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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
MATT SYKES, MEng(Hons) ESD Technical Lead ANZ, Jacobs
Level 11, 452 Flinders St, Melbourne 3000
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.
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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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
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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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
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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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.
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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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 &
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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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.
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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Table 4. Modelling results
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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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.
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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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
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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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
AIRAH and IBPSA’s Australasian Building Simulation 2017 Conference, Melbourne, November 15-16.
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generous folks on the Ladybug discussion forums for providing helpful and timely advice to a
beginner Honeybee user.
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