Improving Energy Efficiency of Indoor Lighting System Based on Computer Vision

Penglu Chen, Ruying Cai and Yi Tan ()
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Penglu Chen: Shenzhen University
Ruying Cai: Shenzhen University
Yi Tan: Shenzhen University

A chapter in Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, 2022, pp 547-558 from Springer

Abstract: Abstract With the increasing number of large-scale buildings in the city, the energy consumption of the lighting system has also increased significantly, which has become one of the main energy consumption parts of existing buildings. At present, the main energy-saving control method of lighting systems is to use sensor control, but this method brings a substantial increase in initial costs, and it cannot be integrated with other digital systems for multi-source data fusion management. This paper attempts to make full use of the real-time video stream data of the existing building surveillance system to replace perception data of the sensor, thereby proposing a new intelligent lighting control system based on computer vision to reduce energy consumption and initial installation costs. First, high-definition infrared cameras are used to obtain real-time images around the lighting equipment and send them back to the pedestrian detection equipment. Then, the environment perception algorithm is used to perceive the brightness of the environment in the images and the YOLOv4 is used to detect pedestrians. Finally, if a pedestrian is detected, the lighting system adaptively adjusts the brightness of the lighting device according to the perceived ambient brightness, which can minimize energy consumption while meeting lighting standard brightness. The effectiveness of the proposed method was verified by the experiments of surveillance video stream collected for 14 days from a campus building. The results of the experiments mainly include two parts: (1) the accuracy of intelligent decision-making control reaches 95.15%; (2) energy consumption and electricity bills are reduced by about 79%.

Keywords: Adaptive adjustment; Computer vision; Energy efficiency; Intelligent lighting control (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-981-19-5256-2_44

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