Low-carbon and intelligent management of tourist homestays based on stm32 and optical sensing: optimizing lighting control via real-time illumination monitoring

The rapid growth of the tourist homestay sector has significantly contributed to local economies and cultural exchange, yet it has also escalated concerns over energy consumption and carbon emissions. Addressing these sustainability challenges, this study introduces an intelligent, low-carbon management framework for tourist homestays, leveraging the versatility of STM32 microcontroller technology and advanced optical sensing. The primary objective is to minimize the environmental impact of homestay operations without compromising guest comfort or service quality. The proposed system integrates a network of environmental sensors—including optical sensors for real-time illumination monitoring— real-time data acquisition, and adaptive control algorithms to manage HVAC, lighting, and appliance loads efficiently. By capturing ambient light intensity via optical sensors, the system dynamically optimizes indoor lighting, reducing unnecessary energy use while maintaining adequate illumination. A hybrid architecture is adopted, combining on-device data processing with cloud-based analytics to balance responsiveness and scalability. Experimental deployment in a real-world homestay environment was conducted, during which both baseline and intelligent control strategies were compared over a four-week period. Results indicate that the intelligent solution achieved substantial reductions in energy consumption and carbon emissions, particularly during periods of fluctuating occupancy and varying natural light. The system maintained stable indoor environmental conditions and demonstrated strong adaptability to unexpected changes in usage patterns. These findings highlight the effectiveness of embedded, data-driven control—in particular, the integration of optical sensing for lighting optimization—in fostering sustainable operation within decentralized hospitality settings. In conclusion, the STM32-based intelligent management solution provides a practical pathway for low-carbon transformation in the tourism accommodation industry and establishes a foundation for future research on integrating advanced sensing and predictive modeling in smart homestay environments.