2 buildings were within 20% of the target. It was not realistic to

expect initial models to be within a 15% variance target since this is a target that

calibrated models aspire to.

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● Testing the REM process for design alternatives to model potential energy savings

Energy Conservation Measures explored energy saving strategies for 5 buildings.

Savings greater than 30% were achieved in 3 out of the 5 buildings. The 2 buildings

that did not get to the 30% target had already undergone energy retrofits.

Qualitative Performance Results:

● Ease of learning technology and expertise required

Training completed at the time of publication indicated that DoD participants could

learn the REM workflow and begin creating and analyzing models in less than one

day. This was way below the target of 6 days.

● User satisfaction with REM technology

Participants indicated a high level of satisfaction with workflow as measured by survey

results.

● Ease of use creating REM models

Preliminary results indicate that energy models can be completed in less than 3 hours

after the process is learned which is way superior to the 2 days per building target.

● Ability to scale the process across the DoD

3 individuals had been trained at the time of reporting compared to a target of 5

individual within the first year.

COMPARISON OF THE APPROACHES

In this section we provide a comparison of the approaches discussed above. We willcompare these approaches based on cost, training time, time to perform analysis as well as

accuracy.

Cost Comparison

According to the DoD study a Level 2 Energy Audit of the 23 buildings they studied could cost

$179,673.00 at an audit cost of $0.12/ft 2 . This is $7811.87 per building.10

According to the DoD study an REM analysis of the 23 buildings studied could cost $6,900.00

or approximately $0.005/ft 2 . This is $300.00 per building.

According to industry commentators a cost of $0.2/ft 2 is a reasonable cost for traditional11

energy modeling. Since the average size of the DoD buildings can be calculated as 60,000 ft 2

the cost of energy modeling using a traditional approach would be $12,000.00 per building.

10 Rupnow, J. & Sullivan, J., 2013, p80.11 For example see http://energy-models.com/leed-and-energy-modeling retrieved in December 2014.

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Fig 2. Cost per Building in dollars comparison.

Time to Train

According to the Canadian Institute of Energy Training (CIET) website a Certified Energy

Auditor course takes 3 days (24hrs) to complete.12

According to the DoD study REM workflow operator training takes 1 day (8hrs) .13

According to the IES VE website basic training on the product takes 3 days (24hrs) .14

Fig 3. Training Time in Hrs comparison.

12 See http://cietcanada.com/events/certified-energy-auditor-cea/ retrieved in December 2014.13 Rupnow, J. & Sullivan, J., 2013, p87.14 See http://www.iesve.com/training/events retrieved in December 2014.

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Time to Perform Analysis

According to the DoD study a Level 2 Energy Audit takes 3 - 10 days. Let us take the median

value of 6.5 days (52 hrs) .15

According to the DoD study an REM analysis takes an estimated 3 hrs to complete .16

According to industry commentators energy modeling takes at least 40 hrs on average.17

Fig 4. Time to Perform Analysis in Hrs comparison.

Accuracy of Analysis

According to the Ausgrid, an Australian electrical utility, Level 2 Energy Audits are typically

about 80% accurate .18

According to the DoD study an REM analysis is 77% accurate for Energy Use Intensity .19

According to Reeves et al. in their study, IES VE had an accuracy, on average, for overall

energy usage of (86.45 + 51.90)/2 = 69.18.20

15 Rupnow, J. & Sullivan, J., 2013, p80.16 Ibid.17 For example see http://energy-models.com/leed-and-energy-modeling retrieved in December 2014.18 See

http://businessservices.ausgrid.com.au/~/media/Microsites/BusinessServices/Files/Metering/Product%20and

%20services/Energy_Audits.pdf retrieved in December 2014.19 Rupnow, J. & Sullivan, J., 2013, p2.20 Reeves et al., 2012, pp 584 - 586.

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http://businessservices.ausgrid.com.au/~/media/Microsites/BusinessServices/Files/Metering/Product%20and%20services/Energy_Audits.pdfhttp://businessservices.ausgrid.com.au/~/media/Microsites/BusinessServices/Files/Metering/Product%20and%20services/Energy_Audits.pdfhttp://energy-models.com/leed-and-energy-modeling


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Fig 5. Accuracy Percent comparison.

COMPARATIVE RETURN ON INVESTMENT OF REM

We end with a return on investment (ROI) analysis that compares the ROI on an REM

workflow to the ROI on a more traditional energy analysis software workflow. As we have

mentioned, part of the deterrence to BPA is the perceived cost and difficulty of performing

such analyses. If this can be demonstrably reduced then there will be a higher rate of

participation among designers. Here we will use ROI as an objective measure of cost and

ease of use.

In order to compare the ROI of using an REM workflow to the ROI of using a non-REM

workflow for BPA we will make a few assumptions. First we will use an ROI calculation

method due to Autodesk in their white paper BIM’s Return on Investment. According to this21

the ROI on design software investment can be computed as:

(B - (B / (1 + E)) x (12 - C) / A + (B x C x D)

where:

A = cost of hardware and software

B

= monthly labor costs

C

= training time

D = productivity lost during training

E

= productivity gain after training

21 See Autodesk, 2007.

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We will compare an REM workflow using Autodesk Vasari to a non-REM workflow using

Integrated Environmental Solutions, Virtual Environment (IES VE).

Training time will refer to the time in months that it takes to complete formal training. We will

assume that the productivity lost during training stays in the same ratio as the training time.

This means if it takes three times as long to train on software A as B then the productivity lostfor A is three times as much as the productivity lost for B.

We will use an Effective Productivity Gain computed as :

Effective Productivity Gain = Productivity Gain x Accuracy Factor x Features Factor

This will penalise software that produces inaccurate results or software that lacks features

that are important for comprehensive BPA studies. The Accuracy Factor will be determined

from Vidmar’s paper where IES VE and Vasari are compared. Both pieces of software score22

8/10 on this measure. The Features Factor

will be determined from the following featureswhich we will consider as a minimal requirement for BPA tools:

● solar analysis

● shadow analysis

● thermal analysis

● daylight analysis

● wind analysis

Features IES VE VASARI

Calculation speed/completion time 4 10

Visual environment & feedback 8 10

User interface simplicity &

intuitiveness

5 8

Ease of use & learning curve 5 8

Software documentation 6 10

Community & technical support 10 7

Total out of 60 38 53

Figure 6. Comparative productivity gain for IES VE against Vasari

22 Vidmar, 2013, pp 7-8.

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These features will be given equal weight in our analysis. Accordingly, IES has the full

complement of features and scores 1.0 while Vasari lacks daylight analysis features and

scores 0.8.

Productivity gain after training will be computed based on Vidmar’s analysis (see Figure 6)

.23

We will consider productivity to be a function of the following factors:

● calculation speed

● visual environment & feedback

● UI simplicity and intuitiveness

● ease of use and learning curve

● software documentation

● community and technical support

We will take the Productivity Gain to be the aggregate points awarded for these factors

against the total number of points possible.

According to both their websites Autodesk Vasari and IES VE both require at least dual core

CPU with 4GB RAM. The retail price for such a workstation at hardware resellers is about

$500.00. Also, according to the IES website, basic training on IES VE takes three days while24

the US DoD reported that Vasari training could be taken in one day . Monthly labor costs will25

be taken uniformly at $16,000.00.

Accordingly first year ROI is as follows:

Vasari: (16000 - (16000 / (1 + 0.57)) x (12 - 0.05) / 1100 + (16000 x 0.05 x 0.05) = 60.89% IES VE: (16000 - (16000 / (1 + 0.51)) x (12 - 0.15) / 5700 + (16000 x 0.15 x 0.15) = 10.57%

CONCLUSION

From the comparison of methods we saw that REM analysis performs better than either

traditional software processes or Level 2 energy audits for cost, training time and time to

perform analysis. We also saw that while REM analysis was not as accurate as Level 2

energy audits for overall energy use, it was more accurate than traditional software methods.

23 Vidmar. 2013.24 See http://www.iesve.com/training/events .25 Rupnow and Sullivan, 2013, p13.

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IES VE VASARI

Cost of Hardware 500.00 500.00

Cost of Software* 5200.00 600.00

Monthly Labor Cost 16000.00 16000.00

Training Time (months) 0.15 0.05

Productivity Lost During Training (%) 0.15 0.05

Accuracy Factor 0.80 0.80

Feature Factor 1.00 0.80

Productivity Gain After Training**(%) 0.51 0.57

First Year ROI 10.57% 60.89%

*Vasari is free but attracts a cost for

cloud based analysis services

**Effective Productivity Gain

Figure 7. Comparative first year ROI for IES VE against Vasari

From the ROI analysis above we see that an REM tool has as much as a six times advantagein ROI over traditional software approaches. This is because of the immense rapidity with

which the software can be learned and deployed on projects. With such a return on

investment it is clearly a path to BPA that designers should consider.

We therefore believe that REM has an important role to play in helping designers perform

BPA studies on existing buildings and thereby achieving high performance solutions.

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REFERENCES

Autodesk. (2007). BIM’s Return on Investment. Retrieved November 2014 from

http://images.autodesk.com/emea_s_main/files/gb_revit_bim_roi_jan07.pdf .

Autodesk. (2009). Rapid Energy Modeling for Existing Buildings. Retrieved November 2014 from

http://images.autodesk.com/adsk/files/rem_executive_summary.pdf .

Autodesk. (2011). Streamlining Energy Analysis of Existing Buildings with Rapid Energy Modeling .

Retrieved November 2014 from http://images.autodesk.com/adsk/files/rem_white_paper_2011.pdf .

Ham, Y. & Golparvar-Fard, M. (2012). Rapid 3D Energy Performance Modeling of Existing Buildings

using Thermal and Digital Imagery. Advanced Engineering Informatics, 27(3), pp 395 - 409.

Loftness, V. (2014). Areas of Practice . Course Notes, Carnegie Mellon University, Autumn 2014.

Natural Resources Canada. (2011). Energy Saving Toolbox: An Energy Audit Manual and Tool .

Ottawa, Ontario. St Joseph Communication.

Reeves, T., Olbina, S., Issa, R. (2012). Proceedings of the 2012 Winter Simulation Conference:

Validation of Building Energy Modeling Tools: Ecotect, Green Building Studio and IES VE (pp

582-593). Presented at the IEEE 2012 Winter Simulation Conference.

Rupnow, J. & Sullivan, J. (2013). Rapid Energy Modeling Workflow Demonstration. Alexandria, VA.

ESTCP Program Office.

Stumpf, A., Kim. H., Jenicek, E. (2011). Early Design Energy Analysis Using Building

Information Modeling Technology. Champaign, IL. Construction Engineering Research Laboratory.

Vidmar, J. (2013). Evaluation of simulation tools for assessment of urban form based on physical performance. Retrieved November 2014 from

https://www.academia.edu/4820747/Evaluation_of_simulation_tools_for_assessment_of_urban_for

m_based_on_physical_performance .

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https://www.academia.edu/4820747/Evaluation_of_simulation_tools_for_assessment_of_urban_form_based_on_physical_performancehttps://www.academia.edu/4820747/Evaluation_of_simulation_tools_for_assessment_of_urban_form_based_on_physical_performancehttp://images.autodesk.com/adsk/files/rem_white_paper_2011.pdfhttp://images.autodesk.com/adsk/files/rem_executive_summary.pdfhttp://images.autodesk.com/emea_s_main/files/gb_revit_bim_roi_jan07.pdf

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HOW RAPID ENERGY MODELING CAN HELP DESIGNERS IMPROVE BUILDING PERFORMANCE IN EXISTING BUILDINGS