Energy Optimization in Consumer Lightings: An IoT-Based Adaptive Control Mode

This study addresses the limitations of conventional lighting systems, which often fail to adapt to dynamic changes in occupancy, daylight availability, and user preferences, resulting in significant energy waste and compromised user comfort. To address these shortcomings, this study presents a cost-effective & highly efficient real-time IoT-enabled adaptive lighting control model that incorporates a time-varying gain controller, passive infrared (PIR) sensor, Pulse width modulated (PWM) LED light sensors, and an adaptive processing unit within a feedback loop to optimize lighting for energy efficiency and user comfort. In addition to this a data-driven approach has been used for zone division, reference generation and occupancy mapping which helps to enhance adaptability across diverse environments. The proposed model dynamically adjusts the lighting levels by integrating real-time user input, daylight harvesting, and occupancy mapping. The real-time experimental validation demonstrates a 35% reduction in energy consumption compared to traditional methods, with a discounted payback period of 3.68 years. The figure of merit (efficiency/cost) of 33.3%, demonstrating comparability to current state-of-the-art systems and highlighting the potential for substantial cost reductions and energy savings. Future research will address sensor calibration challenges, investigate the effects of extended LED dimming, and explore advancements in scalability and predictive control integration.