how do early phase performance modelling results … · early phase building performance modelling...

AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.

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HOW DO EARLY PHASE PERFORMANCE MODELLING RESULTS

COMPARE TO DETAILED METHODS?

PRIYA GANDHI, BSc(Eng), MSc(Arch) ESD Engineer

Umow Lai

Level 4, 10 Yarra Street

South Yarra, Vic 3141

[email protected]

MATT SYKES, MEng(Hons) ESD Technical Lead ANZ, Jacobs

Level 11, 452 Flinders St, Melbourne 3000

[email protected]

ABOUT THE AUTHORS

Priya Gandhi is a building performance engineer with experience in the United States and Australia

and a background in mechanical engineering, architecture, and building science. She conducted her

graduate research at the Center for the Built Environment at the University of California – Berkeley,

where she researched office plug loads and occupant behaviour in low energy buildings, and mixed

mode modelling tools. Priya is focused on energy simulation and daylighting design, and has

experience with a range of whole building modelling and specialised analysis tools. She is

currently an ESD Engineer with Jacobs in Melbourne.

Matt Sykes is a sustainability professional with over 10 years’ industry experience in the design and

analysis of high performance buildings and precincts. He was the lead mechanical engineer for

“One Angel Square” in Manchester – a building that has received numerous sustainability accolades

including the BREEAM Office Building of the Year award in 2013. Since moving to Australia in

2010 Matt has been involved with numerous sustainable building and energy efficiency projects,

particularly in the tertiary education sector. Matt is currently the technical lead of the Jacobs ANZ

ESD team, which operates out of Melbourne.

ABSTRACT

Early phase building performance modelling tools offer the possibility of quick and accessible

performance assessments to guide building form and façade design during the critical early stages

of design when there is insufficient time or information to conduct a detailed whole building energy

modelling exercise. On a recent high-performance university laboratory project, early phase energy

modelling was utilised for façade optimisation during concept design. We were able to provide

timely feedback to the design team by turning around results in less than a quarter of the time than

would have been possible using conventional modelling software. This was the first large-scale

building the team had analysed using a separate early phase modelling tool; therefore, it was

decided to replicate the early phase assessment in another modelling tool for additional comparison.

The key lessons learned from this case study include assessing how to best interpret and utilise the

results of early phase modelling, and understanding the magnitude of impact from key design

changes between early and detailed modelling tools.


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1. INTRODUCTION

According to the World Green Building Council (WorldGBC), all buildings will be required to

operate at net zero carbon by 2050 to achieve the targets set out in the 2015 Paris Agreement on

Climate Change [1]. Australia is signatory to the Paris Agreement, and along with more than 190

other nations, has agreed that global temperatures cannot be allowed to rise more than 1.5°C above

pre-industrial levels [2]. The buildings industry has a key role to play in achieving this goal as the

sector is responsible for approximately 23% of Australia’s greenhouse gas (GHG) emissions and

30% of global GHG emissions [3,4].

The Green Building Council of Australia (GBCA) has joined the WorldGBC and green building

councils from around the world in the Advancing Net Zero project to deliver on the pledges made in

Paris [5]. As part of this, the GBCA has committed to developing a net zero rating tool to be in

place by 2030 [6].

Predictive energy modelling is often a requirement for benchmarking and rating tools, such as the

GBCA’s Green Star suite, and for performance-based ratings such as the National Australian

Building Environment Rating System (NABERS) [7,8]. Certifications under Green Star are on the

rise, and a NABERS rating is required for commercial office spaces over 1000m2 sold or leased as

part of the Commercial Building Disclosure (CBD) regulatory program [9,10]. The CBD program

aims to improve the energy efficiency of large office buildings, and to promote a buildings market

which rewards energy efficiency in order to mitigate Australia’s impact on climate change [10].

Building energy performance modelling is key in assisting building owners of all scales to achieve

their energy efficiency goals, from achieving targeted NABERS and Green Star ratings, to realising

net zero energy certification. Reducing building loads through passive design is the first step to

lowering overall building energy consumption [11], and predictive building simulation can help

ensure projects are on track to meet their performance targets.

Being able to model and provide feedback on a project during early design is critical to impact key

decisions that may not be easily changed later, such as building form, massing, orientation, and

external solar shading schemes. These have significant potential impacts on overall energy use and

are therefore critical to assess early on. However, modelling during the early phases of design can

often be problematic, as typical software used for benchmarking or code compliance modelling

requires a high level of detailed input which is often not available during the conceptual design

stage. Additionally, there is often limited to no budget available at this phase of the project to

engage in detailed building simulation.

During early phases of the design the team may only need to know comparative information, e.g.

which glazing product will provide better thermal comfort, or which shading system will reduce

cooling costs, rather than absolute predictive energy results. However, it is still not clear how the

results of early phase tools compare to the results generated by more detailed simulation packages,

such as those used for Green Star benchmarking or demonstrating compliance with energy codes

such as Verification Method JV3 of the National Construction Code (NCC) Building Code of

Australia (BCA).

1.1 Focus and intent

The primary focus of this paper is to compare the use of three simulation packages: IES Virtual

Environment (IES VE), Sefaira, and Honeybee. While the authors have over 10 years’ experience


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with IES VE, this was their first use of Sefaira and Honeybee for energy modelling. This paper

documents the process and challenges faced by using these tools for the first time, as well as the

comparative results of the parametric assessment conducted in each simulation package.

The scope of the energy modelling assessment was limited to analysing the impact of façade

parameters (insulation, glazing, shading, and air-tightness) on the heating and cooling loads of a

university laboratory project.

The purpose of this paper is to firstly understand how early phase and detailed modelling can

complement each other. This will be done by comparing the results of multiple early phase

modelling tools to understand if, how, and why the results are similar or different. The second

purpose is to understand how and when to best use different early phase modelling tools. The

intended outcome of this investigation is to improve the modelling methodology used on future

high-performance building projects.

Note that this paper is not intended to be a thorough comparison of simulation package capabilities

or an assessment of quality or accuracy, but rather a comparison of how each simulation package

was used, what information it provided, how results from each simulation compared to each other,

and experiences while undertaking the modelling.

2. METHODOLOGY

The concept phase building design was replicated using three different simulation packages: IES

VE, Sefaira, and Honeybee. The relative impact of key envelope parameters was assessed on the

heating and cooling loads as a percentage change against the baseline scenario.

2.1 Project description

The project modelled for this paper is a state-of-the-art teaching laboratory intended for location at

Monash University in Clayton, Victoria. The 10,000m2 building has five levels and contains dry

and wet labs, classrooms, informal learning/study spaces, offices, and plant rooms. The project is

targeting a 5 Star Green Star Design & As Built v1.1 rating and a high performance façade with an

envelope air permeability of no more than 3m3/hr at 50 Pa, consistent with best practice guidelines

for offices and schools [12].

2.2 Simulation software packages

IES VE is a suite of building performance analysis applications. For dynamic thermal building

simulation IES VE uses its proprietary ‘Apache’ simulation engine [13]. Model geometry may be

imported into IES from Revit, SketchUp, Vectorworks, gbXML, IFC and .dxf. The building was

modelled in the IES modelling module – ModelIT.

Sefaira is a cloud-based software which enables designers to conduct quick early phase energy

assessments using the EnergyPlus simulation engine [14]. SketchUp was used to import the

building geometry into Sefaira’s website, and then the web-based interface was utilised to adjust

simulation inputs and calculate results.

Honeybee is an open source environmental plugin to Grasshopper, which is a graphical algorithm

editor integrated into the 3D modelling software Rhinoceros (Rhino). Honeybee connects


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Grasshopper to EnergyPlus, Radiance, Daysim, and Open Studio for building energy and daylight

modelling [15]. For this analysis the project geometry was built in Rhino, and Honeybee

components were used within the Grasshopper interface to build the simulation.

2.3 Simulation parameters

To assess the relative impact of various façade changes on the heating and cooling loads, the same

six scenarios were modelled across each software package. The baseline scenario was based on

deemed-to-satisfy insulation levels (per NCC BCA 2016) with clear double-glazed windows

throughout. Four subsequent scenarios improved upon the baseline, with a final combined model

which included all improvements.

Care was taken to ensure that the modelling inputs were uniform across all three tools. Table 1 lists

the inputs which remained constant across all models and software, while Table 2 lists the

modelling parameters which were varied in each simulated scenario.


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Parameter Model input

Form, orientation and adjacent buildings See Figure 1

Weather file Melbourne IWEC

Internal loads

- Occupancy density

- Occupancy gain

- Lighting gain

- Equipment gain

5 m² per person

75Wsensible / 55Wlatent per person

12 W/m²

30 W/m²

Internal loads profile

- Weekdays

- Weekends

See Table 3

Off

Thermal plant performance

- Gas boiler efficiency (heating)

- Air cooled chiller COP (cooling)

83%

3.4

Temperature control set points

- Heating

- Cooling

20°C

24°C

HVAC operating schedule

- Weekdays

- Weekends

7:00 – 19:00

Off

Outdoor air supply 11.25 L/s per person

Economy cycle (“free cooling”) On

Table 1. Modelling inputs: constants

Parameter Baseline scenario Improved scenario

Insulation

- External walls R-value

- Roof R-value

2.8 m2K/W

3.2 m2K/W

4.0 m2K/W

5.0 m2K/W

Glazing

- Total system U-value

- Total system SHGC

4.08 W/m2K

0.66

2.70 W/m2K

0.20

Solar shading None North: 1x1m horizontal (45°

cut-off)

East: 1x1m vertical (45° cut-

off)

South: none

West: 1x1m vertical (45° cut-

off)

See Figure 1

Envelope air permeability 0.5 ACH (on continuously) 0.1 ACH (on continuously)

Table 2. Modelling inputs: variables


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Start Time End Time Occupancy Lighting Equipment

0:00 1:00 0% 0% 0%

1:00 2:00 0% 0% 0%

2:00 3:00 0% 0% 0%

3:00 4:00 0% 0% 0%

4:00 5:00 0% 0% 0%

5:00 6:00 0% 0% 0%

6:00 7:00 0% 0% 0%

7:00 8:00 0% 0% 0%

8:00 9:00 50% 50% 50%

9:00 10:00 50% 50% 50%

10:00 11:00 100% 100% 100%

11:00 12:00 100% 100% 100%

12:00 13:00 100% 100% 100%

13:00 14:00 50% 50% 50%

14:00 15:00 100% 100% 100%

15:00 16:00 100% 100% 100%

16:00 17:00 100% 100% 100%

17:00 18:00 100% 100% 100%

18:00 19:00 20% 20% 20%

19:00 20:00 20% 20% 20%

20:00 21:00 20% 20% 20%

21:00 22:00 0% 0% 0%

22:00 23:00 0% 0% 0%

23:00 0:00 0% 0% 0%

Table 3. Modelling inputs: operational profiles

Figure 1. Geometry input for IES VE, Sefaira, and Honeybee, respectively

3. RESULTS

The modelled annual heating and cooling energy was extracted for each scenario within each

software package. The results are compiled in Table 4. For all scenarios, the absolute values from

the result files were extracted (kWh gas for heating, kWh electricity for cooling). GHG emissions

were calculated based on emission intensity factors of 1.32 kgCO2e/kWh for electricity and 0.199

kgCO2e/kWh for natural gas. These factors were sourced for Victoria from the Green Star Design &


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As Built Greenhouse Gas Emissions Calculator v1.2. For each improved scenario, the percent

deviation in heating and cooling energy is compared to the baseline scenario.

Table 4 also includes a graphical representation of the percent deviation in heating and cooling

loads achieved with the improved scenarios 1-5. Note that a positive change indicates that the load

increased for that scenario, while a negative change indicates energy savings.


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Table 4. Modelling results


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4. DISCUSSION

Although best efforts were made to input identical modelling parameters, the variation in results

was surprising, with each software package exhibiting different sensitivity levels for each scenario.

4.1 Comparative trends

4.1.1 Baseline scenario comparison

Absolute energy results are not the focus of this assessment as early phase modelling is best used

for a comparative assessment of design options. However, the authors wanted to know how each

software package performed with simplified model geometry and best attempts to input identical

modelling parameters.

The absolute heating and cooling energy results from Honeybee and IES VE were very similar, with

only a 0.5% variation in annual heating and 5% variation in annual cooling energy. Sefaira results

were considerably higher than Honeybee and IES VE energy models, with annual heating and

cooling energy approximately 40% higher.

4.1.2 Heating load trends

Improving insulation values in the IES VE model reduced annual heating energy by 4.8%. This is

nearly double the decrease demonstrated by Sefaira (2.8%), and more than triple the decrease

demonstrated by Honeybee at (1.5%).

Improving glazing performance in Honeybee resulted in a 7.4% increase in annual heating energy.

In IES VE the increase was 4.9% and only 1.7% in Sefaira. It was expected that external solar

shading would increase annual heating energy due to the reduction in solar heat gains, however the

Sefaira model showed a small reduction (0.9%) instead. Honeybee and IES VE results

demonstrated a 10% and 12% increase, respectively. This shows a trend that the Sefaira model is

not recognising beneficial solar heat gains to the same extent as Honeybee and IES VE.

Improving the air-tightness of the building envelope in Honeybee resulted in a 28% reduction in

annual heating energy. This is approximately half of the annual heating energy reduction when

compared to Sefaira and IES VE, which were 59% and 56% respectively.

4.1.3 Cooling load trends

Improving insulation had an almost negligible impact on modelled annual cooling energy across all

three software packages.

Improving the glazing performance in Honeybee reduced annual cooling energy by 12% when

compared to the baseline; however, Sefaira and IES VE demonstrated a 22% and 25% reduction

respectively – approximately double the reduction of the Honeybee model.

Solar shading had a similar impact on annual cooling energy across all modelling tools, with

reductions of 8.6% for IES VE, 7.2% for Honeybee and 6.9% for Sefaira.

Improving air-tightness of the building in Sefaira resulted in a 17% increase in annual cooling

energy, whereas Honeybee and IES VE only demonstrated increases of 6% and 5%, respectively.


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4.2 Overall observations and interrelationships

Table 5 lists each scenario tested and which software resulted in the highest impact on heating and

cooling energy. As the results demonstrate, there was no general pattern; each software package

appeared to have different levels of sensitivity across the various scenarios.

Sefaira’s heating energy was least impacted by reducing beneficial solar heat gains achieved by

applying external shading and reduced glazing solar heat gain coefficient. The evidence that cooling

was reduced significantly with these measures demonstrates that the model was recognising the

glazing and shading performance changes.

The increase in heating energy due to improved envelope air-tightness in Honeybee was half of that

in Sefaira and IES VE. This is not consistent with the response in cooling, where Sefaira

demonstrated approximately three times the increase in cooling energy compared to Honeybee and

IES VE.

Scenario Highest impact on

heating energy

Highest impact on

cooling energy

Improved insulation IES VE Approx. equal

Improved glazing Honeybee Sefaira / Honeybee

Solar shading Honeybee / IES VE Approx. equal

Improved air-tightness Sefaira / IES VE Sefaira

Table 5. Comparison of highest load impact by scenario

4.3 Software discussion

Some key lessons learned through the process of using Sefaira and Honeybee for the first time have

been identified.

In both Sefaira and Honeybee problems were encountered with the initial geometry input process.

Importing 3DS files into SketchUp caused issues with multiple coplanar walls and roofs, even

though Sefaira’s import recommendations were followed. As the first round of results provided

heating and cooling energy much higher than IES VE and Honeybee, and no warnings or alerts

identified a problem, Sefaira was contacted for assistance. In Honeybee, initially an attempt to

import a gbXML file from IES VE was made, however this caused numerous surface geometry

issues down the line. Eventually it was decided to redraw the geometry within Rhino itself, which

resolved these warnings and errors.

In Sefaira the heating results generated were very high unless the supply air temperature was set to

‘minimise heating energy’. The software defaults to ‘fixed supply air temperature’, therefore this is

an important setting to change in most models. Also, unlike IES VE and Honeybee, Sefaira does not

model outside air quantities based on occupancy schedules percentages (i.e. as demand-controlled

ventilation), but rather as either on or off. This is a likely explanation for the 40% higher annual

heating and cooling energy in the Sefaira model.

For Honeybee there was an abundance of learning resources available to the user, including a

welcoming online community willing to answer queries and assist with any modelling issues. For

the few queries posted to the Ladybug forums, replies with answers were typically posted less than


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half a day later. Chris Mackey’s excellent Honeybee Energy Modelling videos on youtube.com

were also a helpful resource throughout the modelling process.

Another key takeaway is that to really benefit from the Honeybee workflow, a much deeper

understanding of Rhino and Grasshopper is necessary. Several of the initial modelling errors and

issues encountered were related to a lack of experience with the Rhino modelling interface (which

is why the geometry input was initially a challenge), and the Grasshopper methodology.

Lastly, the Honeybee workflow makes many assumptions (internal gains, space profiles) which is a

potential time saver during the modelling process, however the default assumptions are based

primarily on ASHRAE sources, which are less useful for projects designed to meet other standards

and codes. It was not complicated to change these defaults, but it did take additional time to create

new inputs.

5. CONCLUSIONS

Through this assessment it was shown that IES VE, Sefaira, and Honeybee each demonstrated

different sensitivities to the impacts that key façade parameters had on heating and cooling loads.

For each scenario, at least two software packages were in reasonable agreement regarding scale of

energy penalties or savings, but it was not always the same two. For the baseline case, Honeybee

and IES VE resulted in closer absolute values, however the percent deviations for improving the

glazing performance and the building air-tightness were not consistent. Sefaira, on the other hand,

resulted in absolute heating and cooling loads approximately 40% higher than the other two

software packages, but the percent deviations for improving glazing and building air-tightness were

consistent with results from IES VE. While both IES VE and Sefaira showed similar energy saving

percentages due to increased building air tightness, Honeybee indicated a much smaller saving.

Further analysis is required to interrogate the Honeybee inputs and results to understand why the

benefit was so much lower.

Of the three tools, it was found that while Sefaira inputs were the least customisable, it was also the

quickest interface for setting up parametric runs. Honeybee was most challenging due to the

additional necessary knowledge of Rhino and Grasshopper, however its ability to be very detailed

and customised was a benefit.

5.1 Next steps

It was expected that the results from Sefaira and Honeybee would be more aligned because they

both use EnergyPlus as their simulation engine. One of the next steps is to fully interrogate the

input data files (IDF) from both tools as we become more advanced users, to understand why the

results were not always consistent. Planning is also underway to investigate the set-up of a custom

library of Australia-specific default load and operational schedules for Honeybee to streamline

future early phase modelling exercises.

ACKNOWLEDGEMENTS

We would like to thank Jacobs for supporting and encouraging this work and Monash University

for giving us permission to use their project. We would also like to thank James Charnley of

BuildingPoint Australia for technical advice on Sefaira and resolving compatibility issues, and the


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generous folks on the Ladybug discussion forums for providing helpful and timely advice to a

beginner Honeybee user.

REFERENCES

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