Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics

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.

Advancing Smart Lighting: A Developmental Approach to Energy Efficiency through Brightness Adjustment Strategies

“…Dimming (Adjustment of Brightness Level) Mobile Application energy use, and the results of this research are significant [19]. The amount of saved energy ranges from 36% to 50% in campus and office environments, as outlined in a review of lighting control technology [17,19].…”

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

“…While the above-discussed research demonstrates promising utility of SVM in SL control, other machine learning algorithms such as decision trees, and XGBoost have also shown significant potential in this field. Somrudee et al [170] used an AI platform to predict dayahead illuminance in a campus setting, applying machine learning models like gradient boosting, random forest, decision tree, and XGBoost to environmental data. XGBoost was the most effective, using five parameters: humidity, temperature, air pressure, illuminance, wind velocity, and timestamp.…”

Testing and Evaluation of Low-Cost Sensors for Developing Open Smart Campus Systems Based on IoT

“…It can generally consist of an application, network/service, and sensor layer [25]. The integration and use of artificial intelligence in this context is becoming increasingly significant [12,16,26].…”

“…In particular, the application of low-cost sensor technology can facilitate the costeffective collection of diverse data and information. This includes climatic data such as temperature, air quality, or humidity [10,11], as well as people, vehicle, and environmental data such as light or noise levels [12,13]. Some of the most common applications for smart building or campus systems include visitor, space, energy, or parking management [4,14,15].…”