A BIM-Based Digital Twin Framework for Indoor Environmental Quality Monitoring and Energy Optimization in Building Operations

This paper proposes a building information modeling (BIM)-sensor-integrated digital twin framework to enable real-time indoor environmental quality (IEQ) assessment and occupancy-adaptive energy optimization. A multi-layer architecture is adopted to connect physical sensing devices, data management infrastructure, and a BIM-based virtual model through a sensor-space-component mapping mechanism and a Dynamo-connected bidirectional data interface. Based on national standards, a compliance-based IEQ assessment scheme is developed to evaluate thermal comfort, air quality, lighting, and acoustic performance. Operational energy consumption and carbon emissions are quantified using energy use intensity and grid emission factors. A case study conducted in a university office building is used to demonstrate the feasibility of the proposed framework. Real-time environmental parameters are synchronized with BIM and automatically classified into compliant, marginal, or non-compliant states using color-coded visualization. Human presence perception and rule-based operational settings enable occupancy-adaptive control of air-conditioning and lighting systems. The results demonstrate that energy usage can be decreased from 3.29 to 2.54 kWh/m2/day, resulting in approximately 23% energy savings and a daily carbon emission reduction of 12.04 kgCO2e while maintaining a compliant indoor environment. The proposed framework provides a scalable and practical solution for integrating BIM and digital twin technologies in building operation and asset management.