Adaptive Control of Streetlights Using Deep Learning for the Optimization of Energy Consumption during Late Hours

Abstract

This paper presents an adaptive control scheme for streetlights by optimizing the energy consumed using deep learning during late hours at night. A city’s infrastructure is not complete without a proper lightening system for streets and roads. The streetlight systems often consume up to 50% of the electricity utilized by the city. Due to this reason, it has a huge financial impact on the electricity generation of the city. Furthermore, continuous luminosity of the streetlights contributes to the environmental pollution as well. Economists and ecologists around the globe are working hard to reduce the global impact of continued utilization of streetlights at night. In regard to a developing country which is already struggling to produce enough electrical energy to fulfill its industry requirements, proposing a system to lessen the load of the energy utilization by the streetlights should be beneficial. Therefore, an innovative and novel energy efficient streetlight control system is presented based on embedded video processing. The proposed system uses deep learning for the optimization of energy consumption during the later hours. Conventional street lighting systems consume enormous amounts of electricity, even when there is no need for the light, i.e., during off-peak hours and late at night when there is reduced or no traffic on the roads. The proposed system was designed, and implemented and tested at two different sites in Karachi, Pakistan. The system is capable of detecting vehicles and pedestrians and is able to track their movements. The YOLOv5 deep-learning based algorithm was trained according to the local requirements and implemented on the NVIDIA standalone multimedia processing unit “Jetson Nano”. The output of the YOLOv5 is then used to control the intensity of the streetlights through intensity control unit. This intensity control unit also considers the area, object and time for the switching of streetlights. The experimental results are promising, and the proposed system significantly reduces the energy consumption of streetlights.

Keywords

• streetlights
• energy consumption
• deep learning
• power saving
• adaptive control
• image processing