Sirada Chaisawat
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Sirada Chaisawat has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Chanon Tantiwattanapaibul
Sirindhorn International Institute of Technology, Thammasat University, 12120, Thailand
1 shared publication - Leeyakorn Cheewakriengkrai
School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
1 shared publication - Paphana Yiwsiw
Sirindhorn International Institute of Technology, Thammasat University, 12120, Thailand
1 shared publication - Somrudee Deepaisarn
Sirindhorn International Institute of Technology, Thammasat University
1 shared publication - Suphachok Buaruk
Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
1 shared publication - Thanakit Lerttomolsakul
School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
1 shared publication
Latest Publications
- Open Access
Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics
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.
Open AccessAutomated Street Light Adjustment System on Campus with AI-Assisted Data Analytics
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.