BIM-G Model Validation

A model was created of the building variant described in Section 3.2.2, using the ESP-r building simulation tool [9]. The simulation was populated with a profile of casual thermal gains due to occupants, appliances and lighting, which was generated as discussed in Sections 3.3.2, 3.6 & 3.7. A ventilation profile, as discussed in Section 3.5, was specified, alongside the thermal demand periods (for space heating control) defined in Table 3.24. This simulation exercise was used to investigate the effects of changing casual thermal gains from appliance and lighting on domestic overheating and potential space cooling requirements [3].

The aforementioned simulation results were also used to provide a degree of validation for the BIM-G model. Similarly, estimated annual results for the BIM-G

model, using the Primary Demand Scenarios defined in Section 3.9, were compared with results from version 4 of the TARBASE Domestic Energy Model (DEM) [40]. The results of this validation exercise, in terms of required input to SHDS, are presented in Table 2.5.

BIM-G TARBASE TARBASE: No Solar ESP-r

Space Heating Requirements (kWh) 18,137 15,240 20,064 17,658

Difference vs. BIM-G (kWh) - -2,897 1,927 -479

Difference vs. BIM-G (%) - -16% 11% -3%

Table 2.5: Space Heating Requirements (as input to space heating distribution system) results from BIM-G thermal model validation exercise

Due to the differences between the BIM-G model, ESP-r, and the TARBASE model, as summarised in Table 2.6, a significant difference in reported thermal requirements was expected. In reality, the difference between BIM-G and ESP-r was minimal, although it should be noted that some functions like latent heat transfer and thermal bridging were not configured in ESP-r. When comparing with the steady state TARBASE DEM, it is important to bear in mind the co-incidence of thermal gains (from appliances and solar radiation via glazing) and Thermal Demand Periods (TDPs). On a sunny winter’s day, for instance, much of the heat derived from solar gain to the building during the day, when the dwelling is unoccupied, will have been lost before the next TDP. To understand the range of potential impacts this effect could have on a steady state model, TARBASE space heating requirements were calculated with and without solar gains, as presented in Table 2.5. These results are distributed around the BIM-G result, providing some confidence that the BIM-G model is generally agreeable with other validated models.

BIM-G Tarbase ESP-r

Dynamic, 5-second Steady State Dynamic, used 15-minute Discrete climate days Annual Average Temperature Continuous annual hourly temps Discrete 5-second thermal gains

& ventilation profiles

Annual averages (over TDPs) for gains & ventilation value

Estimated hourly gains & ventilation profiles

Solar gain profile calculated from hourly data

Solar gain assumed to occur during TDP

Solar gain profile calculated from hourly data

Simple dimensions required Simple dimensions required Needs full definition of building geometry

1D 1D 3D

No Latent heat transfer No latent heat transfer Can do latent heat transfer, if configured

Part Radiation No Radiation, except surface heat transfer co-efficient in U- Value

Full Radiation

Pre-Simulation building No requirement, as steady state Pre-Simulation building During TDPs, comfort temp not

always maintained, due to restricted output of SHDS and heat generators

Thermal comfort always met during TDPs

Thermal comfort always met during TDPs

Thermal Bridges using U-value adjustments

Thermal Bridges using U-value adjustments

Thermal Bridges could not be successfully implemented in version of the software used Simplified thermal mass:

accounts for wall construction (with doors) only

Does not account for thermal mass

Considers thermal mass of all building elements

Table 2.6: Comparison of thermal modelling features (BIM-G, TARBASE DEM & ESP-r)

It is argued that simulating annual thermal demand without a high level of accuracy is not an issue, so long as the annual thermal demand is not an outlier on the thermal demand distribution presented in Figure 3.5. However, the daily profile of space heating, and the response of internal air temperature (which is the control driver for space heating control) to SHDS input is important. This forms the basis of the temporal response of thermal demand to supply, which is argued in Chapter 1 to be important in the modelling of µCHP system transient performance. In Figure 2.11, the space heating demand as calculated in ESP-r, to balance heat loss during the 15-minute timestep, is compared with the thermal output from the SHDS, as calculated by the BIM-G model, averaged to 15-minute temporal precision. There are two distinct differences; at the start and then the end of each TDP. Without full definition of a SHDS, ESP-r does not consider the thermal lag introduced by the thermal mass of the water in the SHDS. This can be observed within BIM-G by comparing the SHDS thermal input with thermal output in Figure 2.11. In addition, BIM-G includes the transient performance curve of the condensing boiler, where output is limited as the boiler reaches nominal operating

temperature. Once a TDP ends, some of the stored heat within the boiler is transferred to the SHDS, maintaining its temperature for longer, prolonging SHDS thermal output.

Figure 2.11: Comparison of SHDS thermal input and SHDS output from BIM-G model with ESP-r space heating demand, on 15-minute timebase, for Weekday operating pattern and Shoulder climate demand scenario

Figure 2.12 presents the internal air temperature simulated by BIM-G and ESP-r, where the difference at the start and end of the TDPs has been explained previously in the context of SHDS thermal output. During the TDPs, the temperature fluctuations are expected due to control hysteresis (as presented in Figure 2.8) and the thermal lag introduced by the thermal mass of the SHDS. The general agreement between the temperature plots adds confidence to the validity of the BIM-G thermal model as a tool to estimate the approximate transient response of a space heating distribution system within a dwelling.

Figure 2.12: Comparison of Internal Air Temperature between BIM-G model and ESP-r, on 15-minute timebase, for Weekday operating pattern and Winter climate demand scenario