Industrial Automation with Decision and Prediction

This paper presents an innovative approach to automated lighting control that integrates manual, mobile, and intelligent machine-driven operations. The principal aim is to optimize energy economy and user comfort through dynamic lighting control through data collection. To make well-informed decisions about lighting requirements, the system collects user activity data pertaining to time and ambient light levels. The suggested system makes use of sensors to track ambient darkness and identify when a user enters a room. It enables both remote control using a mobile application and manual control using traditional switches. In addition, it analyzes trends in user behavior and ambient variables to autonomously control lighting. The lights are automatically turned on by the system when it detects low light levels and the presence of a user, guaranteeing ideal illumination. Additionally, the system uses machine learning algorithms to forecast lighting needs, which reduces wasteful energy use. In addition to offering a seamless user experience, this adaptive lighting system helps promote sustainable energy practices by using less electricity in areas that are either adequately lighted by natural light or vacant