Smart Data-Driven Green Libraries for Sustainable Urban Communities

The evolution of sustainable urban ecosystems necessitates the fundamental reimagining of public and educational infrastructures as intelligent, energy-conserving, and ecologically attuned entities. This research articulates a comprehensive conceptual and technical framework for Smart Data-Driven Green Libraries (SDDGLS), which leverage an integrated network of Internet of Things (IoT) sensors, artificial intelligence (AI), and predictive data analytics to monitor, forecast, and optimize resource utilization. By synthesizing real-time environmental parameters, including air quality, thermal conditions, illuminance, and human occupancy, the proposed system autonomously modulates lighting, climate control, and energy distribution. Empirical simulations and analytical assessments demonstrate that AI-powered predictive management can curtail energy consumption by up to ۲۸%, all while ensuring superior occupant comfort and a marked reduction in CO۲ emissions. Transcending mere operational efficacy, these intelligent facilities function as dynamic nuclei for environmental stewardship, cultivating sustainability awareness and literacy within the community. The findings underscore the transformative potential of data-driven architectural paradigms, positioning libraries as proactive agents within the urban fabric, directly advancing the goals of enhanced air quality, diminished carbon footprints, and the development of resilient urban futures.

کلیدواژه ها:

Green Libraries ، IoT ، Data Analytics ، Smart Cities ، Sustainability