# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 3 Methods
### Properties of the Virtual Model  
The computational model for this study is based on a simplified rectangular room geometry, rather than based on specific real-world case studies. The space measures 3.0 m in height, 6.0 m in width, and 3.0 m in depth, resulting in a floor surface area of 18.0 m\({}^{2}\) and a volume of 54.0 m\({}^{3}\). Reflectance values were set at 40% for the floor, 60% for the walls, and 80% for the ceiling across all models. Regarding fenestration, a single square window centered on the facade with a 0.7 transmittance was adopted for all virtual models. This configuration was chosen to isolate the impact of window size variations, intentionally excluding the influence of window position and transmittance. While the present study primarily focuses on the comparative analysis of dynamic metrics rather than the impact of various window configurations, future research could fruitfully explore the effects of diverse window placements and surface reflectances, assessing the metrics' performance under such varied conditions as highlighted by previous studies [62; 64].  
The window dimensions were varied in both width and height to achieve window-to-wall ratios (WWRs) of 20%, 30%, and 40%. While not being based on any national or European regulation, this specific range was selected based on feasibility considerations for common use environments, balancing minimum illuminance requirements with overall energy performance. While a fully glazed facade might theoretically yield optimal results across all metrics--along with minimized electric lighting energy consumption--it would lead to a drastic increase in other energy demands for heating and cooling. Fifty calculation points positioned 0.8 m above the floor, distributed in 5 lines and 10 rows at 0.6 m intervals, formed the study grid across the analysis plane--following the point layout required for sDA calculations. This configuration enabled more precise spatial metric calculations, and it allowed for simultaneously accelerating the computational process by avoiding an excessively dense array of points. For the non-spatial dynamic metrics, the authors decided to focus on the central section displayed in red in Figure 1; since the central areas of the room are typically occupied for the majority of the time, its results are more relevant for the comparative analysis of metrics. In future studies, it would be of great interest to evaluate the behavior of the metrics in the lateral sections as well.  
The calculation process incorporated two specific occupancy profiles: a full-day schedule from 08:00 to 18:00--a timetable commonly used for the calculation of sDA--and a half-day schedule spanning 08:00 to 14:00. This dual approach allows for a comprehensive analysis of lighting performance under varying usage patterns, capturing both the full spectrum of a typical workday, including critical peak solar hours and late afternoon conditions, and the distinct implications of morning-focused occupancy by concentrating on the period of highest natural light availability. Daylight Saving Time (DST) was deliberately excluded, reflecting the anticipated discontinuation of DST within the European Union, offering results uninfluenced by seasonal clock shifts for long-term applicability.