Both models used the same base weather data consisting of hourly values of ambient temperature, humidity, total, and beam radiation on a horizontal surface and wind speed。 Hence, the valida- tion also includes the calculation of radiation on tilted surfaces and the method used to determine sub-hourly values of these param- eters。 A 5 min time-step was used for both simulations and for all subsequent analysis。

Fig。 4 shows the internal zone temperature and absolute humid- ity ratio predicted by the model implemented here compared  with

Tsa  = Tamb +

˛G + oεl

。 4

sky

hco

4    。

amb

(4)

the corresponding values output by the TRNSYS Type 56 model。 The ambient temperature and humidity are also shown for  reference。

The models predict a very similar building temperature profile and identical building humidity。 The building temperature drops

where Tskyis the sky temperature, εl  is the longwave emissivity of

the roof, hco  is the external convective heat transfer coefficient and

˛G is the total radiation absorbed on the surface of the   roof。

initially at the start of the simulation due to a combination of heat loss to the ambient temperature and night sky radiative cooling。 However,  throughout  the  period  the  building  remains generally

180 M。J。 Goldsworthy / Energy and Buildings 135 (2017)  176–186

Fig。 4。 Comparison of building temperature and absolute humidity as predicted by the current model and using the TRNSYS Type 56 model。

hotter than the ambient due to the thermal mass contained within the structure and the effects of the internal heat  loads。

A small difference of <1 ◦C between the maximum daytime tem-

perature predictions is present on several days with the model here predicting slightly hotter temperatures than the TRNSYS Type 56 model。 The most likely sources for this discrepancy are the method of calculating radiative transfer and the window heat transfer cal- culation。 In the case of the latter, the simplified model employed here used single values of the SHGC and the overall U-value whereas the Type 56 model employs a detailed window heat transfer model with incident angle dependent transmittance and absorbance and temperature dependent heat transfer coefficient。 Hence, small dif- ferences in building conditions are expected。 Nevertheless, the results show that the current simplified model is suitable for assess- ing the relative impact of different building thermal parameters on the comfort conditions with PV-A/C  conditioning。

4。 Methodology & parameter selection

In this study, occupant comfort is the key performance metric since the operating cost to run the off-grid system is negligible。 Here the total annual degree of discomfort dd, measured in degree- hours above 25 ◦C, and the annual percentage improvement in the degree of discomfort pddimpr relative to the same  building with- out conditioning, were used as metrics。 When evaluating systems with different battery capacities and numbers of PV modules, it is clearly beneficial to minimise both of these parameters to minimise the capital investment, though here no economic calculations have been included。

In total 13 different building parameters have been considered as given in Table 1。 The meaning of the orientation and aspect parameters and the window location may be clarified with refer- ence to Fig。 5。 In order to assess the absolute as well as relative influence of each parameter on the occupant comfort, generally two values of each parameter (with the exception of the orientation and wall construction) were considered; one representing a typical ‘above average’ value and one a typical ‘below average’  value。