## 5 Case Study: Practical Implementation of DT for HEMS
### Limitations, Challenges, and Extensions  
The proposed framework offers significant advancements in enhancing HEMSs through the integration of DT technology. However, certain limitations and challenges remain to be addressed. Scalability is a key consideration, as the framework has been evaluated within specific IoT configurations, and its application across diverse and larger-scale residential setups with more complex IoT ecosystems requires further validation. Additionally, while the framework demonstrates robust predictive capabilities, ensuring consistent accuracy under dynamic and unpredictable user behaviors in real-world environments presents an important area for further exploration. User engagement and usability assessments have primarily been based on predefined use cases. Broader validation across varied demographics and user preferences is essential to refine the framework's design and functionality. Another important consideration is the adaptability and generalizability of the DTs. While effective for specific scenarios, ensuring these models can accommodate diverse home layouts and IoT device capabilities remains a challenge. In terms of implementation, deploying the framework across heterogeneous residential infrastructures and ensuring seamless compatibility with varying IoT configurations presents practical complexities. Additionally, the lack of standardized DT models tailored for residential environments highlights the need for collaborative efforts to establish universal standards. Data privacy and security also emerge as critical concerns, requiring robust measures to safeguard user information and foster trust. Lastly, integrating advanced technologies such as artificial intelligence, machine learning, and renewable energy systems introduces new opportunities but also challenges in ensuring interoperability and optimizing system performance. Despite these limitations, the framework establishes a strong foundation for advancing residential energy management, providing clear pathways for addressing these challenges and guiding future research. Moreover, while the current study validates the proposed framework using a two-bedroom residential layout, this choice was intentional to demonstrate the feasibility and benefits of utilizing a predefined framework. Future work could explore adapting this framework to larger and more complex environments, such as multi-storied buildings. Future iterations should also consider incorporating predefined room types and leveraging modular grid-based designs to dynamically construct more complex structures. This enhancement would significantly improve the framework's adaptability and scalability, broadening its applicability while maintaining its inherent advantages. The proposed framework was designed with scalability and flexibility in mind, allowing for future extensions to address additional factors and scenarios. For instance, the framework can be augmented to include mechanisms for handling network disconnections,sensor failures, and high data loads to improve robustness in real-world applications. Moreover, while the current implementation focuses on thermal comfort using PMV indices, it can be extended to incorporate additional environmental factors such as noise levels and air quality. These enhancements would provide a more holistic approach to home energy management, ensuring a comprehensive understanding of occupant comfort and environmental sustainability. By building on the existing modular and adaptable design, such extensions can be seamlessly integrated without compromising the framework's simplicity and usability. The most critical achievement of the proposed framework is the seamless integration of IoT devices with the Digital Twin environment, which forms the foundation for various advanced visualizations. By leveraging real-time data collected from IoT sensors, the framework enables a wide range of interactive and informative visual representations, including energy consumption patterns, thermal comfort levels, and occupancy trends.