Track: Energy and Resource Efficiency

Abstract

The lighting system is an essential component of any industrial city's infrastructure for drivers' safety to prevent accidents and facilitate ease of guidance as an accessibility manner to the city. However, street lighting accounts for a significant portion of the overall energy consumption in industrial cities that does not reflect the actual vehicles passing the street or the intended purpose of the lights. In this paper, we propose a smart sensing model to reduce street lights' electrical consumption based on demand using an Internet of Things (IoT) camera with a License Plate Recognition (LPR) function that detects vehicles. If a vehicle is detected using LPR, the street lights will be turned on for 15 seconds to allow the vehicle to pass the street, then turn off part of the street lights if no other vehicles are detected as an ideal status of the lights. Our results concluded that the proposed smart sensing model accomplishes its objective by achieving the highest street light electrical reduction in one hour across five days, which is 89.17 % for half of the lights controlled using IoT. Moreover, the highest average reduction for all five days with a period of 12 hours is 74.09% compared to regular status.