Building 2

Validation of the base building model was conducted with nine months of consumption data taken from utility bills from 2013. Historical climate data was used from a weather station located approximately 5 km from the site, with records covering the period for which utility data was accessible. During the validation period, the building was only partially tenanted, when the bottom three floors were unoccupied. This was modelled in the simulations. An acceptable level of accuracy was defined in accordance with ASHRAE (2012), as a model having a CV (RMSE) of less than 15%, and a NMBE of less than 5%. It should be noted that ASHRAE (2012) stipulates the use of 12 months of baseline data to ensure a model is calibrated, which was not accessible for this particular building.

The monthly base building energy consumption for the calibrated model is shown in Figure 7-7, along with the base building consumption from utility bills. Following the calibration process

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outlined in Section 7.2.2, the calibrated model produced a CV (RMSE) of 9.1% and a NMBE of - 0.2% compared to the utility data, and can therefore be considered calibrated according to ASHRAE (2012).

Figure 7-7 Monthly measured and predicted energy consumption for Building 2 calibrated model. Error bars show ±15% of base building consumption. Base building predictions made using the four modelling protocols are also shown.

The predicted monthly consumption for the case study building simulated with the default assumptions from the four protocols tested was also displayed in Figure 7-7. All four protocols predicted annual base building energy consumption greater than the measured data. The JV3 protocol predicted the highest energy consumption (12.8% greater than measured) followed by NABERS (10.0%), and Green Star (7.6%). ASHRAE predicted consumption 1.76% greater than measured. It should be remembered that these values are for partial occupancy. If applied to a fully occupied building, it is likely that the influence of the default values would have been more substantial. This is discussed further in Chapter 8. It was expected that the ASHRAE protocol would result in a higher predicted consumption, given the tighter assumed comfort bands, and the relative importance of cooling set-point demonstrated in Chapter 6. Reasons for why this did not occur can be postulated through an examination of the predicted energy end-use breakdown.

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The annual whole building energy end-use breakdown of the building, as predicted by simulations with the four simulation protocols is shown in Figure 7-8. It can be seen that the NABERS protocol results in the highest predicted whole building consumption, 20.4% greater than the ASHRAE protocol, which gives the lowest predicted consumption. The results for the ASHRAE protocol show the lowest lighting and ICT consumption, as was the case for Building 1. The reason for this can be clearly seen in Figure 7-9, where the equipment and lighting power density diversity profiles are shown. The equipment and lighting power density default value has been combined with the recommended diversity profile, to illustrate the changing internal gains assumed by each protocol. The ASHRAE protocol predicted a peak internal gain of 14.7 W/m2, compared to 24.0 W/m2 predicted by the JV3 protocol. This substantially greater internal heat load, at a time of maximum cooling demand, had a significant influence on the predicted base building energy consumption of the case study building.

Figure 7-8 Predicted annual energy end use breakdown for Building 2 simulated with the use of four simulation protocols and compared with a simulation ‘calibrated’ against actual performance data.

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Figure 7-9 Lighting and equipment internal gains profile for the four tested protocols.

The calibration metrics calculated for the base building predictions from each protocol are shown in Table 7-3. The NABERS, Green Star, and ASHRAE assumptions resulted in acceptable CV (RMSE) values, but only the ASHRAE assumptions also produce an output with an acceptable NMBE value.

Table 7-3 Monthly discrepancy between measured and predicted base building energy consumption with the use of four simulation protocols to represent Building 2.

JV3 NABERS Green Star ASHRAE Calibrated

CV (RMSE) 16.4% 14.1% 12.2% 9.9% 9.1%

NMBE 14% 11% 9% 2.0% -0.2%

As was expected, the protocols did not result in predictions of the energy consumption of the building that corresponded closely to the metered data, for a number of reasons including the following. Only the NABERS protocol was designed to be used for absolute predictions; the other protocols were designed for comparison against a benchmark building with similar characteristics. NABERS was by design a conservative protocol. However, for the case study building, there was poor information regarding expected occupant density and schedule, lighting schedule, and tenancy equipment fit-out. It was likely that for a building such as this, with poor documentation, these protocols would be relied upon by a typical modeller for BPS inputs. The results from this study

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show that the default value selected for these parameters can influence the prediction of the base and whole building performance, and has the potential to influence optimal retrofit strategies.

7.4

Discussion

Analysis of the ABCB reference building showed that the selection of simulation protocol can significantly affect predicted energy consumption, and load breakdown for office buildings in Australia. Investigative simulation identified the following as significant features of the simulation protocols:

 NABERS usage schedules assumed that 50% of equipment is left on after –hours, including weekends. The weekend load was significantly higher than predictions from other protocols, and this accounts for almost half of the difference between NABERS and Green Star protocols. NABERS also assumed a low occupant density, which impacted on internal gains and therefore HVAC loads.

 ASHRAE 90.1 required tighter temperature/comfort bands than the other protocols, and this increased energy consumption by almost 15%. This, combined with low occupant density, gave a proportionally higher base building prediction. This was balanced somewhat by lower specified equipment and lighting power densities, however, the load breakdown differed significantly from that predicted by the other protocols.

 JV3 and Green Star predicted similar performances for both locations, and significantly lower than NABERS. JV3 specified a higher peak equipment power density, but this was balanced by low after-hours usage.

For Building 2, the case study building, there was limited information available regarding expected occupant density and schedule, lighting schedule, and tenancy equipment fit-out. It was therefore necessary for the present author to make an engineering judgment as to an appropriate value. The tested protocols were reasonable sources upon which to base assumptions of the uncertain inputs. The uncertain parameters can all influence the performance of the HVAC system substantially, and potentially influence optimal retrofit strategies. A 12.8% difference between the highest and lowest prediction was found for base building consumption, and a 20.4% variation for whole building consumption.

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It was difficult to provide meaningful comment on how representative of the Australian building stock the assumptions embedded in the simulation protocols were, without access to large databases of building attributes. The average Lighting Power Density from the Building Energy Efficiency Register was shown in Section 4.2.1 to be 13.5 W/m2 (SD = 6.00), which suggests that values specified in all protocols may have been low, although reasonable. It was also shown that 73.1% of the functional spaces assessed under BEEC had an NLPD outside of the range of assumptions used in the Australian protocols; with 57% of spaces having a NLPD higher than 12 W/m2. Warren (2003) found an average occupancy in Australian office buildings of 20.6 m2/person, with an interquartile range from 14 to 53 m2/person. This suggests the occupancy assumptions of the Australian protocols may have been too high. However, that study had a limited sample size, and relied on self-reported occupant density. In Australian offices, HVAC set-points are generally 20 – 24 oC on an annual basis, 22.5 ± 1.5 oC in summer, and 21.5 ± 1.5 oC in winter (Roussac et al., 2011). This suggests the temperature bands recommended by Green Star and JV3 may be somewhat less stringent than operating practice.

An extensive literature search revealed no large-scale study that had undertaken actual field measurements of ICT power density, or ICT usage schedules in Australia. This makes it difficult to determine the validity of the simulation assumptions for these three inputs, which were shown in Chapter 6 to strongly affect predicted consumption. All three inputs have some level of occupant or organisational influence, and it is therefore difficult to estimate actual values during an energy audit. The results of this study suggest that it would be useful to devote some effort to improving the accuracy of the simulation assumptions for these particular inputs. It was shown in Chapter 6 that whilst the most significant inputs were related to a buildings’ tenancy energy consumption, they were also the most influential parameters when just the heating and cooling load was considered. Therefore, accurate representation of these parameters is essential when considering upgrades to the base building services. The forthcoming results of detailed monitoring in New Zealand as part of the Building Energy-End Use Study (BEES) may provide further information on these inputs with some relevance to the Australian stock.

The results from the present study have highlighted the importance of including uncertainty and sensitivity analyses as a routine part of BPS, particularly when BPS is used in the retrofit optimisation problem. Results from additional case studies would enhance transparency in this area.

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8

Calibrated Reference Buildings for Simplified Building