## 1 Introduction
Creating a personalized DT model for each user can be both time-consuming and memory-intensive, as each DT requires dedicated storage and processing resources to maintain an accurate and responsive virtual replica [11]. Moreover, designing and creating these models also demands considerable effort, especially when using custom techniques like 3D scanning to capture unique environments, which further complicates scaling DT applications across many users. This personalized modeling approach can lead to significant resource consumption, particularly when managing high volumes of IoT data, making it a potential bottleneck in effectively scaling DT applications. Additionally, ensuring that DTs are continuously updated in response to real-time IoT data requires efficient data synchronization, which can strain computational resources if not optimized [12]. Addressing these challenges is crucial for making IoT-integrated DT solutions feasible, efficient, and user-friendly in real-world settings. Overall, HEMSs are increasingly adopting IoT technologies to enable smarter energy management. However, traditional HEMSs face several limitations, including lack of real-time feedback and actionable insights to help users make informed decisions; limited scalability in adapting to diverse residential setups and IoT ecosystems; and challengesin providing predictive capabilities for energy optimization and occupancy trends. These limitations hinder the ability of HEMSs to fully support user engagement, energy efficiency, and sustainability practices. DT technology seems to be a promising solution. While DT technology has shown promise in industrial and commercial applications, its integration with IoT for residential HEMS remains largely unexplored. Existing studies focus predominantly on either DT frameworks for large-scale systems or basic IoT implementations for energy management, without addressing the potential of combining these technologies to enhance scalability, usability, and efficiency in residential settings. This research aims to fill this gap by proposing a novel DT-enabled HEMS framework that bridges the physical and digital domains, empowering users with real-time insights and predictive analytics for sustainable energy management. The primary scientific goal is to address challenges related to deployment complexity, scalability, and resource efficiency by introducing a standardized parametric model for Digital Twins. This framework emphasizes seamless integration, real-time monitoring, and intuitive visualization to empower users with actionable insights into their energy consumption patterns. By utilizing a pre-defined Digital Twin framework, the solution bypasses the need for extensive customization, such as complex 3D scanning or unique modeling, thereby enabling efficient scalability across multiple users while maintaining resource efficiency. This setup facilitates seamless integration between IoT and Digital Twin systems, allowing users to benefit from the insights and advantages of a Digital Twin without the complexity and effort typically associated with creating and maintaining personalized models. Consequently, this approach makes advanced home energy management more accessible, user-friendly, and effective, even for large-scale deployments. The main novelties of this paper area as follows:  
* Non-intrusive, user-friendly plug-and-play IoT setup. The proposed framework simplifies the installation process by leveraging pre-configured IoT devices and standardized interfaces. This approach was empirically validated during the case study implementation, where IoT devices were installed with minimal modifications to existing infrastructure, as detailed in Section 5. * Pre-designed, scalable Digital Twin model. The proposed standardized DT framework significantly reduces computational demands by avoiding resource-intensive custom modeling. This claim is substantiated by the results in Section 5.4, which demonstrate a 98% reduction in memory usage compared to traditional approaches.