Smart Streetlight Energy Saving System Based on mmWave Radar
DOI:
https://doi.org/10.46604/aiti.2024.13175Keywords:
deep learning, LSTM, mmWave radar, point cloud data, DBSCANAbstract
Streetlights serve as fundamental infrastructure to meet the lighting needs of people on every road. However, their extensive deployment often results in unnecessary energy waste, with many streetlights maintaining high brightness despite minimal usage during the night. This study aims to develop a smart energy-efficient streetlight system that automatically adjusts lighting levels based on the absence of vehicles and pedestrians, detected after a 3-minute countdown. Specifically, the study utilizes mmWave radar to collect point cloud data, which undergoes denoising through Doppler, DBSCAN, and XYZ techniques. Additionally, the mmWave radar assists in training an LSTM model to identify pedestrian pathways. The implementation of the proposed system significantly reduces energy consumption and annual costs by automatically dimming or turning off streetlights in areas with minimal pedestrian activity during nighttime.
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Copyright (c) 2024 Yu-Kai Lin, Jhih-Ci Li, Kai-Li Wang, Yu-Ping Liao
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