## 1 Introduction  
Approximately 40% of worldwide energy use and 36% of worldwide emissions are generated by buildings and cities, which renders them significant contributors of both global energy consumption and CO\({}_{2}\) emissions [1]. This significant environmental footprint has underscored the need for innovative approaches and cutting-edge technologies in managing and optimizing energy consumption within the residential sector. As a result, various digital technologies like the Internet of Things (IoT) and Digital Twins (DIs) have developed as promising solutions to address the challenges associated with energy management and sustainability while regulating thermal comfort in home environments [2]. These technologies will be more effective when integrated in real-life and real-time scenarios, allowing for immediate data-driven adjustments and optimizations that align with user preferences and environmental goals. Overall, the seamless, real-time integration of IoT and DTs into Home Energy Management Systems (HEMSs) is critical for maximizing energy efficiency, improving user comfort, and supporting the sustainability goals of modern smart cities. The IoT allows seamless connectivity and real-time data collection from various smart devices, providing granular insights into the energy usage of specific appliances and overall household consumption [3]. Moreover, the continuous data streams generated by IoT networks are pivotal for applications in HEMSs, such as error analysis and prescriptive maintenance [4]. By analyzing these data flows and created patterns within HEMSs, specific applications can predict and address potential system faults or inefficiencies, ensuring uninterrupted operation and reducing energy waste, while extending the lifespan of household devices. In an even broader sense, IoT data can also enhance security within the IoT ecosystem itself through self-healing techniques that autonomously detect and resolve vulnerabilities [5]. This capability to ensure reliable, secure, and efficient operation underlines the critical role of IoT data in modern HEMS applications. Overall, these capabilities make IoT data indispensable for advancing intelligent, resilient, and sustainable HEMSs that adapt dynamically to both user needs and environmental demands. In parallel, DT technology offers a virtual replica of physical assets while enabling dynamic simulation, predictive analysis, and optimization of systems such as HEMSs. By integrating IoT within these DTs, it is possible to create an advanced, real-time model of the residential environment that can monitor, control, and optimize energy use and other parameters autonomously [6; 7; 8]. Visualization of IoT data within DT applications plays a crucial role in enhancing user-centered experiences while providing clear, interactive representations of energy usage and device performance. Such visualizations help users better understand their energy consumption patterns and enable them to make informed decisions about optimizing household energy. Furthermore, visualization tools aid in quickly identifying issues within the IoT network per se, making it easier for users to detect and address problems, such as connectivity issues or device malfunctions, through intuitive graphical insights. This enhanced transparency fosters user engagement and empowers individuals to actively participate in managing their home energy environment more effectively [9]. Nonetheless, one of the primary challenges in integrating IoT with DTs for real-life applications is ensuring that IoT setups are non-intrusive and quick to deploy [10]. Especially in home environments, IoT devices must be easy to install without requiring significant modifications to existing infrastructure, as intrusive setups can discourage adoption and increase complexity while adding inconvenience to the tenants. Furthermore, achieving real-time visualization outcomes from DTs presents scalability and replicability issues [11].