The overall building heating and cooling loads and the heat loads associated with the building fabric, ventilation system, solar gain and internal gains were compared with IES simulations.
Figure 6-9 shows the monthly heating plant load for the IES and Excel simulations. Whislt the values for the months of December and January are in fair agreement, for other months the IES model predicts much lower heating plant loads than the Excel model. The annual heating plant load is predicted to be 87 MWh for the Excel model compared with 64 MWh for IES. The IES results are somewhat surprising for the winter months of November, February and March, in relation to the external temperature profile.
Figure 6-9. Monthly net heating plant load for IES and Excel simulations
Figure 6-10 charts the overall cooling load profile for the building for the IES and Excel models, indicating reasonable agreement in summer but less good in winter. On an annual basis the Excel simulation predicts a cooling plant load of 512 MWh compared with 437 MWh for IES.
Figure 6-10. Monthly net cooling load for IES and Excel simulations
Examination of the building fabric heat load profile and ventilation heat load charts (Figure 6-11 and Figure 6-12) indicates that the Excel simulation predicts higher heat losses than IES in winter and slightly lower losses in summer. However, averaged over the year the net heat load profiles for the building fabric and ventilation heat losses are quite similar for the Excel and IES simulations.
Figure 6-12. Monthly ventilation heat load for IES and Excel simulations
A more detailed analysis of the monthly heating and cooling plant loads suggests a possible reason for the discrepancy between the Excel and IES results. Figure 6-13 shows the net thermal load for the heating and cooling plant load for each month (by subtracting the cooling plant load from the heating plant load in each month). This demonstrates a much closer agreement between the Excel and IES results, typically within 5 MWh in any month. A possible explantion follows below.
In any month there can be both heating and cooling demand (for example a need to heat the building in the morning following an overnight fall in temperature, followed by a need to cool it later in the day because internal gains exceed the heat losses from the building fabric and ventilation). For several months, particularly in spring and autumn, the Excel results indicate both heating and cooling loads, whereas for IES the load is predominantly either heating or cooling, with almost no heating and cooling occurring simultaneously within the same month.
Examination of the Excel simulation hourly heat loads confirms that over the course of a day both heating and cooling can occur (but not at the same time), with a heating load between the hours of 5 a.m. and 8 a.m. and a cooling load that could start as early as 9 a.m. in summer (or as late as 3 p.m. in winter) and ending at 5 p.m. Therefore, for many months of the year it is it likely that there will be both heating and cooling loads.
The IES simulations on the other hand appear to show only heating or cooling (but not both) on any given day. In January the heat load typically peaks at just over 50% of the plant capacity
for the first hour, then falls exponentially to about 15% of capacity by the end of the day. In July the cooling plant load is typically quite steady over the whole day at about 70% of capacity, which seems unusual, since the cooling load would be expected to increase during the course of a day as the external temperature rises and due to the warming effect of the solar gain.
Since the net (heating-cooling) loads are similar for both Excel and IES (as are the individual heat loads for fabric losses, ventilation etc.) it is likely that these differences are associated with the algorithms used for setting the operating parameters for the heating and cooling plant over the course of a day. The Excel model relies on using the temperature error at the end of the preceding hour to estimate the output required from the heating (or cooling) plant over the next hour to bring the building back into balance (the thermal capacity of the building os also used within this calculation). The algorithm used within the IES model has not been investigated.
Figure 6-13. Net monthly (heating-cooling) plant load for IES and Excel simulations
The daily heat gains from the IES simulation for natural ventilation and infiltration are shown in Figure 6-14. Comparison with the equivalent Excel model results for day 3 (Figure 6-6) confirms that the shape of the daily profiles is very similar (the plots are of opposite sense because the IES plot is for heat gain whereas the Excel model plot is for heat load).
Figure 6-14. IES results plot showing the natural ventilation and infiltration heat gains over 2 days in January
The peak (cloud free) solar gain profiles (Figure 6-15) match well for the IES and Excel simulations. However, since the Excel model uses a more limited weather data set, the daily average illuminance and solar gain in each month are estimated using a ‘cloud transmittance’ factor which has been derived empirically and is the same value for every calendar month. Refinements to the model, to incorporate a seasonal cloud cover factor would be expected to improve the correlation of monthly gains and the overall heat load for the building.
Figure 6-15. 24 hour peak solar gain (28 April) for IES and Excel simulations
Wed Thu Fri Sat
100 50 0 -50 -100 -150 -200 -250 -300 -350 G a in ( k W )
Date: Wed 01/Jan to Fri 03/Jan
Natural vent gain: 6 rooms (office_6st_1800fa_rect_121029_0deg.aps) Infiltration gain: 6 rooms (office_6st_1800fa_rect_121029_0deg.aps)
Figure 6-16. Internal heat gains according to the IES and Excel model simulation results
The internal heat gain chart (Figure 6-16) demonstrates good correlation between IES and the Excel model and provide support for the Excel model algorithms. The minor differences are probably due to the way that the Excel model calculates the number of working days in each month (pro-rata according to the total number of days in each calendar month, rounded to the nearest integer number).