Intelligent dimming control and energy consumption monitoring system of tunnel lighting

Abstract

To achieve energy saving and safe driving in a tunnel, this work proposes a tunnel intelligent dimming control and energy consumption monitoring system. Firstly, a dynamic predicted tunnel traffic volume is established to optimize the timeliness of the system tunnel traffic volume. Secondly, a backpropagation neural network-based dimming control model is constructed, in which the luminance outside the tunnel, traffic volume and vehicle speed serve as the input, and the luminance inside the tunnel is the output. This model is then combined with the established integrated closed-loop control model of the tunnel internal and external luminance to achieve continuous dimming control of the tunnel. Finally, an energy consumption monitoring system is designed with an energy monitoring unit. The designed system was implemented and tested for 63 days in the Yongtaiwen–Chayuanli (China) tunnel. Experiment results show that the designed system can automatically control the luminance of the tunnel lighting according to the luminance measured outside the tunnel, traffic volume and vehicle speed, thus achieving continuous dimming control. This significantly reduced the power consumption (by approximately 65%) whilst ensuring the safety of tunnel traffic.