Model Predictive Control for Adaptive Energy-Efficient Street Lighting: A Comprehensive Framework and Case Study

This study tackles the challenge of optimizing light emitting diode (LED) street lighting to enhance energy efficiency without compromising safety and comfort in dynamic urban environments. Conventional adaptive lighting systems often overlook real-time microlocation variations, such as fluctuating traffic, pedestrian activity, and weather conditions, resulting in excessive energy consumption or inadequate illumination. To address this limitation, we develop and validate a simulation environment that integrates a model predictive control (MPC) algorithm designed to predict and dynamically adjust lighting intensity using high-resolution environmental data. Our primary objectives are to establish a robust MPC framework for real-time streetlight optimization under varying conditions, to validate its performance through simulation and real-world deployment, and to quantify its energy savings. Theoretically, this work contributes to a novel MPC-based control strategy for predictive modeling of dynamic urban variables, allowing proactive lighting adjustments. Practically, we demonstrate the system’s feasibility through a case study in Sisak, Croatia, where it achieved 31.94% energy savings while maintaining illumination quality. This research advances smart city infrastructure by offering a scalable and tunable control framework that harmonizes energy efficiency with public safety.