Adaptive lighting System Using Lux and Tod Data


EOI: 10.11242/viva-tech.01.09.17

Download Full Text here



Citation

Jaydeep Ghadshi,Anojkumar Yadav, Dhawal Mistry, Vishal Pal, "Adaptive lighting System Using Lux and Tod Data", VIVA-IJRI Volume 1, Issue 9, Article 17, pp. 1-7, 2026. Published by Electrical Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

The Adaptive Lighting System for smart homes that intelligently regulates indoor lighting using Lux intensity and Time-of-Day (Tod) data. The system integrates a Real-Time Clock (RTC) module to track temporal information and a Lux sensor to continuously measure ambient light levels. An Arduino microcontroller processes these inputs to dynamically control lighting through a relay module, enabling automatic switching and brightness regulation based on daylight availability and predefined time schedules. By adapting illumination levels to user activity patterns and natural lighting conditions, the proposed system ensures optimal visual comfort while minimizing unnecessary power consumption. Experimental implementation demonstrates that the system effectively reduces energy usage during daylight hours and peak non-occupancy periods without compromising user convenience. The design is cost-effective, easy to deploy, and scalable, making it suitable for modern smart home environments. This adaptive approach contributes to sustainable energy management by reducing electrical load and promoting efficient utilization of artificial lighting resources. The proposed solution offers a practical and environmentally responsible method for enhancing automation, comfort, and energy efficiency in residential applications.

Keywords

adaptive lighting, Arduino, energy efficiency, Lux sensor, smart home, time-of-day data.

References

  1. A. Kumar, P. Singh, and S. Sharma, “Design and Implementation of Automatic Light Intensity Control Using LDR and Arduino,” International Journal of Engineering Research & Technology (IJERT), vol. 8, no. 7, 2022.
  2. A. Kostic and P. Djokic, “Smart Adaptive Lighting System for Multifunctional Room” IEEE Conference on Lighting Systems, 2020.
  3. Ashish Pandharipande, Shuai Li (2013) Light-Harvesting Wireless Sensors for Indoor Lighting Control. In: IEEE Sensors Journal, vol.13 pp 4599 – 4606.
  4. Andrzej ozadowicz Jakub Grela Energy saving in the street lighting control system—a new approach based on the EN-15232 standard. In: Energy Efficiency, vol.10, pp 563–576, Doi: 10.1007/s12053-016-9476-1.
  5. B. Patel and R. Mehta, “Energy Efficiency Through Automated Lighting Systems,” Journal of Sustainable Technology, vol. 5, no. 4, 2021.
  6. Cem Çatalbaş, “Environment Adaptive Lighting Systems for Smart Homes” Ankara University, September 2017.
  7. Chen, Z., et al. “IoT-Based Smart and Intelligent Smart City Energy Optimization,” Sustainable Energy Technologies and Assessments, 49(2022) p. 101724.
  8. C. Zhang and D. Li, “Photodiode Applications in Light Intensity Sensing for Automation,” Sensors and Actuators Journal, vol. 11, no. 2, 2020.
  9. D. Caicedo and A. Pandharipande, “Daylight-Adaptive Lighting Control Using Light Sensors” IEEE Transactions on Industrial Electronics, 2014.
  10. Dr. David Solomon Raju MTech Scholar, Associate Professor., “Automatic Light System Using LDR And Pir Sensor” JETIR August 2024.
  11. D. Johnson et al., “Smart Lighting Systems: Integration of IoT and Artificial Intelligence,” IEEE Access, vol. 9, 2021.
  12. F. Pinto, Guilherme M. Soares, Thiago R. F. Mendonça, Pedro S. Almeida, Henrique A. C. Braga Smart Modules for lighting system applications and power quality measurements. In: 2014 11th IEEE/IAS International Conference on Industry Applications, IEEE, Doi: 10.1109/INDUSCON.2014.7059448.
  13. International Energy Agency – SHC Task 61 Team,” Review of Lighting and Daylighting Control Systems” Technical Report B.2, 2021.
  14. Monika Vaghela, Harshil Shah, Hardik Jayswal, Hitesh Patel, Arduino Based Auto Street Light Intensity Controller. In: Inventi Rapid: Embedded Systems Vol. 2017, Issue 3, pp 1-4.
  15. Mumtaz Alam., “RTC Based Automatic Street Light Using Arduino & LDR” November 2023.
  16. Putrada, A. G., et al. "Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey," IEEE Access, 10(2022) pp. 45,137–45,178.
  17. Vidhi Gupta, Harsh Yadav, Sourabh Vijayaash, Dr. Meha Sharma, “design of adaptive lighting based smart system” March_2019.