Sustainable Energy Performance Optimization of Occupancy Sensor Placement in Smart Lighting Systems for University Classrooms

This study proposes a reproducible methodology for optimizing occupancy sensor placement and assessing the sustainable energy performance of smart lighting systems in university classrooms. The research was conducted in Block H of the South Campus of the Universidad Politécnica Salesiana, Quito, using one representative classroom for detailed geometric analysis and extending the optimization to eight classrooms with different dimensions, areas, and installed lighting configurations. The proposed framework integrates Voronoi-based spatial analysis, genetic algorithm optimization, and dynamic occupancy-based lighting control simulation as a retrofit-oriented strategy for existing educational buildings. For the representative classroom, the optimized sensor position was located near the geometric center of the room and achieved an estimated spatial coverage of 94.7% under the adopted sampling-based geometric model and an effective detection radius of 6 m. The multi-classroom analysis showed that the required number of sensors depends on classroom geometry and the adopted sensing radius; at R = 6 m, most classrooms satisfied the 90% coverage criterion with one sensor, while the largest classroom required two sensors. Based on occupancy schedules and automatic control rules, the dynamic simulation showed reductions in lighting operating time of 48% and 52% for 10 h and 12 h daily scenarios, respectively. These reductions were translated into lower daily and monthly energy consumption across different lighting configurations. The results indicate that optimized occupancy-based control can support sustainability-oriented energy management in university buildings by reducing unnecessary electricity use while preserving the existing lighting infrastructure. However, the results are limited to occupancy-based control and do not include daylight harvesting, photometric validation, or a complete economic payback assessment.