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Smart city article details

Title	Automated Street Light Adjustment System On Campus With Ai-Assisted Data Analytics
ID_Doc	11260
Authors	Deepaisarn S.; Yiwsiw P.; Chaisawat S.; Lerttomolsakul T.; Cheewakriengkrai L.; Tantiwattanapaibul C.; Buaruk S.; Sornlertlamvanich V.
Year	2023
Published	Sensors, 23, 4
DOI	http://dx.doi.org/10.3390/s23041853
Abstract	The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features. © 2023 by the authors.
Author Keywords	artificial intelligence; automated system; data analytics; environmental sensors; Internet of Things (IoT); smart city; smart street lighting; web application

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