Practical application

Indoor lighting system is one of the significant contributors of building’s energy consumption. This study aims to improve indoor lighting system efficiency by developing an adaptive lighting control system that integrates the spatial dimensions of DF and real-time outdoor illuminance sensing based on soft sensors in two educational rooms in Yogyakarta, Indonesia. The research developed the system using in situ measurements, developed predictive and control algorithms, integrated them into a building energy management system, and validated its performance. The soft sensor algorithm demonstrated good predictive performance with MAE and RMSE distributions of 8.09 ± 5.69 lux and 10.76 ± 7.70 lux at the first room, respectively, and 8.62 ± 6.54 lux and 11.85 ± 8.95 lux in the second room, respectively. The constructed DF-based soft sensor performs accurately in real-world implementations with MAPE values of 11 % ± 8 % and 14 % ± 6 % for the first and second rooms, respectively. The tests also showed data acquisition, communication, and algorithm computation duration of 57.9 ± 0.4 ms, 22.7 ± 0.8 ms, and 6.1 ± 1.2 s, respectively, with no data loss during the transmissions. This study successfully developed and implemented an adaptive lighting control system based on a DF-based soft sensor that is reliable, scalable, and applicable for both existing and new buildings. However, the system should be treated as an operational estimation framework for maintaining illuminance within a certain performance range, rather than as an absolute point-estimation model with high precision at every location.

The proposed DF-based soft sensor system offers a practical and scalable solution for adaptive indoor lighting control with a cost-effective yet accurate, reliable, and applicable approach. The system was developed using a calibrated outdoor illuminance sensor, an embedded microcontroller, and integration with a Building Energy Management System (BEMS), and thus allows real-time prediction of indoor illuminance without the need for costly hardware sensor networks. This approach reduces installation complexity and operational costs and is applicable to both existing and new buildings, is flexible, and is scalable for other types of buildings, occupant and activity patterns, and climates and regions.