BUILDING TUNING USING SIMULATION A PRACTICAL CASE STUDY€¦ · building was required to achieve a...

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

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BUILDING TUNING USING SIMULATION – A PRACTICAL CASE STUDY

MATTHEW WEBB, M.AIRAH

Sustainability Consultant

Umow Lai

L4, 10 Yarra Street

South Yarra Vic 3141

[email protected]

ABSTRACT

Building tuning is recognised within the construction industry as a worthwhile, but not mandatory,

process. Building tuning aims to ensure that a building (at least) reaches its intended energy and

water performance. The requirement that office buildings, in particular, achieve specific NABERS

Ratings has highlighted the usefulness of building simulation in combination with tuning to achieve

desired performance.

For successful performance outcomes, the design should cater for tuning from its inception.

Secondly, the targets for performance and comfort need to be explicitly defined. Targets are readily

obtained through Building Energy Modelling (BEM). Once the building commences operation, As

Built documentation and operational schedules are fed back to the BEM to align with selections of

materials, plant and equipment. Furthermore, specific targets for building services systems are

logically categorised according to the BMS submeter framework.

When building tuning proper commences, submeter data from the BMS is compared against BEM

benchmarks. Building performance is analysed and anomalies are identified. Diagnosing the cause

of anomalies and their rectification requires a coordinated effort from building owners, developers,

design team and contractors.

This paper presents two Melbourne case studies that examine building tuning, based on simulation,

and its effectiveness. The first case study is a one-year-old office building with a single tenant. The

second building is a multi-level office building with multiple government tenants. This building

reached practical completion in 2012 and represents a mature building in the context of building

tuning, however tuning continues to adapt to variations in building use and to improve energy and

water efficiency.

Building tuning lies at an intersection of design, simulation, performance and building management.

This paper illustrates how a building tuning program can be established and conducted, using

building simulation as a basis. The effectiveness of building tuning is demonstrated on two example

office buildings.

INTRODUCTION

To mitigate the worst effects of climate change, significant reductions in greenhouse gas emissions

will be required in the next 30 years to meet the goals of the Paris agreement to keep global

temperature rise well below 2°C [1]. Buildings account for approximately 19% of the world’s

greenhouse gas emissions [2] and therefore the property sector has a responsibility to find strategies


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to reduce the carbon intensity of building operations. Low energy design plays a large role in the

reduction of operational carbon emissions. However, it is also important for buildings to operate as

intended by design, and to investigate opportunities for further energy and water reductions far

beyond practical completion. Building tuning is one strategy that can assist in reducing building

energy consumption, and building tuning can be greatly assisted through building energy

simulation.

In 2009, Umow Lai was tasked with the design and development of mechanical and electrical

services and sustainability initiatives for the proposed Dandenong Government Services Offices

(GSO, see Figure 1). The subsequent design included a highly insulated building envelope with a

fritted, high performance double-glazed façade. An Underfloor Air Distribution (UFAD) was

selected for the mechanical services supplied by eight Air Handling Units (AHUs). Heating was

provided by two gas-fired condensing boilers and cooling provided by two air-cooled chillers. The

project was initially required to achieve 5 Star Green Star Office v3 Design and As Built Ratings,

however through economically feasible initiatives, the project achieved 6 Star Green Star Office

Design and As Built Ratings, as well as a 6 Star Green Star Interiors Rating. Operationally, the

building was required to achieve a 4.5 Star NABERS Base Building Energy Rating and 5.0 Star

NABERS Water Rating.

Figure 1. Dandenong GSO

Sustainability was a key focus in the design, construction and operation of the facility was strongly

supported by modelling and simulation. In particular, Building Energy Simulation (BES) was an

important factor in the design process, construction and through to commissioning and operation.

The BES for GSO continues to be an important comparative tool in the ongoing energy

management and building tuning.

1. PURPOSE OF BUILDING ENERGY SIMULATION

Initially, Building Energy Simulation (BES) was used as a tool to inform the architectural and

services design. A BES model was constructed in IDA ICE v4.0 [3] to test design initiatives and to

ensure that the building would be capable of achieving the desired performance ratings.


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Once GSO was operational, the BES was repurposed as a tool for the analysis of the building

energy and water performance and tuning of building services. The energy consumption estimates

(alongside water consumption modelling) would be compared to the actual operational

performance. The comparative analysis would reveal where the building was underperforming in

relation to design expectations. Remedial action could then be undertaken. In addition, the detailed

comparison would highlight opportunities for new initiatives to improve the building performance.

2. METHODOLOGY – BUILDING TUNING WITH BES

2.1 Building Energy Simulation Model

Building documentation was translated into an accurate BES in IDA ICE v4.0 (Figure 2). Annual

simulations were conducted using a variety of building performance initiatives during design

development and BES was a critical tool in the design of the building envelope, particularly the

façade double glazing, and the mechanical services, i.e. UFAD. Energy consumption from the

models was used to determine the probability of achieving NABERS performance targets and as

evidence for the Green Star submissions.

Figure 2. 3D perspectives of IDA ICE model

2.2 Interpretation from design to operation

Following practical completion and initial commissioning, the BES was updated with As Built data.

At this time, it was necessary to align the BES output with the submetering installed on site. In the

case of GSO, virtual metering categories had been specified for the Building Management System

(BMS) that would align with the major components of services energy consumption that were

relevant for base building energy consumption. This was a key consideration, as the use of BES for

building tuning could only be undertaken with accurate energy submetering.

Along with an estimate for the actual building performance, a specific set of benchmarks was

established that related building services energy consumption with an overall annual NABERS

Energy target [4]. This was necessary to determine the base level performance requirements for the

building, i.e. set maximum limits for each of the building services to achieve the desired NABERS

Rating. Maximum annual limits were set using NABERS Reverse calculators [5]. The percentage

contribution of each service category to the overall total energy consumption was then calculated

for the GSO building. The relevant service breakdown was unique to this building, and the

percentages resulted directly from the project-specific BES. From the percentage contribution (ps,a)


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and the NABERS maximum energy consumption (Nbenchmark), the annual energy consumption target

for each building service (Es,a) was calculated as follows:

Es,a = ps,aNbenchmark (1)

Annual consumption figures were further divided into monthly, daily and hourly energy budgets.

Simple arithmetic division was not appropriate in all cases, since several major building services

have a substantial dependence on external weather conditions. For practical purposes, given that

detailed site-specific weather was not available, energy consumption of weather-related services

was divided by month to give a monthly percentage consumption (ps,m). As with the percentage mix

of each service in the overall energy consumption, the monthly breakdown was specific for each

service in this unique building design and location. Therefore, the simulation results were critical in

the development of accurate NABERS targets. The monthly energy budget for each weather-

dependent service (Es,m) was then calculated as follows:

Es,m = ps,mEs,a = ps,m ps,aNbenchmark( ) (2)

With monthly energy budgets calculated, the next step was to further subdivide monthly targets into

daily and hourly targets. This was undertaken for all services (weather-dependent or not). The

critical factor in creating daily and hourly targets was building occupation. For GSO, the building

was expected to operate on all business days (Monday to Friday excepting public holidays). Daily

plant operation was between 7am and 7pm. The daily and hourly energy targets were thus

calculated using a conditional statement on the day of the week and the hour of the day.

3. SIMULATION RESULTS AND INTERPRETATION

3.1 Simulation Results

The annual simulation results from the GSO BES are summarised in Table 1. Multiple iterations

were completed during design development. The results presented here represent the As Built

outcome, such that all HVAC zoning, AHUs and plant was input as accurately as possible.


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Building service BES Energy

consumption

Electricity [all kWh]

Lighting Ground - L7 109,573

Lighting Basement 42,233

Chiller 91,097

AHU fans and Fan Coil Units 183,233

Pumps 19,949

General Exhaust (including car park

ventilation) 31,414

Lifts 36,676

Rainwater and stormwater pumps 21,286

Security, communications and other

general power 23,891

Annual Total [kWh] 559,352

Natural Gas [all MJ]

Boiler [MJ] 830,356

Domestic Hot Water [MJ] 154,310

Annual Total [MJ] 984,666

Table 1. Green Star As Built BES results

3.2 Tuning targets

Using the NABERS Reverses calculators, the building area (14,462.5m2) and operational hours (60

hours per week), the NABERS targets were calculated as shown in Table 2 below.

NABERS

Target

Gas

benchmark

[MJ]

Electricity

benchmark

[kWh]

4.5 Star 1,813,264 1,038,365

5.0 Star 1,344,417 769,880

5.5 Star 1,008,312 577,410

Table 2. Annual NABERS energy targets

The combination of BES outputs and the annual NABERS maximum targets were then combined to

calculate the monthly tuning targets for each of the major building services. BES NABERS targets

were also adjusted from raw Green Star outputs due to variations in the actual operational profiles, a

slightly different mix of energy coverage required for the base building NABERS Rating, and


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energy data obtained from other UFAD buildings. Furthermore, during building monitoring and

tuning, targets have been updated to reflect the current NABERS targets and the operational reality.

The current NABERS Energy 5 Star targets (as at June 2017) targets are shown in Table 3.

Building service 4.5 Star NABERS

Simulated Target

5.0 Star NABERS

Simulated Target

Electricity [all kWh]

Lighting Ground - L7 130,834 97,005

Lighting Basement 39,458 29,255

Chiller 249,208 184,771

AHU fans and Fan Coil Units 320,855 237,893

Pumps 88,261 65,440

General Exhaust (including car park

ventilation) 92,414 68,519

Lifts 34,266 25,406

Rainwater and stormwater pumps 62,302 46,193

Security, communications and other

general power 20,767 15,398

Annual total [kWh] 1,038,365 769,880

Natural Gas [all MJ]

Boiler [MJ] 1,529,101 1,133,729

Domestic Hot Water [MJ] 284,163 210,688

Annual Total [MJ] 1,813,264 1,344,417

Table 3. Monthly 5 Star NABERS simulated operational energy targets

3.3 Tuning dashboards

The basic annual service targets listed in Table 3 formed the foundation for a more detailed

development of targets at a daily resolution. Using the data analysis software, Tableau [5], a series

of building performance dashboards were created. These dashboards displayed, on a monthly basis,

the performance of a particular service during the month compared to a monthly total (as calculated

from equation 3), the previous year’s performance (where available), and against daily performance

targets. An example dashboard is shown in Figure 3, displaying mechanical services consumption

for the month of January 2017.


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Figure 3. Monthly dashboard display for mechanical services (January 2017)

4. OPERATIONS AND TUNING

4.1 Energy and water monitoring and NABERS Tracking

Combining high-resolution simulation results as benchmarks (for specific services) alongside

building performance data allows close monitoring of the building energy consumption for building

systems. With daily targets, it is possible to closely monitor all of the submetering and highlight

potential malfunctions and areas of opportunity for improvement. Facility managers are then able to

correct inefficient or malfunctioning plant before having a large impact on the buildings’ energy

performance.

The comparison of building performance data and simulated benchmarks on a monthly basis also

provides the facility manager ongoing feedback in relation to contractual obligations for NABERS

and other performance ratings. For example, the overall electricity consumption for GSO is plotted

against the monthly simulated 5 Star NABERS Energy benchmarks in Figure 4 (as at July 2017).

This gives information on how the current performance relates to both historical performance and

simulated benchmarks.


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Figure 4. Historical electricity performance of GSO against simulation benchmarks (in red), as at

July 2017

4.2 Building tuning, effects and monitoring

Simulated benchmarks in energy performance monitoring and building tuning can be further

leveraged to identify opportunities for building tuning and then assess the effectiveness of specific

tuning measures. Over the course of building operations at GSO, the comparison analysis of

building performance data against the simulation has led to building efficiency improvements.

Examples of these have been noted in Table 4.


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Service Observation Outcome

Lighting

Public holiday consumption

matches business day

consumption.

Controls adjusted to account for public

holidays (this has required constant resetting

during building lifetime).

Lighting Daily use high in common areas. Reset light timers to 15-minute intervals.

Halogen fittings replaced with LEDs.

Lifts Over-use of heavy duty goods lift. Staff advised to use passenger lifts only,

resulting in a decrease in lift energy.

AHU Fans and

heating Over-consumption during heating.

Supply air temperatures increased in UFAD

system. Fan energy decreased.

Car park

ventilation

Excessive consumption (up to 10

times expectations).

New controls regime written to more strictly

control fan operations based on carbon

monoxide control. Energy consumption now

5% of initial start-up consumption.

Table 4. Building tuning observations and corrective actions

4.3 AHU controls tuning

In 2015, based on the high consumption of AHU fans, an investigation was conducted on the

effectiveness of the UFAD system in creating a vertical thermal gradient through the occupied

zones (i.e. to ensure that the UFAD system was not creating a ‘fully mixed’ air system consistent

with a ceiling-based diffuser air distribution). The installation of additional sensors and logging of

temperatures, along with discussion with building controls technicians, highlighted additional

energy efficiency measures that could be implemented. Initially, stratification measurements on

Level 5 indicated that the temperature gradient between the floor and the ceiling could be increased.

Subsequent changes were made to increase (decrease) AHU off-coil temperatures and to increase

the range of operation of the Variable Control Dampers (VCDs).

The results of testing indicated that stratification had improved on the north. However, in the west

zones there was no measureable difference in stratification and further improvements could be

achieved on the west façade zones. Minor improvements were noted in AHU energy performance.

Further discussion with the facility manager and controls contractor identified a series of additional

tuning measures that could be implemented to improve efficiency. Eventually the decision was

made to implement static pressure reset control in the main AHUs [7]. This work was undertaken in

early December 2016. The controls contractor updated the hardware and programmed the

controlling regime whereby static pressure is lowered by management of VAV damper positions,

according to heating and cooling demand in each space. When comfort conditions are met and the

dampers are not 100% open, the BMS will allow dampers to open and static pressure to be lowered

until dampers are 100% open (if possible whilst maintaining space conditions). There is a check in

the BMS such that if zones call for more cooling (or heating), damper position and static pressure

will be reset to maximum settings to ensure sufficient airflow reaches zones and comfort conditions

are maintained.


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The effects of this change have been monitored through the past 6 months. AHU fan energy

consumption has decreased markedly (see Table 5). During warmer (cooling-dominated) weather,

fan energy consumption has been moderate throughout the day before increasing during the

afternoon in response to a higher cooling demand. In colder, heating-dominated weather, the fan

energy consumption has peaked during morning warm up before dropping considerably to about

10% of the peak value. Figures 5 and 6 below show the behaviour of the fans with static pressure

reset in January 2017 and May 2017. The equivalent month from the previous year is shown for

comparison.

Year Month

Previous year’s

consumption

[kWh]

Current year

consumption

[kWh]

Comparison to

previous year

2016 December 22,985 10,564 -54.0%

2017

January 19,867 11,269 -43.3%

February 22,100 8,324 -62.3%

March 21,690 11,339 -47.7%

April 20,158 5,667 -71.9%

May 24,279 11,534 -52.5%

June 24,065 11,853 -50.7%

July 24,399 11,657 -52.2%

Table 5. AHU fan energy comparison

Figure 5. AHU fan energy consumption – January 2016/2017

Benchmark Consumption below benchmark Consumption above benchmark


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Figure 6. AHU fan energy consumption – May 2016/2017

The overall effect of the static pressure reset control has been to significantly reduce the mechanical

services energy consumption below the 5 Star benchmarks (as seen, for example, in Figure 3). This

has allowed the building to improve its month-to-month NABERS Rating despite adversely hot

weather in March 2017 and an increased occupancy and use of the building.

5. LIMITATIONS AND FURTHER IMPROVEMENTS

There are several limitations in the GSO BES used to generate the NABERS benchmarks. While

accurate for the building and its services, the benchmarks were based on a standard weather file for

the closest available weather station with annual data (Moorabbin, Victoria). As a result,

comparisons of benchmarks on a daily or hourly scale did not exactly match the metered building

performance. Thus average daily benchmarks were developed on a monthly basis and represent the

typical consumption expected from the building at different times of the year. Further

improvements to the precision of the BES results can be obtained by re-simulating with current

weather data for the site. However, most buildings (GSO included) only have rudimentary ambient

air temperature sensors. The Bureau of Meteorology has only a limited number of detailed

monitoring stations and most of these do not have solar radiation measurements (which often need

to be satellite-derived).

Summarising, there are several challenges to presenting simulated data with current, accurate,

weather data from the relevant period of inspection and investigation. Ultimately, however, an

accurate weather data set can only enhance, and not fundamentally overhaul, the information

available on tuning dashboards. General trends and isolated incidents have been identified (and

continue to be identified) with daily targets mapped out for GSO.

Benchmark Consumption below benchmark Consumption above benchmark


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Tuning targets are not static. Building tuning is a continual, iterative process where targets are

adjusted to reflect the desired benchmark energy consumption and the actual operation of the

building. Initially, raw simulation output requires critical scrutiny and adjustments, if necessary, to

account for a more realistic representation of the services schedule and power output. In addition,

occupancy needs to be fine-tuned to reflect actual staff numbers and their working hours. This

should be updated progressively during tuning to ensure numbers and schedules remain accurate.

Furthermore, it has been noted that significant running measures have been carried out at GSO –

most recently the implementation of static pressure reset on the AHU controls. Based on the

performance results from the most recent six months, it would be possible to update the NABERS

benchmarks to reflect the system improvements. A set of adjusted benchmarks would provide the

building services team with a new pathway to maintain current levels of efficiency and push the

building towards improved NABERS performance. The goal is to achieve a 5.5 Star NABERS

Energy Rating.

Future building projects aspiring to leverage benefits from both BES and building tuning can also

interact with the models arising from Building Information Modelling (BIM). The process for BIM

integration in BES is still developing, and there are challenges to overcome. Major BES software

packages cannot interpret and filter raw BIM models from architects and designers. Likewise, there

is not yet practical pathway to import data back into the BIM model once construction is complete.

BIM software developers have integrated intrinsic energy modelling capabilities into the software

over the past 5-10 years, although these lack the detail and sophistication required for building

tuning using simulation.

In the current market, there are opportunities to derive more accurate building simulations that more

closely match the operation and control of buildings. This requires a much more detailed approach

to the creation and control of building HVAC systems than is conventionally undertaken for energy

analysis. However, there are several firms that are developing automated methods of analysing

building control systems and creating models that accurately mimic HVAC systems. Combined

with detailed, up-to-date weather data, these simulations can provide accurate feedback to facility

managers in time to resolve issues that may be undermining energy performance. However, there

are limits on the quantity of information that facility managers can interpret and the actions that can

be taken on any performance alerts that may arise. Further automation is possible to provide

authorisation for the performance management system to control aspects of the BMS. Any

automated control must be limited, however, since overreach could lead to the undermining of the

primary purpose of building services, i.e. the provision of safe, well-lit, comfortable spaces to live

and work.

KEY OUTCOMES AND CONCLUSION

Building simulation for tuning is a useful tool in an industry where access to data is continually

increasing. Building simulation can act as an overlay across this data inundation and provide a

guide for structure, presentation and context.

The introduction of simulation into tuning is methodologically straightforward. Current BES

software packages allow the creation of models that accurately account for the energy consumption

of building services. Outputs from the BES model are then categorised by time across the year (to

account for weather variation) and by each separate building service. Critical to the process is the


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alignment of available detailed energy submetering groups. These can be achieved with virtual

metering, with care being taken to avoid overuse of arithmetical computations from metering

outputs to avoid compounding measurement errors. The maintenance of data integrity is also

imperative and should be explicitly specified in construction contracts.

The simulation and tuning process at Dandenong GSO has exemplified a practical case in the use of

BES to complement ongoing building performance tuning. A BES model was constructed for the

building that was used during the design phase and to assist in the achievement of Green Star

Ratings. The model was then further augmented to provide a realistic representation of the building

performance in operation. Building service targets allowed for the corrective actions in the case of

malfunctions and for the proposal of engineering upgrades to improve energy efficiency.

Significantly, this prompted the implementation of a static pressure reset control for the AHU fans.

This has resulted in a large reduction in fan energy consumption, leading to an improvement in the

building’s NABERS energy rating, despite an increase in building use by tenants.

It was noted that it would be ideal to apply real-time weather data to simulation outputs for

benchmarking purposes. From a practical perspective, typical weather data for a given location is

sufficient to allow for performance evaluation and maintenance works. There is a limit to the

quantity of information that can be feasibly assessed by facility managers. Automated methods and

systems can provide very detailed simulation results and feedback on performance. However, as it

stands, the process presented for GSO has proven to be valuable in enabling the building to exceed

its original energy performance targets.

Building simulation can readily be applied to practical building operations to monitor performance,

track NABERS Ratings point out faults, investigate opportunities for improvement, and continually

tune the building. The case study of Dandenong GSO indicates that building simulation provides a

useful analysis tool well into the life of building operation.

ACKNOWLEDGMENTS

The author wishes to acknowledge the ongoing support and cooperation provided by JLL facility

management at Dandenong GSO.


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REFERENCES

1. Summary of the Paris Agreement. United Nations Framework Convention on Climate Change,

http://bigpicture.unfccc.int/#content-the-paris-agreemen Viewed 2016-06-26.

2. Lucon O., D.et al., 2014: Buildings. In: Climate Change 2014: Mitigation of Climate Change.

Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel

on Climate Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Green Star - Office v3 Technical Manual 2008, Green Building Council of Australia. Sydney,

Australia.

3. User Manual – IDA Indoor Climate and Energy, Version 4.0, 2009. Equa Simulation AB, Solna,

Sweden.

4. NABERS Guide to Building Energy Estimation 2011, NSW Office of Environment and Heritage,

Sydney, Australia.

5. NABERS Energy for Offices Reverse Calculator 2015, 11 edn, NSW Office of Environment and

Heritage, Sydney, Australia, 21 January 2015.

6. Tableau. www.tableau.com Viewed 2016-06-26.

7. Taylor, ST 2007. ‘VAV System Static Pressure Setpoint Reset’. ASHRAE Journal, June 2007.

http://bigpicture.unfccc.int/#content-the-paris-agreemen

http://www.tableau.com/

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