Artificial Intelligence and Internet of Things in Green Libraries: Enhancing Energy Efficiency and Resource Management

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Authors: Akash Sharma*, Bhanu Pratap Sharma, Swati Tiwari, Brajesh Tiwari
Cited by: 0

Abstract

Context: Libraries are becoming "green" institutions in response to the global climate imperative, which means they are trying to have less of an impact on the environment. Combining Artificial Intelligence (AI) with the Internet of Things (IoT) is a game-changing strategy to reach this goal, going from simple conservation to smart, data-driven stewardship. Objectives: This study seeks to examine the practical uses of AI and IoT in improving energy efficiency and resource management within library operations. It wants to show how these technologies may improve building systems, make better use of materials, and encourage long-term user interaction. In the end, it wants to provide a model for modern green libraries. Methods: The suggested framework calls for setting up an IoT sensor network to keep an eye on real-time data on occupancy, temperature, lighting, and the status of equipment. AI systems, such as machine learning models, look at this data stream to find trends, automate controls, and give facility managers and workers useful insights. Results: Using this AI-IoT synergy leads to big changes: HVAC and lighting systems that adapt to save energy, better use of space and collections, predictive maintenance that stops waste, and data-driven plans to cut down on water and paper use. The library changes into a place that is responsive and works well. Conclusion: Libraries may take the lead in sustainability by using both AI and IoT together. Not only does this strategy save a lot of money and resources, but it also strengthens the library's function as a community model for environmental stewardship and technological innovation, creating a new standard for how institutions should work.

Fields of Study & Subjects

Library Science and Information Internet of Things and AI Web and Library Services

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
7 February 2026