BIM and Energy Simulations by viktor k

2011- Technology Innovation Grant BIM in Energy Simulations Viktor Kuslikis, M.Arch

Abstract ............................................................................ p.3 Part A. Background

Introduction.......................................................................... When to use?...................................................................... What is involved?................................................................ Thermal Zones ................................................................... Accuracy + Baseline Models ............................................. Day lighting + Energy ........................................................

Table of Contents

Part B. Software Interoperability

Translating Design Data .................................................... Software ............................................................................. Trouble Shooting ............................................................... Case Study .........................................................................

Part C. Example

p.4 p.5 p.5 p.7 p.7 p.9

p.10 p.11 p.11 p.13

Variables + Defaults ........................................................... Design 1 .............................................................................. Design 2 .............................................................................. Design 3 .............................................................................. Design 4 .............................................................................. Design 5 .............................................................................. Shading Paradox ................................................................ Final Design ........................................................................ Glare+Design Trade off ......................................................

p.14 p.16 p.17 p.18 p.19 p.20 p.21 p.22 p.23

Conclusion ............................................................ References ............................................................. Appendix.1 ............................................................ Appendix.2 ............................................................

p.24 p.25 p.26 p.28

Technology Innovation Grant. BIM in Energy Simulations.

Abstract

Utilizing BIM models in environmental simulations can help architects and engineers deliver a better building by supporting energy performance decisions at various stages of the design process. Clients are demanding buildings that consume less energy, and subsequent lower operating costs. Many public bodies have already mandated that all new constructions must meet target energy reductions or obtain certain certifications. Predicting the energy performance of a building is the result of a complex set of interrelationships that requires the input of hundreds of factors, many of which are unavailable at the early design stage. As the design evolves, the number of unknowns decreases. Hence, it is important that any simulation process is flexible to allow the input of new variables throughout the process. Utilizing the native building model ensures that the design information used in the simulation is accurate and reliable. Conversely, having someone update a separate model at every major design change creates unnecessary rework and can result in the use of outdated or erroneous information. CH2MHILL has long been a proprietor of BIM technology to support an integrated design process, and has dedicated significant resources implementing it companywide. Leveraging the building model to incorporate analysis software, whether to help evaluate design iterations or in applying for environmental certifications, is the logical evolution of the technology. The purpose of this technology innovation grant was to determine what steps are required to export building information from the native model to a third party analysis software, using standard company approved design delivery methods. Although, not withstanding minor mistranslations, it is currently possible to translate essential design information from the modeling software to the analysis platform via a Green Building XML (.gbXML) schema. What is now required is an in depth exploration and evaluation of the analysis programs presently available and implementation on a project.

Technology Innovation Grant. BIM in Energy Simulations.

Introduction

Part A. Background

Thermal models subject the building to virtual climate conditions to predict its real word performance. It can help designers evaluate design concepts in relation to energy performance in ways that were previously unavailable. A building’s orientation, massing, and envelope, combined with its mechanical systems, operating schedules, and internal loads are all not only critical in determining how much energy it consumes, but also in the comfort of the occupants. For example, external windows are a great source of natural light that can be very pleasant to occupants, but on certain days, too much sunlight can cause glare making the interior space unbearable. The same windows can be a great source of passive solar heat gain during the winter, but a nuisance in the summer. Understanding these interrelationships in detail can be difficult without simulation tools. In an increasingly competitive and value driven industry, simulation tools can give design consultants an innovative edge over their competitors, and help better meet the client’s needs. Large clients are increasingly showing interest in sustainability mandates. For example, the United States General Services Administration (GSA) has committed to meeting the ‘Architecture 2030 Challenge’, mandating that all new buildings are not only LEED approved, but are carbon neutral by the namesake date. To help meet this challenge, they are actively exploring how to utilize building models in thermal simulations to strengthen the reliability, consistency, and usability of energy predictions. Given that the GSA owns more than eight thousand buildings, costing more than 250 million dollars annually to operate, their interest in energy efficiency and thermal modeling dictates to the potential market for the technology (U.S. General Services Administration, 2009). Besides the GSA, the list of clients that are committing that all new buildings meet some sustainability mandate, whether LEED certification, Architecture 2030, or other, is growing everyday encompassing participants from both the private and public sectors. Energy simulations will undoubtedly play a greater role in helping clients and designers predict energy use in their buildings. Hence, it is safe for one to assume that they will also expect that the consultants they hire to be experts with the technology.

Technology Innovation Grant. BIM in Energy Simulations.

When to use? Simulation tools are most affective during the early stages of the project, which is when decisions influencing the energy performance of the building have the greatest impact. At this time, architects can use thermal and day lighting models to help evaluate different options and inform decisions relating to building geometry and passive design strategies. While engineers can conduct final energy predictions, and detailed analysis of building systems during the design development phase when more information is available. Hence, it is important that an affective energy simulation platform must be able to support both the conceptual and detailed design phases. Many consultants only utilize energy models during the detailed design phase for the sole purpose of applying for LEED certification, not realizing the full potential of the technology. It is only in the last few years that architects have begun to take interest in using simulation software as a design tool. Innovative architects are beginning to use simulation tools to balance site conditions, climate, thermal performance, and day lighting to inform the massing and fenestrations of the building. Much to the delight of the client, since the entire design of the building, from functional program to aesthetics, is to optimize energy performance and reduce operating costs. The future of architecture is no longer ‘form follows function’, but ‘form follows performance’.

1900, form follows function // 2010+, form follows performance What is involved? All energy analysis programs involve two parts, a simulation engine, and a graphic user interface (GUI). The simulation engine contains the actual thermodynamic equations that predict the building’s performance. Besides thermal performance, simulation engines can predict day lighting, ventilation, incident radiation for active solar systems, and even emergency egress. Conversely, the GUI provides an interface for the user to create, or import the building geometry, define the simulation parameters and finally present the results in a visually intuitive format. Both parts are equally important as the GUI dictates the ease of use, organization of output, and software interoperability, while the simulation engine is what determines the reliability of the results. Since the intention of developers is that engineers utilize simulation engines in all phases of the design process, it is important that they are open to external verification. It would impossible to convince any architect or engineer to take professional responsibility for design based on a computer simulation, without being able verify basis of the calculations. Technology Innovation Grant. BIM in Energy Simulations.

Another consideration is whether legislative bodies such as the United States Green Building Council (USBGC) or the United States Department of Energy (DOE) approve the program. The DOE has a list of approved calculation engines to predict energy saving for tax credits. Popular engines on their list include Energy Plus, DOE 2.2, eQuest, Trace700, and IES <VE> (U.S. Department of Energy, 2011). When deciding which simulation software package to use, it is important that the user carefully consider both the GUI and the simulation engine.

http://www1.eere.energy.gov/buildings/qualified_software.html (U.S. General Services Administration, 2009)

(U.S. General Services Administration, 2009)

Energy models can seem esoteric to new users because they require the input and understanding of a large number of variables. This requires a specialized user, who needs to be part architect, part mechanical engineer, and part technician, with the ratio dependant on the stage of the project. Before the program can run a successful simulation they must input the geometry of the building, layout or thermal zoning, building assemblies, operating schedules, climate data, internal loads (people, lighting, equipment), and preliminary HVAC components. Due to large number of required inputs, many simulation engines rely heavily on predefined defaults to simplify the process. After the user selects the intended occupancy, the program automatically attaches the operating schedules or usage profiles associated with it to determine the internal loads. These profiles not only outline basic information such as when the building is in use, the number of people in each room, and the comfort range, they also include the sensible and latent loads from equipment, electrical lighting, and people, along with the thermal mass properties associated with the furniture and finishes. Even though the profiles are default, in most programs it is possible to edit or create custom profiles. Besides occupancy profiles, most simulation platforms allow users to create or edit default construction assemblies. Finally, taking all the above variables into account the program performs hourly simulations to determine the loads on the building, and based on the defined systems, the energy needed to meet them.

Technology Innovation Grant. BIM in Energy Simulations.

Thermal Zones: Thermal zones are a key concept in energy modeling, and defining them is a vital step in translating design data between the modeling and simulation platforms. A zone is an area that the user controls with a single thermostat (Bobenhausen, 1994). It can be a single enclosed room, or one space can comprise of several zones as in the case of large open offices. The amount and location of thermal zones depends on the heating and cooling loads of the space in question. Often areas near windows, or along the perimeter of the building, have higher loads than near the core due to solar gains and heat loss. Conversely, many internal zones in large buildings often only require heat removal through the majority of the year because of gains from people and equipment. As an estimate for conceptual energy simulations, ASHRAE 90.1 standard dictates that perimeter zones be located 4.5m (15â&#x20AC;&#x2122;) from an exterior wall (ASHRAE, 2010). The amount and location of thermal zones are a large factor in determining how to distribute and deliver conditioned air throughout the building. Since the user has to create the thermal zones in the 3D model, and can only alter them in the simulation interface, it is best to consult with a mechanical engineer before the process.

Accuracy + Baseline Models The accuracy of energy modeling tools is difficult to verify because of the complicated interdependencies that exists in buildings, combined with real world variations in occupancy and operating schedules. This is why most experienced users emphasize that energy models should be primarily used as a tool to identify relative performance, not to predict actual energy use. Regardless of caveats from experts, energy simulations will inevitably foster expectations from clients (Turner & Frankel, 2008). Using a baseline or reference model to compare the results against is critical for the user to gauge if the results are reasonable. LEED requires projects to generate a baseline model using ASHRAE 90.1 standard, which sets the minimum requirements for energy efficient buildings. It is similar to a building code but for energy performance. This means that the user needs to run at least two simulations. First is with the minimum requirements as outlined in the code pertaining to construction assemblies, HVAC systems, thermal zones, and internal loads. Second is with the improved building envelope, and any passive measures such as shading devices.

Technology Innovation Grant. BIM in Energy Simulations.

The percentage by which the proposed design exceeds the energy performance of the baseline model determines how many credits it gets (Ibid.). Since LEED grants energy performance credits on the predicted performance of the building during the time of design, everything depends on the validity of the baseline model. Besides the possibility of being inaccurate, it is also difficult to create baseline models for buildings with high equipment loads, such as industrial, manufacturing, and research facilities. Even with a LEED rating, there is no guarantee that the building will perform as modeled. A study conducted by the New Buildings Institute (NBI) on behalf of the U.S. Green Building Council (USGBC) looked at the post occupancy energy use (POE) of 121 LEED certified buildings. Out of the group, the average estimated energy savings was 25% less than their predicted baselines, and the actual median measured savings was 28% (Turner & Frankel, 2008). In reality, while the majority of the buildings used considerably less energy, 20% of the buildings in the study used more energy than their predicted base line (ibid.). One of the problems with energy simulations to date is there is little data collected on the POE of buildings, both new and existing. Without a credible database, it is difficult to judge the creditability of simulation results in relation to similar buildings in size, type, occupancy, and location. While the DOE does have a publically accessible online database, 2003 Commercial Buildings Energy Consumption Survey (CBECS), it is limited in scope and they have not updated since because of limited resources. Therefore using simulation software for the sole purpose of applying for LEED or other environmental certification is limiting the potential of the technology as a powerful design tool.

http://buildingsdatabook.eren.doe.gov/CBECS.aspx

Technology Innovation Grant. BIM in Energy Simulations.

Day lighting + Energy Table.1- Day Lighting Space:

Day light Factor

Lux

Art Studios, Galleries

4-6%

1500

Factories, Laboratories

3-5%

750

Offices, classrooms

300

Lobbies, lounges

150

(Lechner, 2009)

In addition to predicting energy consumption, another popular function of simulation software is to help architects better understand how day lighting can affect interior environments. While the relationship between building performance and energy consumption is straightforward, the lower the better, lighting is less intuitive. Daylight is the natural light from an overcast sky, irrespective of building orientation, which does not cause shadows. While direct light is the undiffused light straight from the sun on day with no cloud cover. The standard unit to measure light is the â&#x20AC;&#x2DC;luxâ&#x20AC;&#x2122; or foot-candle, which refers to the number of lumens that fall on a unit of area. However, to quantify levels of natural light in buildings designers often use daylight factors, which refers to the ratio in illumination between the exterior and indoor space. Maximizing natural light is important when designing elevations, but too much glazing can result in uncomfortable spaces. Understanding what the optimal levels of natural light are, and how they relate to solar heat gains and convective losses is an important responsibility of the architect. Although, the recommended amount of daylight varies with the occupancy and the age of the occupants as outlined in Table.1, as a general rule of thumb it ranges from 1-6% or 100-1500 lux for most buildings. Examples of different daylight simulations Technology Innovation Grant. BIM in Energy Simulations.

Translating Design Data

Part B. Interoperability

The first step in any simulation program is setting up the building geometry. While most GUIâ&#x20AC;&#x2122;s have basic 3D CAD capabilities, they are very limited and difficult to work with. The better option is to import the CAD data from an external program. This process involves two parts, exporting the building geometry and design information to an exchange file, and importing the data into the analysis program to run the simulations. While there are many ways to transfer data between CAD and analysis platforms, the industry standard for energy models are Green Building XML schema (gbXML) files. For simpler calculations, such as calculating incident radiation for active solar systems, often file formats that only translate surfaces such as drawing exchange files (.dxf), SketchUp models (.skp), or drawing files (.dwg) are sufficient. What distinguishes gbXML files is that they allow users to transfer both geometric and space data from the native building model to the simulation software. However, since every program handles gbXML files differently, unfortunately, design information is often misinterpreted or lost during the process. Part of the difficulty in importing 3D CAD data into simulation programs is the difference in solid versus surface geometry. In most BIM applications, the user models each individual building element. For example if you are placing a wall in the model, you do not draw six separate planes, but place a single intelligent element that you can edit and organize. While, most simulation programs are surface based, for example walls are planes without any thickness. The problem is that during the translation, the software has to reduce all of the elements to a single surface. Which in the case of a single wall is relatively simple, but in a complex building with thousands of individual elements, it can confuse the program and make the process very unpredictable. This is why Googleâ&#x20AC;&#x2122;s Sketch Up, which is a surface based modeler, is becoming a popular means of translating design information into simulation platforms. Granted, the arbitrary nature of the process can be very frustrating, there are steps that the user can take to ensure that the exchange process translates the necessary design information successfully.

Technology Innovation Grant. BIM in Energy Simulations.

Software

Autodesk Ecotect

Bentley AECOsim

From the variety of available analysis tools, we used a trial version of Integrated Environment Solutions (IES) Virtual Environment (VE) software package, used in tandem with Beltley’s MicroStation V8i and Project Wise. The key benefit of IES VE was that it included a multitude of simulation tools besides thermal modeling, including day lighting, and shadow studies all in one relatively intuitive platform. It also proved to be the most effective in importing gbXML files created in MicroStation, when compared to Autodesk’s Ecotect or Bentley’s AECOsim, which both had problems translating the exterior envelope assemblies. Besides platform compatibility, the DOE and LEED Canada have approved the thermal calculation engine used by IES. Hence, baseline templates regarding occupancy and building assemblies based on the ASHRAE 90.1 standards are already preloaded into the software. Overall, the program was very effective and especially conducive to the design process. For preliminary design studies, the software offered numerous standard templates for quick thermal calculations. As the design progressed, it was easy to edit occupancy templates, building assemblies, internal loads, and HVAC systems as required. Finally, at the later design stages the program allows mechanical engineers to design detailed HVAC systems within the same interface. It is truly a simulation platform suited for all phases of the design process, from conceptual to detailed all within one interface.

Trouble Shooting Problems transferring the building data from the native CAD platform to the analysis software are common with the technology, and are usually the first difficulty one encounters when starting out. However, there are several helpful troubleshooting steps. The first common problem was that building information was getting lost during the translation process. This can be the result of the user having not modeled the building correctly.

Technology Innovation Grant. BIM in Energy Simulations.

When preparing a model for energy simulations, it is vital that all the building elements such as walls, slabs, and windows follow a parental hierarchy. What this means is that, a window or door, needs to be associated with the wall, or parent. If the user can move the window assembly, and the opening in the wall follows, then the two elements are associated. If they need to punch an opening in the wall before placing the window, then there is no association and the information will not translate successfully. This may involve remodeling portions of the native model to create the necessary associations, or in a worst-case scenario starting from scratch.

+ Ensure that the building is modeled correctly + Simplify the model as much as possible + Create small tolerances between thermal zones and building elements + Modify opening tolerance settings before exporting the file

Another common problem is misinterpretations during the translation process. The software only has limited intelligence, and often confuses elements and spaces. This is especially problematic with objects inside zones, such as landings and runs within stair enclosures. Not all the information in the native building model is necessary for the simulations. At the minimum, two key elements need to be successfully translated, the thermal zones, and the building envelope. Interior elements such as beams, columns, railings, stairs landings do not significantly affect energy simulations, and can create misinterpretations during the translation process. It is good practice to simplify the native model as much as possible before attempting to export it to the exchange file. At later stages in the design process, as the model becomes more populated, misinterpretations become more common. In the case study, we had difficulties with a stair enclosure, the landings and stair runs were exporting as individual spaces. By removing them, leaving only an empty shaft, the software was able to translate the space without errors. Creating a separate analysis container to export from helps the user simplify the translation process. Within this container, they can reference in or copy the various models and turn off, or delete needless elements as necessary.

Technology Innovation Grant. BIM in Energy Simulations.

In addition to lost and misinterpreted data, another common problem is that zones do not translate correctly. If can be caused by the user not having defined the spaces correctly. Often two adjacent spaces sharing a single wall will confuse the translation process and cause errors. Since adjacent spaces are unavoidable, one possible solution is for the user slightly offset the space boundaries. When defining the space, instead of drawing the shape flush to the wall, create a small (10mm) gap between the boundary and the enclosure. In some cases, this offset will prevent translation errors caused by the software misinterpreting adjacent spaces. It is also important to ensure that volumes are properly enclosed, small gaps between building elements such as walls and slabs can cause misinterpretations during the export process. When exporting to a gbXML file the user can specify the tolerance for openings, to ensure that doors or entrances translate correctly. While interoperability problems are common, they are improving with every software update. Even with the problems associated with gbXML files, they are a significant upgrade over their predecessors, Industry Foundation Classes (IFC) files. Given that several major software developers such as Autodesk and Bentley are supporting them as their standard exchange platform for energy simulations, one can expect future improvements in regards to interoperability.

Case Study: In 2010, in preparation for their 2013 fundamentals handbook, engineers at ASHRAE decided to see how they could utilize BIM models in energy simulation software for the renovation of their head quarters in Atlanta. Their experience was plagued with problems. Upon importing the IFC file into the simulation software there were 477 error messages. Even after they successfully cleaned up the model, the simulations did not recognize the exterior envelope in the calculations leading to unreliable results. Inevitably, their only solution was to hire an external consultant to rebuild the model from scratch. Besides having serious interoperability problems, the engineers also found that the simulation software made too many generalized default assumptions regarding internal loads and exterior envelope assemblies making it unsuitable for any detailed analysis. Their conclusion was that software vendors have overhyped using BIM for thermal modeling, and is currently only good for producing impressive graphics and very preliminary calculations. However, they did recognize the potential of the technology (Burning, 2011). While the paper did not mention what simulation software the engineers used, it is important to note that there are programs currently available that alleviate many of their concerns, especially with the ability to edit default load assumptions and improved platform interoperability.

Technology Innovation Grant. BIM in Energy Simulations.

Variables + Defaults

Part C. Example

Cast-in-place Concrete Structure

In addition to introducing the technology and outlining what is involved, the third part of the innovation grant is illustrating how architects can use the software to support conceptual design decisions. For the demonstration, we designed a hypothetical office building located in Toronto that includes a double height open office space, a conference space, an area for individual offices, and a stair enclosure. The goal was to illustrate how simulation software could help architects balance natural light and energy consumption in designing the south faรงade of the structure. With the hypothesis, that thermal and lighting simulations would help the user understand how changing the sizes and locations of fenestrations affect the performance and livability of the building, would lead to the most optimal configuration. Upon importing the building data into the simulation interface, the user must define the construction assemblies, location, occupation, and default HVAC system. From the default envelope options, we chose a standard masonry envelope, with insulated roof and floor slab as summarized in Table.2. Appendix one outlines the detailed descriptions of the assemblies including thermal properties. Next, the program prompted the user to select a HVAC system. Table.3 summarizes the list of default systems provided within the program, which included many sophisticated systems. Given that the climate file was for Toronto (Appendix.2), where heating loads dominated, we chose a central heating hot air system. The key advantage of the system was that besides being relatively easy to understand, it could provide heating, air conditioning, and ventilation all from one system. In the simulation program, the HVAC system was associated with the occupancy profiles of the spaces, which dictated the cooling and heating set points along with the internal loads, as illustrated in Tables 4 and 5.

Technology Innovation Grant. BIM in Energy Simulations.

Table.2- Default Construction Assemblies

Table.4- Internal Gains:

Exterior Wall

4” Face Brick, 2” Insulation, 8” Heavy Weight Concrete Block

Room

Interior Wall

8” Light Weight Concrete Block

Profile (all gains)

Gain-1 Sensible W max

Gain-2 Sensible W max

Gain-3 # people

Gain-3 (max) Sensible W

Gain-3 (max) Latent W

Exterior Window

Large Double-Glazed Windows w/ Aluminum Frame

Studio

8-6pm

1231.28

10.764

8.951

684.37

547.496

Stair

8-6pm

60.945

0.101

8.610

8.103

Roof

4” Heavy Weight Concrete w/ 4” Insulation

Conference

8-6pm

1733.951

5.382

53.352

3891.863

2513.495

Offices

8-6pm

1467.190

10.764

10.67

810.805

648.644

Ground Slab

Insulated Slab-on-grade Floor

Floor Slab

8” Light Weight Concrete Floor Deck

Table.3- Default IES <VE> HVAC Systems: - Active chilled beams - Central heating convectors - Central heating hot air - Central heating radiant floor - Central heating radiators - Constant volume dual duct - Constant volume terminal reheat - VAV single duct - VAV dual duct - Fan coil system - Induction system - Forced convection heater flue - Forced convection heater no flue - Multi zone cold deck - Radiant cooled ceilings - Radiant heater flue - Radiant heater multi burner - Split systems w/ mechanical ventilation - Split systems w/ mechanical ventilation and cooling - Split systems w/ natural ventilation - Water loop heat pump

Gain-1= Fluorescent lighting, Gain-2= Misc. Equipment, Gain-3= People

Table.5- Room Conditions: Room

Heating Point (°C)

Heating Cooling Cooling RH Area Profile Point Profile (max.%) m2 (°C)

Furniture Solar Mass ReflectFactor ance

Studio

21.1

24h

23.9

8-6pm

104.1 1.0

0.05

Stair

16.1

24h

23.9

8-6pm

9.4

1.0

0.05

Conference

21.1

24h

23.9

8-6pm

123.9 1.0

0.05

Offices

21.1

24h

23.9

8-6pm

123.9 1.0

0.05

With the necessary variables in place, it was now possible to run the simulations. It is important to note that with each model the variables outlined above remained constant, all other things being equal. Only the size and number of the windows fluctuated. This allowed us to compare different schemes, without having to worry excessively about the validity of the results. Based on ASHRAE general rules of thumb, the annual energy consumption for a typical office building is 30.5kWh/ft2 (ASHRAE, 2010). Since the energy usage of the test building, without any fenestrations was 259.3kWh/m2 (24.09 kWh/ft2), we know that we are least in the ballpark. With each design option, we varied the size and location of the windows in an attempt to find the best solution that balanced day lighting and energy performance.

Technology Innovation Grant. BIM in Energy Simulations.

Design One. Glazing= 0 m2

Artificial Illuminance

Total Yearly Energy Consumption = 93.7 MWh

Min.- 204.62 lux

Total Yearly Energy Consumption per Area = 259.3 kWh/m2

Ave.- 477.09 lux Max.- 585.69 lux

Technology Innovation Grant. BIM in Energy Simulations.

Total Carbon Dioxide Emissions = 37,440.2kgCO2 16

Design Two.

Glazing= 127 m2

Total Yearly Energy Consumption = 119.1 MWh

Total Yearly Energy Consumption per Area = 329.5 kWh/m2 Total Carbon Dioxide Emissions = 43,610.6 kgCO2 Technology Innovation Grant. BIM in Energy Simulations.

Design Three.

Glazing= 34.2 m2

Total Yearly Energy Consumption = 100.7 MWh

Total Yearly Energy Consumption per Area = 287.8 kWh/m2 Total Carbon Dioxide Emissions = 39,144.6 kgCO2 Technology Innovation Grant. BIM in Energy Simulations.

Design Four.

Glazing= 83.6 m2

Total Yearly Energy Consumption = 109.3 MWh

Total Yearly Energy Consumption per Area = 302.4 kWh/m2 Total Carbon Dioxide Emissions = 41,223.8 kgCO2 Technology Innovation Grant. BIM in Energy Simulations.

Design Five.

Glazing= 127 m2

Total Yearly Energy Consumption = 120.0 MWh

Technology Innovation Grant. BIM in Energy Simulations.

Total Yearly Energy Consumption per Area = 332.2 kWh/m2 Total Carbon Dioxide Emissions = 43,221.8 kgCO2 20

Shading Paradox

Total Yearly Energy Consumption = 98.4 MWh

Total Yearly Energy Consumption per Area = 272.4 kWh/m2

Exterior shading devices can reduce solar heat gain and subsequent cooling loads by blocking direct summer sunlight, but their effectiveness in reducing the building’s aggregate energy consumption depends on the climate. Design.5 utilizes exterior shading devices roughly 250mm deep and spaced 600mm apart, given that the solar altitude in Toronto on June 21 at around noon is 70° (0.6m/tan70=0.218m≈250mm). Surprisingly, the shading devices proved to be counterproductive causing the building to consume more energy than without them. The results did not make sense, as shading devices are a simple and effective way to reduce heat gain in buildings, which logically should result in better energy performance. Table.6 - Maximum Solar Gains (kW) Location

Peak date

Solar Gain w/o Shading

Solar Gain w/ Shading

Reduction

Toronto

June. 15

9.812

5.879

40%

July. 15

10.528

5.861

44%

Aug. 15

14.010

5.845

58%

Sept. 15

18.761

9.912

47%

June.15

7.834

5.789

26%

July.15

7.946

5.844

26.5%

Aug.15

11.061

6.031

45%

Sept.15

16.255

6.984

57%

Las Vegas

Total Yearly Energy Consumption = 95.0 MWh

Total Yearly Energy Consumption per Area = 263.0 kWh/m2

While the shading devices did reduce solar heat gain during the summer months in some cases by more than 50% as illustrated in Table.6, they hindered it during the winter as evident in the increased heating loads. Since buildings in Toronto only require cooling for a maximum of three months, reducing solar heat gain does little to affect annual energy consumption. Hence, shading devices would be more effective in regions where cooling loads dominate. Changing the location from Toronto to Las Vegas (Appendix.2), and rerunning the simulations, the devices become net reducers of annual energy consumption by 3.4MWh.

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Final Design

Total Yearly Energy Consumption = 109.3 MWh

Total Yearly Energy Consumption per Area = 302.4 kWh/m2

The final step of the exercise was to select the most optimal design option and further improve its performance. Out of the five options, Design.4 had the optimal balance of day lighting and energy performance. From the shading paradox, it is evident that in colder climates rather than taking measures to minimize the cooling load, the prudent choice is to reduce the heating loads. Since energy flows from high to low temperatures, the obvious step is to keep as much heat inside the building as possible by increasing the thermal resistance of the exterior building envelope. Changing the exterior windows from double (U= 2.9214 w/m2K) to triple glazing (U= 1.4554 w/m2K), and adding 5â&#x20AC;? inches of insulation to roof, increasing the thermal resistance (U=0.3216 W/m2K to U= 0.1641 W/m2K) were obvious choices. The net result was an improvement of 14.6 MWh/year, over the base line.

Total Yearly Energy Consumption = 94.7 MWh

Total Yearly Energy Consumption per Area = 262.2 kWh/m2

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Glare + Design Trade Offs Table.7 - Common Brightness Levels CD/m2

Example:

0.03 Candle Light 3 Well illuminated work space 30 Side walk on a cloudy day 300 Fresh snow on a sunny day 3000 500W Incandescent lamp (Lechner, 2009)

June 21- 1pm

Poor vision Normal indoor brightness Normal outdoor brightness Excessively bright Blinding glare

While the shading devices did not improve the buildingâ&#x20AC;&#x2122;s energy performance, they did significantly reduce glare or brightness. Lux and daylight percentages are suitable for determining the amount of natural light, they do not indicate brightness levels, which can be a major cause of discomfort for occupants. Using Radiance, an advanced day lighting function in IES, we were able to simulate glare levels. Given that comfortable brightness levels range from 30- 500CD/m2 as illustrated in Table.7, anything above 1000CD/m2 is uncomfortable. The results illustrated that during the summer months, the office would essentially be unbearable; imagine having to wear sunglasses inside the office after lunch. Shading devices helped reduce the glare to bearable levels during the summer, albeit while harming the aggregate energy performance of the building. The shading devices illustrate how simulation software can help designers understand and make tradeoffs, rather than obsessing over the bottom line watts per hour metric.

Technology Innovation Grant. BIM in Energy Simulations.

Conclusion

As the demand for energy efficient buildings increases, so will the need for simulation and thermal modeling tools. While, it is impossible to predict aesthetic trends, given the growing commitment to net zero buildings, performance driven design is the future of architecture. However, only using the technology, to predict energy usage near the end of the design phase to apply for certification, limits its potential as a powerful design tool. Using simulations to support decisions during all phases of the design process cannot only lead to better performing buildings, but better performing architects and engineers. Instantaneously seeing how ones respective design decisions can affect the energy performance of a building can be a tremendous learning resource to less experienced architects and engineers, who often cannot yet fully grasp the cascading affects of their actions. The potential benefits of the technology are undeniable, what is now required is an exploration into the various simulation programs currently available. Since the interoperability between the modeling and analysis interface is vital in successfully integrating the technology into the design process, choosing the correct simulation software platform is extremely important. It will also determine the extent that we can utilize the native building model, or whether we will have to create auxiliary models for simulations. Given CH2MHILLâ&#x20AC;&#x2122;s strong culture of innovation, and its investment in building information modeling in support of an integrated design process to date, not taking full advantage of the native model in energy simulations would be taking onestep forward, but two steps backwards. Technology Innovation Grant. BIM in Energy Simulations.

ASHRAE. (2010). ASHRAE Standard 90.1- Energy Standard for Buildings Except Low-Rise Residential Buildings . Atlanta: ASHRAE. ASHRAE. (2010). Principles of Heating, Ventilating and Air-Conditioning, 6th edition. (R. Howell, W. Coad, & H. Sauer, Eds.) Bobenhausen, W. (1994). Simplified Design of HVAC Systems. New York: John Wiley and Sons. Burning, S. (2011, April). BIM test at ASHRAE HQ. ASHRAE Journal , 28. Lechner, N. (2009). Heating, Cooling, Lighting; Sustainable Design Methods for Architects. Hoboken: Wiley.

References

Turner, C., & Frankel, M. (2008, March 4). Energy Performance of LEED for New Construction Buildings. Retrieved March 2011, from New Buildings Institute- Benchmarking and Feedback: http://www. newbuildings.org/sites/default/files/Energy_Performance_of_LEED-NC_Buildings-Final_3-4-08b.pdf U.S. Department of Energy. (2011, July 28). Building Technologies Program- Tax Credits. Retrieved October 1, 2011, from Energy Efficiency and Renewable Energy: http://www1.eere.energy.gov/buildings/qualified_ software.html U.S. General Services Administration. (2009, February). Series 5 - Energy Performance and Operations. Retrieved March 2011, from 3D-4D Building Information Modeling: http://www.gsa.gov/portal/ content/102283

Technology Innovation Grant. BIM in Energy Simulations.

Exterior Window Assembly (1): Description- Double-Glazed Windows (Reflective Coating) w/ Aluminum Frame Material

Thickness m Conductivity Resistance W/(m•K)

Transmittance

Refractive Index

Glass (solar)

0.0060

1.060

0.290

1.526

Cavity

0.0120

0.1587

Glass (float)

0.0060

1.060

0.290

1.526

U-Value (glass only)= 3.128 W/m K U-Value (assembly)= 3.106 W/m2K Visible Light Normal Transmittance= 0.760 2

Appendix.1

Exterior Window Assembly (2): Description- Low-E Triple Glazing SC-0.65 w/ Aluminum Frame Material

Thickness m Conductivity Resistance W/(m•K)

Transmittance

Refractive Index

Glass (solar)

0.0060

1.060

0.690

1.526

Cavity

0.0120

0.2994

Glass (float)

0.0060

1.060

0.780

1.526

Cavity

0.0120

0.2994

Glass (float)

0.0060

1.060

0.780

1.526

U-Value (glass only)= 1.306 W/m K U-Value (assembly)= 1.507 W/m2K Visible Light Normal Transmittance= 0.720 2

Exterior Wall Assembly: Description- 4” Face Brick, 2” Insulation, and 8” Heavy Weight Concrete Block Material

Thickness m Conductivity W/(m•K)

Density Specific Heat kg/m3 Capacity J/(kg•K)

Face Brick

0.1016

1.3310

2083.0

921.0

Insulation

0.0508

0.0430

32.0

837.0

Heavy Weight Concrete

0.2032

1.730

2243.0

837.0

Gypsum Board

0.0191

0.160

801.0

837.0

Total Thickness= 0.3746m Total R-Value= 1.5322 m2K/W U-Value= 0.5946 W/m2K

Technology Innovation Grant. BIM in Energy Simulations.

Interior Walls:

Roof Assembly (1):

Description- 8” Light Weight Concrete Block

Description- 4” Heavy Weight Concrete with 4” Insulation

Material

Thickness m Conductivity Density W/(m•K) kg/m3

Specific Heat Capacity J/(kg•K)

Material

Thickness m Conductivity Density W/(m•K) kg/m3

Specific Heat Capacity J/(kg•K)

Light Weight Concrete

0.2032

837.0

Stone

0.0124

1.8020

2243.0

837.0

Membrane

0.0095

0.1900

1121.0

1674.0

Insulation Board

0.1016

0.0430

91.0

837.0

Concrete

0.1016

1.7300

2243.0

837.0

Cavity

0.0127

Acoustic Tile

0.0191

0.0610

480.0

2142.0

0.380

609.0

Total Thickness= 0.2032m Total R-Value= 0.5347 m2K/W U-Value= 1.3341 W/m2K

Elevated Slab: Description- 8” Light Weight Concrete Floor Deck Material

Thickness m Conductivity Density W/(m•K) kg/m3

Specific Heat Capacity J/(kg•K)

Light Weight Concrete

0.2032

837.0

0.380

609.0

Total Thickness= 0.2572m Total R-Value= 2.9646 m2K/W U-Value= 0.3216 W/m2K

Roof Assembly (2):

Total Thickness= 0.2032m Total R-Value= 0.5347 m2K/W U-Value= 1.3341 W/m2K

Description- 6” Heavy Weight Concrete with 9” Insulation

Ground Floor Slab: Description- ASHRAE Insulated Slab-on-grade Floor Material

Thickness m

Conductivity Density W/(m•K) kg/m3

Specific Heat Capacity J/(kg•K)

Insulation

0.2335

0.120

550.0

1004.0

London Clay

0.750

1.410

1900.0

1000.0

Cast Concrete

0.100

1.400

1900.0

1000.0

Polyurethane Board

0.050

0.025

30.0

1400.0

Screed

0.100

0.410

1200.0

840.0

Synthetic Carpet

0.003

0.060

160.0

2500.0

Material

Thickness m Conductivity Density W/(m•K) kg/m3

Specific Heat Capacity J/(kg•K)

Stone

0.0127

1.8020

2243.0

837.0

Membrane

0.0095

0.1900

1121.0

1674.0

Insulation Board

0.2286

0.0430

91.0

837.0

Concrete

0.1524

1.7300

2243.0

837.0

Cavity

0.0127

Acoustic Tile

0.0191

0.0610

480.0

2142.0

Total Thickness= 0.4350m Total R-Value= 5.9475 m2K/W U-Value= 0.1642 W/m2K

Total Thickness= 1.2365m Total R-Value= 4.3112 m2K/W U-Value= 0.1986 W/m2K

Technology Innovation Grant. BIM in Energy Simulations.

Appendix.2 - Climate

Technology Innovation Grant. BIM in Energy Simulations.