## 2 Materials and Methods
### Modelling Approach and Analysis Criteria  
The simulated work plane illuminance (WPI) values were transcribed into a tabular form which contains the average illumination of four grid points in each row (lines as shown in Figure 5a). Furthermore, the quality of daylight distribution employed the uniformity index (Ui) of interior daylight, which is the ratio of the average illumination to the maximum illumination. This ratio should not be less than 0.6 above the work plane, as per the NBN L13-001 code and international guidelines [34]. The simulations were performed within the design days at the summer and winter solstices and during the mid-season (21 June, 21 December, 21 March) at 9.00 a.m., 12.00 p.m., and 3.00 p.m. to evaluate the annual variation and the critical period. The choice of the simulated points (grids) inside the office followed the grids plotted as per the arrangement of work planes. The minimum number of points in the deep-office prototype was 28. The distance between the simulated grid points was kept at 1 m for accurate results, as depicted in Figure 5. The reflection by the surfaces used in this experimental set is presented in Table 5.  
For more details, the analysis of LSPV in last phase used climate-based daylight metrics that include the useful daylight illuminance (UDI), daylight autonomy (DA) and daylight glare probability (DGP), supported by a 3D illuminance contour map for glare assessments. The criteria of assessments of each metric are summarized in Table 6. The simulation of lighting energy consumption is through daylight control strategy (photosensor controlled dimming: 300 lux), where eight light-emitting diode (LED) indoor artificial lights (17.5 W luminaire power, luminaire luminous flux: 1552 lm and luminaire efficacy: 89 lm/W) were placed in the middle of line 1, 3, 5 and 7 and linked with illumination sensors (S1, S2, S3, S4, S5, S6, S7 and S8). The light distribution of the lighting in this study is shown in  
Figure 5: (a) Plan view representation of the grid points, illuminance sensors and lighting positions, (b) light distribution of conical illuminance.  
\begin{table}
\begin{tabular}{c c} \hline
**Material** & **Reflection Coefficient** \\ \hline Ceiling & 80\% \\ Floor & 20\% \\ Wall & 70\% \\ Furniture & 50\% \\ Light shelf & 90\% \\ Solar panel & 07\% \\ Reflector & 99\% \\ Double glazing low-E & VLT 79\% \\ \hline \end{tabular}
\end{table}
Table 5: Material reflection coefficient percentages.  
Figure 5b. The illumination sensors were placed 0.85 m above the floor surface, based on the height of the work plane. Their operating profiles follow their real use from 8.00 a.m. to 5.00 p.m.  
The amount of energy produced by the multi-crystalline photovoltaic module was derived using the Simple model. This model was simulated by multiplying the fraction of surface area that had active solar cells by the total solar radiation incident on the PV array; module conversion efficiency was set to \(n=10\%\). While the area of the solar module used varies according to the case set used in this study, the amount of energy produced depends primarily on photovoltaic array efficiency and inverter modelling efficiencies at the operating conditions. Nevertheless, the difference between lighting energy consumption and the energy produced is either positive or negative. Hence, for every configuration, the net energy production is the most significant performance indicator required to determine the optimum LSPV configuration.