Digital twins have become widely adopted in the building sector for operational monitoring and management; however, existing solutions rely heavily on users’ knowledge for decision-making and lack effective human-computer interaction mechanisms, limiting user engagement and actionable feedback. This paper introduces a human-computer interaction-enhanced digital twin visualization framework integrated with large language models (LLMs), designed to optimize occupants’ energy use by providing real-time insights into indoor environmental conditions and enhancing user engagement and actionable understanding through intuitive data exploration. By combining Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and LLMs, the framework enables users to interact with a 3D model of the building, monitor real-time data (e.g., temperature collected from edge devices), and receive environment adjustment suggestions to support decision-making. Compared to the conventional visualization platforms, the framework adds an additional interaction-feedback layer which combines predefined environmental threshold rules with a lightweight LLM to provide real-time prompts and context-aware suggestions to users. The 3D visualization environment is implemented using the Unity engine, enabling immersive interaction with building models and sensor data in real time. A locally hosted, lightweight LLM is embedded into the interface to support offline reasoning and privacy-preserving feedback generation. The system simulates the ability to detect and interpret abnormal building conditions and provide context-relevant responses, offering preliminary decision-support capabilities within the digital twin framework. Experimental results show that this system improves data comprehensibility, responsiveness, and user engagement, encouraging more sustainable occupant behavior. The integration of digital twins with lightweight interactive intelligence highlights a promising step toward adaptive and user-centered building operations. This paper also introduces our real implementation of the edge intelligence supported platform for an energy saving and net zero oriented building in collaboration with industry partners.