## 3 Digital Twin for HEMS
### Defining the Digital Twin Framework in HEMSs  
A DT serves as a dynamic digital replica of physical systems, offering real-time monitoring, analysis, and control. Within HEMSs, DTs enable the integration of IoT data with spatial and environmental models, bridging the physical and digital domains. This transformation is crucial for optimizing energy management and providing actionable insights into household energy use. The DT framework integrates IoT-driven data collection with virtual modeling to dynamically represent and manage the home environment, enhancing system efficiency and user engagement. Figure 1 depicts the transition from a classical HEMS architecture to an extended system integrating DT capabilities. The classical HEMS structure consists of three primary components:  
* Physical Layer: Includes the actual rooms, IoT sensors, and appliances.
* Data Collection and Processing: Handles data streams, middleware communication, and database storage for managing and analyzing energy usage.
* Applications: Offers basic monitoring and control through user-facing applications.  
The proposed system builds upon this by introducing a DT Layer, which mirrors the physical environment in a virtual space. This DT Layer enables real-time visualization, advanced simulations, and enhanced interaction. Digital rooms, IoT sensors, and appliances within the DT Layer are synchronized with their physical counterparts, offering users actionable insights and predictive analytics. This architecture bridges the gap between monitoring and actionable control, significantly enhancing the scalability, usability, and efficiency of HEMS.  
In contrast to traditional HEMS architectures, which rely on static configurations and limited real-time capabilities, the integration of Digital Twins introduces dynamic simulation and predictive analysis. This enables real-time adjustments and optimization based on live IoT data streams, reducing latency and improving user engagement. The proposed DT framework uses standardized models, ensuring scalability and efficient resource usage while allowing for customized interactions with the home environment. By bridging IoT  
Figure 1: Extended HEMS architecture incorporating DT Layer.  
data with spatial models, the system provides granular insights into energy consumption and thermal conditions, leading to enhanced sustainability and user comfort. This research focuses on transforming traditional HEMSs into dynamic, user-centric systems through the integration of IoT and Digital Twins. By enabling real-time simulation, predictive analytics, and enhanced user interactions, this study aims to address scalability and usability challenges in energy management.  
Specifically, Figure 2 depicts the transition from a classical HEMS architecture to an extended system with integrated DT capabilities. The diagram compares the two architectures, emphasizing the added value of the DT Layer. A Classical HEMS is limited to the Physical Layer, Data Collection and Processing, and Applications. It primarily focuses on IoT-based data collection and basic user interactions, offering limited insights and lacking predictive capabilities. Meanwhile, in the extended HEMS with DT, the DT Layer transforms the system into a dynamic, interactive platform. This layer provides real-time visualization of household energy use, advanced simulations, and predictive analytics. By mirroring physical rooms, IoT sensors, and appliances in a virtual environment, the DT Layer bridges the gap between monitoring and actionable control, empowering users with enhanced engagement and decision-making tools.