Connected smart elevator systems for smart power and time saving

• Published in: Scientific reports 2024-08, Vol.14 (1), p.19330-13
• Main Authors: Rashed, Ahmed Nabih Zaki, Yarrarapu, Manasa, Prabu, Ramachandran Thandaiah, Raj Antony, Gnana Sagaya, Edeswaran, Logashanmugam, Kumar, E. Santosh, Aswitha, K., Snehith, N., Ahammad, Shaik Hasane
• Format: Article
• Language: English
• Subjects: 639/166/4073; 639/166/987; Access control; Accuracy; Adaptability; Algorithms; Artificial intelligence; Building management systems; Decision making; Deep learning; Elevators & escalators; Energy; Energy efficiency; Energy management; Environmental impact; Feedback; Floor prediction; Humanities and Social Sciences; Image processing; Information processing; multidisciplinary; Pattern recognition; Power saving; R&D; Renewable energy sources; Research & development; Science; Science (multidisciplinary); Smart elevator; Sustainable practices; Time-saving
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-69173-1

Smart elevators provide substantial promise for time and energy management applications by utilizing cutting edge artificial intelligence and image processing technology. In order to improve operating efficiency, this project designs an elevator system that uses the YOLO model for object detection. Compared to traditional methods, our results show a 15% improvement in wait times and a 20% reduction in energy use. Due to the elevator’s increased accuracy and dependability, users’ qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. Due to the elevator’s increased accuracy and dependability, users’ qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. The successful integration of artificial intelligence (AI) and image processing technologies in elevator systems presents a promising foundation for future research and development. Further advancements in object detection algorithms, such as refining YOLO models for even higher accuracy and real-time adaptability, hold potential to enhance operational efficiency. Integrating smart elevators more deeply into IoT networks and building management systems could enable comprehensive energy management strategies and real-time decision-making. Predictive maintenance models tailored to elevator components could minimize downtime and optimize service schedules, enhancing overall reliability. Additionally, exploring adaptive user interfaces and personalized scheduling algorithms could further elevate user satisfaction by tailoring elevator interactions to individual preferences. Sustainable practices, including energy-efficient designs and integration of renewable energy sources, represent crucial avenues for reducing environmental impact. Addressing security concerns through advanced encryption and access control mechanisms will be essential for safeguarding sensitive data in smart elevator systems.