## 5 Case Study: Practical Implementation of DT for HEMS
### Evaluation of Key Performance Metrics  
The scientific aim of this research was validated through a set of carefully defined Key Performance Indicators (KPIs) that measure the framework's effectiveness in achieving its objectives. These KPIs included real-time system performance, predictive accuracy, scalability, usability, and energy efficiency. Specifically, the real-time system performance  
Figure 14: Thermal comfort in DT. Figure 12: Thermal comfort conditions. Figure 13: PMV scale color map. was assessed by measuring data synchronization latency, ensuring seamless integration of IoT data into the Digital Twin environment. Scalability was demonstrated through system response times under varying loads, maintaining robust performance even with increased IoT device connections. Usability was estimated through user experience based on ease of navigation and actionable insights. In Table 4, the KPIs for evaluating the suggested framework for IoT-integrated HEMSs are outlined. The table highlights essential aspects such as the setup time required for plug-and-play IoT integration and DT framework installation, emphasizing non-intrusiveness with minimal structural modifications. Real-time monitoring is measured by latency and memory efficiency, ensuring the synchronization of IoT data within the DT environment. Metrics for system performance include the ability to minimize storage requirements by using reusable 3D models and maintaining RAM utilization during high-load operations. Additionally, scalability and seamless integration are evaluated through metrics like data reflection time, model build time, and resource efficiency, ensuring the system's readiness for real-world applications. This comprehensive approach ensures the proposed framework not only meets technical and operational standards but also aligns with user expectations for non-intrusiveness and intuitive interaction. The integration of IoT and DT technologies, evaluated through these performance indicators, demonstrates the potential to optimize energy efficiency, enhance real-time monitoring, and provide actionable insights for sustainable energy management in modern households. As such, the study contributes significantly to the \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{1}{c}{**Metric**} & \multicolumn{1}{c}{**Proposed Framework**} & \multicolumn{1}{c}{**Traditional IoT Systems ([13; 14; 15; 47])**} \\ \hline Setup Time for IoT Deployment & Average 15 min per user & Average 30–45 min per user \\ \hline Intrusiveness in Installation & \textless{}5\% requiring structural modifications & 20–30\% requiring structural modifications, including rewiring and new fixtures \\ \hline Time to Build DT Household Household & 1 h total (standardized model) & 200–300 h total (4–6 h per house for customized modeling) \\ \hline Overall Time for 50 Users (IoT + DT) & \textless{}13.5 h & \textless{}225–275 h \\ \hline Setup Time for IoT Deployment & Average 15 min per user & Average 30–45 min per user \\ \hline Memory Usage for DT & \textless{}500 MB (single model for all users) & \textless{}25 GB total (500 MB per model \(\times\)50 models) \\ \hline Scalability & Highly scalable with standardized DT models & Limited scalability due to custom modeling and extensive resource requirements \\ \hline Interoperability & Seamless integration with diverse IoT protocols (Zigbee, Wi-Fi, Z-Wave) & Often protocol-specific, requiring additional middleware for compatibility \\ \hline User Experience & Intuitive 3D visualizations, satisfaction score \(\geq\) 4.7/5 & Often lacking in real-time visualization, satisfaction score \textless{}3.5–4/5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Key performance metrics for evaluating IoT-integrated DT framework in HEMS. advancement of scalable and user-friendly solutions for smart energy management, paving the way for future research and practical implementations in the HEMS domain.