AI-driven smart home energy optimization: integrating AI agents with IoT for adaptive decision-making

The growing interest in Digital Twin (DT) Technology represents a significant advancement in academic research and industrial applications. Leveraging advancements in Internet of Things (IoT), sensors, and communication devices, DTs are increasingly utilised across different sectors, notably in the energy domain such as Power Systems and Smart Grids. DT concepts facilitate the creation of virtual models mirroring physical assets, streamlining real-time data management and analysis. Driven by the potential of DTs to revolutionise energy systems, this paper offers a comprehensive review of DT applications in the power sector, specifically within next-generation energy systems like Smart Grids. TThe integration of DT technology with Machine Learning (ML) algorithms is highlighted as a key factor in significantly enhancing the performance and capabilities of these advanced energy systems. In contrast to prior reviews, our study meticulously investigates all of the crucial components of energy systems, including forecasting, anomaly detection, and security, which are fundamental for improving the management of operational grids. In addition, the study examines the seamless incorporation of Renewable Energy into current grids and investigates how DT technology could contribute to Electric Vehicles for increased sustainability and reliability within the Smart Grid framework. This review underlines that DTs significantly enhance the management of real-time data and analysis, consequently improving operational grid management. There are ample opportunities into further research and development to design a more advanced and digital system as compared to conventional power systems. The findings are presented in clear and concise tables, highlighting current limitations, proposing effective solutions, and identifying potential future research directions in academia and industry.

• Examined various battery types, BMS architectures, and modelling techniques. • Explored deterministic, non-deterministic, and hybrid SOC estimation methods. • Discussed SOH estimation and RUL prediction using various methods. • Exploration of digital twin technologies and battery digital twins (BDTs) for enhancing BMS functionality. • Discussion of future research directions in battery state estimation. Accurate state estimation is fundamental for the safety, reliability, and performance of electric vehicle (EV) batteries. This review highlights the critical need for precise evaluation of key battery states such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL). It surveys the existing modelling and estimation techniques used in lithium-ion battery (LIB) systems. It explores parameter identification methods and the challenges associated with real-time monitoring and predictive diagnostics in dynamic EV environments. The paper further investigates the transformative role of digital twin (DT) technologies in addressing these challenges. By integrating battery digital twins technologies(BDTs): Internet of Things (IoT), cloud computing, artificial intelligence (AI), and extended reality (XR), it offers a virtual replica of the battery system. That enables continuous monitoring, predictive maintenance, and performance optimization. The review delves into the architecture, functions, challenges and future perspectives of battery digital twins (BDTs). And it emphasizes their potential to enhance traditional battery management systems (BMS) through intelligent, adaptive control. Through comparative analysis and identification of research gaps, this review provides a roadmap for future advancements in state estimation and digital twin integration.

Migrating the industry and society into digital area have gradually been increased in recent years. Digitalization has made our life more efficient and faster. As a result of digitalization, the Fourth Industrial Revolution, also known as Industry 4.0, was born. The Fourth Industrial Revolution, like previous revolutions, aims to facilitate life and improve the quality of life. Two terms are especially important for digitalization. These terms are Industry 4.0 and Internet of Things (IoT). Industry 4.0 and the IoT focus on the networking and automation of devices both used human being and industry. Digitalization and IoT technologies play an important role in improving energy efficiency. Digital twin (DT) technology is one of the most important key of Industry 4.0 technologies under digitalization. DT represents a digital copy of a physical system or entity. Physical objects and DT models created interact with each other. DT technologies have a wide range from manufacturing to healthcare sectors and from maritime transportation to the heavy industry. DT provides many conveniences for industries to grow with digitalization. Meanwhile, DT draws attention with its development in the academic field. In this study, an overview of DT is presented, a DT roadmap is proposed, some algorithms emerging in the concept of DT for industrial processes are explained and a case study is analyzed. This roadmap shows different methods to be applied in order to make accurate forecasting for the future applications and sustainable manufacturing with DT. As a result, DT concept is thought to attract much attention in the near future.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

A digital twin (DT) system is a virtual system that can provide a comprehensive description of a real physical system. The DT system continuously receives data from physical sensors and user input information and provides information feedback to the physical system. It is an emerging technology that utilizes an advanced Internet of Things (IoT) to connect different objects, which is in high demand in various industries and its research literature is growing exponentially. Traditional physical systems provide data support for the monitoring of physical objects such as buildings through digital modeling techniques, data acquisition tools, human computer interfaces, and building information models (BIM). However, DT can offer much more than data presentation. DT uses the received data to perform operations such as analysis, prediction, and simulation, and finally transmits the analysis results to the physical system as feedback. Compared with other physical systems, DT has the characteristics of bidirectional data exchange and real-time autonomous management. The plant factory control system based on digital twin technology continuously measures the power consumption of electrical equipment through the sensors of the physical system and makes the corresponding virtual color-coded gradient map based on the obtained data. The darker the virtual device is, the more power it currently requires, and just based on the shade of color gives the user a very intuitive idea of the current power usage of the electronic device. There has been extensive research on digital twin technology, but there are few studies on implementing plant factories based on digital twin technology. This paper proposes the idea of combining digital twin technology with plant factories to provide research directions for future smart agriculture. It proves that smart agricultural production with sustainability can also benefit from this idea.

The Digital Twin (DT) technology is considered as a backbone in the Industrial 4.0 revolution as it is playing a vital role in the digitization of various industries. A DT is a virtual representation of a physical entity, thus having the ability to simulate real data generated at physical space to optimize, estimate, control, monitor and forecast states / configurations. Despite enormous benefits, DT technology has several implementation challenges. Although deploying DT on edge or cloud platforms yields a plethora of services, its implementation in both spaces faces certain limitations. These limitations include latency, data communication overload, transmission energy consumption, privacy concerns, and communication inefficiencies. It is evident that these shortcomings could significantly impact real-time monitoring and control. Therefore, when considering whether to deploy DT on the edge or on the cloud, it is necessary to make a trade-off, or alternatively, adopt a hybrid approach. However, it is important to acknowledge that even with a hybrid approach, the aforementioned issues will persist to some extent. To address these challenges, this article introduces two innovative approaches. Local DT (LDT) and Distributed DT (DDT). These deployment strategies are designed to mitigate latency, minimize data communication overload, reduce energy consumption, improve communication efficiency, and strengthen privacy measures.Thus, resulting in environmental and economic sustainability. Consequently, these advancements facilitate superior real-time monitoring and control capabilities. Through the utilization of LDT and DDT methodologies, organizations can harness the full potential of DT technology, thereby maximizing its benefits.

Background and Aims: The effects of digital twin (DT) technology for remission of T2D on cardiovascular risk reduction is unknown. We evaluated the effectiveness of the DT to improve A1c and body weight and ASCVD risk, in patients enrolled to achieve remission of T2DM. DT platform uses AI and Internet of Things, to integrate multi-dimensional data to give precision nutrition and health recommendations via the mobile app and by coaches. Materials and Methods: Data from 208 participants who had been on DT for 1 year were analyzed. Remission was defined as A1C levels less than 6.5% without medication for over 3 months. Outcomes included the change in HbA1c, body weight and change in ASCVD risk scores. The DT uses a machine learning algorithm to integrate clinical and sensor data to predict personal glucose response. Patients were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using the mobile app. Results: The study participants had a mean diabetes duration of 3.7±2.7 years and a mean age of 44±8.5 years. Based on the ADA criteria, at 1 year, 72.6% (n=151/208) continued to be under remission. The mean 10-year ASCVD risk (%) at baseline was 7.9 (±7.8, 95% CI 6.8 to 8.9) which reduced to 3.6 (±4, 95% CI 3.1 to 4.2) at 360 days, p<0.0001. A1c (%) at baseline was 8.9 (±1.8, 95% CI 8.7 to 9.2, minimum 5.5, maximum 16.2) which reduced to 6 (±0.6, 95% CI 5.9 to 6.1, minimum 4.7, maximum 9.2), p<0.0001. Body weight (kg) at baseline was 78 (±14.3, 95% CI 76.1 to 79.9) which reduced to 70.4 (±12.9, 95% CI 68.7 to 72.1), p<0.0001. There was a significant correlation between the reduction in A1c and ASCVD (Pearson r 0.41, 95% CI 0.31 to 0.5, p<0.0001). Similarly, there was a significant correlation between the reduction in body weight and ASCVD (Pearson r 0.25, 95% CI 0.12 to 0.3, <0.0001). At baseline, there were 49% of participants who had low risk score (<5%), 16% as borderline risk (5-7.5%), 27% as intermediate risk (7.5-19.9), 8% as high risk (>20%) that at 1 year shifted to 77% as low risk, 11% as borderline, 11% as intermediate risk and 1% as high-risk scores. Conclusion: The implementation of digital twin (DT) technology, utilizing AI and Internet of Things, demonstrated significant potential in T2DM management in participants enrolled to achieve remission. In our study with 208 participants, DT effectively reduced A1c levels, body weight, and ASCVD risk scores over 360 days. The correlations between reductions in A1c, body weight, and ASCVD underscore the potential of DT as a transformative tool in diabetes and cardiovascular risk management.

With the continuous advancement of computer and Internet technologies, the application of BIM technology in power engineering construction has also been widely developed. The paper proposes the integration of BIM technology and digital twinning technology to realize the informationization and intellectual construction of power engineering. Based on the BIM power engineering project management platform, the paper introduces digital twinning technology, and applies advanced methods such as Internet of Things and big data to give real-time dynamic information to the BIM model, and establishes power engineering digital twins approach from the perspective of the whole process and all participants. In addition, the application value of BIM technology and digital twin technology in power engineering is analyzed. Through the integration of BIM technology and digital twin technology, digital and intelligent management of power engineering construction can be realized, and real-time perception, resource sharing and application capabilities of smart building can be promoted.

Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.

Aim: The integration of Industry 4.0 into healthcare has introduced the Digital Twin (DT), a dynamic virtual replica that mirrors physical biological systems. Traditional dental diagnostics often rely on static two-dimensional methods, which lack predictive capabilities and are prone to errors. This review evaluates the adaptability of DT technology in dentistry, analyzing its potential to enhance diagnostic precision and treatment efficiency compared to conventional modalities. Materials and Methods: A narrative review was conducted using the PICOS framework to assess DT efficacy. A comprehensive literature search spanning 2014 to January 2024 was performed across databases including PubMed, Web of Science, and Scopus. The review synthesized data on DT mechanics, specifically the integration of Artificial Intelligence (AI), Internet of Things (IoT), and Cloud computing within clinical workflows. Results: In Endodontics, DT mitigates the risk of Nickel-Titanium (NiTi) file separation by using real-time thermal and torque monitoring to predict instrument fatigue before failure occurs. In Orthodontics and Orthognathic Surgery, DT fuses facial scans (STL) with CBCT data (DICOM) to create high-fidelity models. These models facilitate “what-if” treatment simulations and the fabrication of patient-specific surgical guides, demonstrating high translational accuracy between virtual planning and postoperative outcomes. Conclusion: DT technology represents a transformative shift from reactive to proactive dental care. By enabling real-time monitoring and predictive simulation, DT enhances surgical precision and personalization, establishing a new standard for data-driven diagnosis and treatment planning.

The inherent nature of public health emergencies often renders them unpredictable and inadequately managed in a timely manner, resulting in significant impacts on lives, property, and social stability. In recent years, the frequency and severity of such emergencies have escalated. As an integration of artificial intelligence, machine learning, deep learning, big data, and the Internet of Things, digital twin technology has been increasingly adopted across diverse fields, including industrial production, urban planning, and healthcare. This study aimed to investigate recent advancements in digital twin technology, particularly in the fields of medicine and urban construction, with a focus on its potential to mitigate the impacts of public health crises. By employing the PPRR framework, this study further explored the prospects for constructing and applying digital twin technology within the public health management system. Taking perceived fidelity (SF), model plasticity (MP), decision coupling (DC), and governance credibility (GC) as the core constructs, we discussed the operability of unified modeling and quantitative evaluation of each stage of digital twin implementation. This approach comprehensively explained all four phases of emergency management: prevention, preparedness, response, and recovery. Through enabling the digitalization, intelligence, and visualization of the entire health emergency management process, digital twin technology offers holistic solutions throughout the life cycle of public health emergencies. These include preventive measures, real-time detection, early warning systems, and intelligent emergency response management. Such innovations have the potential to significantly enhance management efficiency and effectiveness, bolster coordinated responses to public health crises, and improve the overall resilience of public health systems.

The adoption of the Internet of Things (IoT) technology in the Industry 4.0 framework is increasingly on the rise. Industrial organizations, including those operating in the aerospace sector, are interested in exploiting sensed data in a virtual environment. The Digital Twin (DT) emerges as the virtual counterpart of a real-world system for analysing, monitoring and simulating its behaviour and supporting decisions process. While the concepts of both IoT and DT are widely discussed by the academia, empirical demonstrations on their synergic integration seem to lack in the literature. Considering the HexaSFDT framework, this paper aims to narrow this gap and to discuss a practical case study of an aerospace company. Moreover, it focuses on contextualizing the framework in a process-oriented DT. Following a collaborative approach, several interviews have been conducted with both operational and strategic profiles of the aerospace company. Data flows sensed from the industrial scenario have been analysed to monitor the behaviour of the critical materials management process. Accordingly, the HexaSFDT framework has been customized for fitting the process of the case study. The paper provides a double implication. It discusses the adoption of IoT and DT technologies by presenting an empirical research. Also, through an industrial case study, it supports practitioners, and in particular aerospace companies, in understanding how these technologies can be leveraged to assist people in decision-making.

The Halal supply chain, which encompasses the sectors of food, medicines, cosmetics, has enormous challenges to comply with Islamic rules, maintain product traceability and combating fraud. Given the increasing global demand for Halal products, the demand for technology innovations to enhance supply chain transparency and ensure Halal compliance is growing. This paper explores the application of Digital Twin (DT) technology in Halal supply chain management, highlighting its ability to address these challenges. The main research question of this paper is as follows: How can Digital Twin technology facilitate transparency, traceability, and compliance in the Halal supply chains? The current study employs a qualitative methodology through a review of the extant literature and reviews of industrial case studies. The study reveals that the Digital Twin technology promises to considerably improve the operational excellence of Halal supply chains through real-time Halal status monitoring ensuring that products comply with religious and ethical requirements along the supply chain. The main applications include tracking Halal certification, preventing cross-contamination, and enhancing transparency for certification bodies, manufacturers, and consumers. The results indicate that Digital Twin technology has the potential to transform Halal supply chains by reducing fraud, improving operational efficiencies, and building consumer trust. As a comprehensive Halal compliance solution, Digital Twin can enable a more transparent and trustworthy Halal certification process on a global scale. The study extends the knowledge body on technology innovations in supply chain management especially with religious and ethical product certifications and sets the stage for future research in the area.

Digital twin technology is the talking point of academia and industry. When defining a digital twin, new modeling paradigms and computational methods are needed. Developments in the Internet of Things and advanced simulation and modeling techniques have provided new strategies for building complex digital twins. The digital twin is a virtual entity representation of the physical entity, such as a product or a process. This virtual entity is a collection of computationally complex knowledge models that embeds all the information of the physical world. To that end, this article proposes a graph-based representation of the virtual entity. This graph-based representation provides a method to visualize the parameter and their interactions across different modeling domains. However, the virtual entity graph becomes inherently complex with multiple parameters for a complex multidimensional physical system. This research contributes to the body of knowledge with a novel graph-based model reduction method that simplifies the virtual entity analysis. The graph-based model reduction method uses graph structure preserving algorithms and Dempster–Shaffer Theory to provide the importance of the parameters in the virtual entity. The graph-based model reduction method is validated by benchmarking it against the random forest regressor method. The method is tested on a turbo compressor case study. In the future, a method such as graph-based model reduction needs to be integrated with digital twin frameworks to provide digital services by the twin efficiently.