Indoor automatic dimming system based on particle swarm optimization

• Main Authors: Hsueh, Yu Fang, Kuan, Chen Xi, Ho, Kun Che, Liu, Yi Hua, Cheng, Yu Shan, Kung, Ching Ju
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Daylight Response Dimming System (DRDS); Energy conservation; Lighting; Machine learning; Machine learning algorithms; Neural networks; Particle Swarm optimization (PSO); Prediction algorithms; Smart lighting
• ISSN: 2163-5145
• DOI: 10.1109/ISIE54533.2024.10595815

Indoor lighting design is to combine natural light sources and artificial light sources in a specific space and makes complete planning to meet the needs of users for lighting, to achieve the effect of creating a certain scenario. Due to the mainstream of LED lamps and the popularity of the Internet of Things, the lighting design market is growing. Compared with traditional lighting equipment, smart lighting can bring users a more comfortable lighting experience. It can be turned on and off through a computer or mobile device, or the lamps can be controlled and dimmed according to the user's preference, making the lighting environment more ergonomic and reducing the power consumption of the lighting system. In this paper, an indoor automatic dimming system based on machine learning and particle swarm optimization (PSO) algorithm is implemented. The experiment is carried out remotely. Through neural network and particle swarm optimization algorithm, the dimming commands of lamps that best fit the current environment are found to meet the illumination requirements of the users. This study focuses on the operation and application of machine learning and PSO algorithm, and verifies the correctness and feasibility of the proposed automatic dimming system by comparing the simulation and measured results with the traditional daylight responsive dimming system (DRDS), and verifies the improvement of dimming accuracy and energy saving performance of the proposed method.