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1 THE OFFICIAL JOURNAL OF AIRAH Ecolibrium DECEMBER 217 VOLUME RRP $14.95 PRINT POST APPROVAL NUMBER PP352532/1 THE ECOLIBRIUM ANNUAL AIRAH AWARDS SPECIAL One with the lot Quarantine facilities use serious kit... just like this. ESTABLISHED 1947 Celebrating 7 years as the official journal of AIRAH

2 PEER-REVIEWED TECHNICAL PAPERS Building tuning using simulation A practical case study By Matthew Webb, M.AIRAH Umow Lai INTRODUCTION In order to mitigate the worst effects of climate change, significant reductions in greenhouse gas emissions will be required in the next 3 years in order 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]. Therefore, the property sector has a responsibility to find strategies 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, and building tuning can be greatly assisted through building energy simulation. In 29, 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. 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, commissioning and operation. The BES for GSO continues to be an important comparative tool in the ongoing energy management and building tuning. PURPOSE OF BUILDING ENERGY SIMULATION Initially, BES was used as a tool to inform the architectural and services design. A BES model was constructed in IDA ICE v4. [3] to test design initiatives and to ensure that the building would be capable of achieving the desired performance ratings. 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 estimates (alongside water 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. METHODOLOGY BUILDING TUNING WITH BES Building Energy Simulation Model Building documentation was translated into an accurate BES in IDA ICE v4. (Figure 2). Annual simulations were conducted using a variety of building performance initiatives during design development. 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 from the models was used to determine the probability of achieving NABERS performance targets and as evidence for the Green Star submissions. 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 sub-metering installed on site. In the case of GSO, virtual metering categories had been specified for the building management system (BMS). This would align with the major components of services energy that were relevant for base building energy 46 ECOLIBRIUM DECEMBER 217

3 SIMULATION RESULTS AND INTERPRETATION Figure 2: 3D perspectives of IDA ICE model.. This was a key consideration, as the use of BES for building tuning could only be undertaken with accurate energy sub-metering. Along with an estimate for the actual building performance, a specific set of benchmarks was established that related building services energy 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 in order 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 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) and the NABERS maximum energy (N benchmark ), the annual energy target for each building service (E s,a ) was calculated as follows: E s,a = P s,a N benchmark (1) Annual 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 of weather-related services was divided by month to give a monthly percentage (P s,m ). As with the percentage mix of each service in the overall energy, 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 (E s,m ) was then calculated as follows: E s,m = P s,m E s,a = P s,m (P s,a N benchmark ) (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. 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. Electricity [all kwh] Building service BES energy Lighting Ground L7 19,573 Lighting Basement 42,233 Chiller 91,97 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] 83,356 Domestic Hot Water [MJ] 154,31 Annual Total [MJ] 984,666 Table 1: Green Star As Built BES Results. Tuning targets Using the NABERS Reverses calculators, the building area (14,462.5 m2) and operational hours (6 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 1,38, Star 1,344, , Star 1,8, ,41 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 energy data obtained from other DECEMBER 217 ECOLIBRIUM 47

4 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 217) targets are shown in Table 3. \ Building service Electricity [all kwh] 4.5 Star NABERS Simulated Target 5. Star NABERS Simulated Target Lighting Ground L7 13,834 97,5 Lighting Basement 39,458 29,255 Chiller 249,28 184,771 AHU fans and Fan Coil Units 32, ,893 Pumps 88,261 65,44 General exhaust (including carpark ventilation) 92,414 68,519 Lifts 34,266 25,46 Rainwater and stormwater pumps Security, communications and other general power 62,32 46,193 2,767 15,398 Annual Total [kwh] 1,38, ,88 Natural Gas [all MJ] Boiler [MJ] 1,529,11 1,133,729 Domestic hot water [MJ] 284,163 21,688 Annual Total [MJ] 1,813,264 1,344,417 Table 3: Monthly 5 Star NABERS simulated operational energy targets. 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 [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 for the month of January 217. OPERATIONS AND TUNING 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 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 for GSO is plotted against the monthly simulated 5 Star NABERS Energy benchmarks in Figure 4 (as at July 217). This gives information on how the current performance relates to both historical performance and simulated benchmarks. 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 to 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. Service Observation Outcome Lighting Lighting Lifts AHU fans and heating Carpark ventilation Public holiday matches business day. Daily use high in common areas. Over-use of heavy duty goods lift. Over- during heating. Excessive (up to 1 times expectations). Controls adjusted to account for public holidays (this has required constant resetting during building lifetime). Reset light timers to 15-minute intervals. Halogen fittings replaced with LEDs. Staff advised to use passenger lifts only, resulting in a decrease in lift energy. Supply-air temperatures increased in UFAD system. Fan energy decreased. New controls regime written to more strictly control fan operations based on carbon monoxide control. Energy now 5% of initial start-up. Table 4: Building tuning observations and corrective actions. AHU controls tuning In 215, based on the high 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 48 ECOLIBRIUM DECEMBER 217

5 Meter Meter Description 5 Star Benchmark 5.5 Star Benchmark Actual data Deviation % Plot 95% 15% VM12-Month Mechanical 57,321 43,334 52,68 Benchmark 5. Star 5, 216 January 217 January 5, 4,5 4,5 4, 4, Actual Data Electricity (kwh) 3,5 3, 2,5 2, 1,5 3,5 3, 2,5 2, 1,5 Benchmark Electricity (kwh) 1, 1, Figure 3: Monthly dashboard display for mechanical services (January 217). January February March April May June July August September October November December 1, Actual Data Monthly Electricity (kwh) 9, 8, 7, 6, 5, 4, 3, 2, 1, Figure 4: Historical electricity performance of GSO against simulation benchmarks (in red), as at July 217. DECEMBER 217 ECOLIBRIUM 49

6 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 216. 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 1% open, the BMS will allow dampers to open and static pressure to be lowered until dampers are 1% 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. The effects of this change have been monitored through the past six months. AHU fan energy has decreased markedly (see Table 5). During warmer (cooling-dominated) weather, fan energy 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 has peaked during morning warm up before dropping considerably to about 1% of the peak value. Figures 5 and 6 below show the behaviour of the fans with static pressure reset in January 217 and May 217. The equivalent month from the previous year is shown for comparison. Year Month Previous year s [kwh] Current year [kwh] Comparison to previous year 216 December 22,985 1, % 217 January 19,867 11, % February 22,1 8, % March 21,69 11, % April 2,158 5, % May 24,279 11, % June 24,65 11, % July 24,399 11, % Table 5: AHU fan energy comparison. 216 January 217 January 2 Total fan energy (kwh) Benchmark Consumption Below Benchmark Consumption Above Benchmark Figure 5: AHU fan energy January 216/ ECOLIBRIUM DECEMBER 217

7 216 May 217 May 2 Total fan energy (kwh) Figure 6: AHU fan energy May 216/217. The overall effect of the static pressure reset control has been to significantly reduce the mechanical services energy 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 217 and an increased occupancy and use of the building. 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 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. Tuning targets are not static. Building tuning is a continual, iterative process where targets are adjusted to reflect the desired benchmark energy 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 ultimate 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 DECEMBER 217 ECOLIBRIUM 51

8 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 five to ten 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 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 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, 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 in order 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. REFERENCES [1] Summary of the Paris Agreement. United Nations Framework Convention on Climate Change, Viewed [2] Lucon O., D.et al., 214: Buildings. In: Climate Change 214: 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 28, Green Building Council of Australia. Sydney, Australia. [3] User Manual IDA Indoor Climate and Energy, Version 4., 29. Equa Simulation AB, Solna, Sweden. [4] NABERS Guide to Building Energy Estimation 211, NSW Office of Environment and Heritage, Sydney, Australia. [5] NABERS Energy for Offices Reverse Calculator 215, 11 edn, NSW Office of Environment and Heritage, Sydney, Australia, 21 January 215. [6] Tableau. Viewed [7] Taylor, ST 27. VAV System Static Pressure Setpoint Reset. ASHRAE Journal, June 27. THANK YOU The author wishes to acknowledge the ongoing support and cooperation provided by JLL facility management at Dandenong GSO. ABOUT THE AUTHOR Matthew Webb, M.AIRAH, is a sustainability consultant with Umow Lai, based in the firm s Melbourne office. A former winner of AIRAH s Student of the Year Award, Webb is a biomimicry expert. 52 ECOLIBRIUM DECEMBER 217