## 2 Literature Review
### Digital Twins in Residential Energy Management  
DTs in residential energy applications represent a transformative approach to energy management, enabling enhanced efficiency and sustainability. These models focus on the complexities of modern systems, extending the integration of IoT schemas for home energy management. The creation of dynamic, virtual replicas of physical assets enables real-time monitoring, predictive maintenance, and data-driven decision-making, leading to optimized energy consumption and improved system efficiency ([PERSON] et al., 2020). Recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) further enhance DT capabilities, allowing for intelligent decision-making that adjusts to user behaviors and external factors ([PERSON] et al., 2020). This section explores the role of DTs in residential energy management systems, focusing on their potential to create energy-efficient, self-regulating home environments that are aligned with the goals of smart and sustainable living. [PERSON] et al. (2020) propose a multi-layered DT framework that integrates both household-specific Home Digital Twins (HDTs) and a centralized Energy Digital Twin (EDT) to enhance energy management systems. In this framework, each HDT reflects household energy use patterns and user preferences, enabling local appliance scheduling optimization through edge-deployed reinforcement learning (RL). The EDT, in turn, aggregates energy demand data across households, allowing for dynamic adjustment of dual tariff rates based on real-time demand trends. Complementing this approach, [PERSON] et al. (2020) introduce a DT framework that leverages Building Information Modeling (BIM), IoT, and ML services to optimize energy usage and sustainability within various case studies, including smart grids, microgrids, and district heating networks. Their findings highlight the benefits and challenges of DT applications in HEMSs, noting key limitations in scalability, data quality, and stakeholder engagement. [PERSON] et al. (2020) extend the application of AI-driven DTs in HEMSs to optimize hybrid renewable hydrogen systems for self-sufficiency and efficiency. The effectiveness in real-time energy optimization is evident by the implementation in a housing project in Spain but the complexity of the system creates challenges in adaptability and scalability, while the centralized processing and demanding ML algorithms raise concerns regarding security and computational demands on larger scales. [PERSON] et al. (2020) focus on user empowerment by introducing an ML-powered digital twin for smart homes. The system uses an unsupervised machine learning approach to simulate the impact of different appliance modes on energy consumption. Through a user-friendly web-based interface, users can create trigger-action rules and receive recommendations for optimizing energy consumption. Table 1 presents a detailed comparison of key features and methods across various related works in the domain of IoT-enabled DT systems for energy management. The comparison highlights significant gaps in prior research and underscores the comprehensive capabilities of this paper. The features compared were selected to address the key challenges in deploying IoT-enabled DT systems for HEM. These include scalability, usability, and resource efficiency, critical factors for real-world adoption. 'Non-intrusive IoT Setup' ensures minimal structural modifications for seamless integration in diverse environments, while 'Pre-designed DT Framework' highlights the scalability and efficiency of standardized models, eliminating the need for resource-intensive customizations. The term 'Non-intrusive IoT Setup' focuses on minimizing the impact on existing infrastructure, such as avoiding structural modifications or rewiring, whereas 'Ease of Installation' highlights the simplicity and user-friendliness of the process, such as plug-and-play functionality. While related, these terms address different aspects of deployment.