2025· International Journal of Modern Research in Science & Engineering· Vol 8, pp. 01-13· 0 citations
TL;DR
A Hybrid Digital Twin–IoT Framework that integrates real-time IoT sensing, cloud-edge computing, machine learning, and Digital Twin simulation for intelligent energy optimization for scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making is proposed.
Abstract
Rapid urbanization, increasing energy demand, and stringent environmental regulations have accelerated the adoption of smart building management systems. However, conventional Building Energy Management Systems (BEMS) rely on static control strategies and limited predictive capabilities, resulting in inefficient energy utilization. This paper proposes a Hybrid Digital Twin–IoT Framework that integrates real-time IoT sensing, cloud-edge computing, machine learning, and Digital Twin simulation for intelligent energy optimization. Environmental and operational data from sensors, including temperature, humidity, occupancy, lighting, CO₂ concentration, and energy meters, are processed at the edge and synchronized with a cloud-based Digital Twin for real-time monitoring and predictive analytics. The framework forecasts energy demand, occupant behavior, and HVAC performance while optimizing building operations to reduce energy consumption and operational costs without compromising occupant comfort. Continuous interaction between the physical building and its virtual counterpart enables predictive maintenance, adaptive control, intelligent fault diagnosis, and renewable energy integration, delivering scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making.
The proposed intelligent energy harvesting framework provides an efficient and sustainable power solution for next-generation Internet of Things (IoT) devices by integrating multi-source ambient energy harvesting, Maximum Power Point Tracking (MPPT), hybrid energy storage, and machine learning-based energy management. The framework effectively harvests energy from solar, thermal, radio frequency (RF), vibration, and wind sources while optimizing power utilization through adaptive energy prediction and intelligent task scheduling. Experimental evaluation demonstrates that the proposed system achieves higher energy utilization, lower power consumption, improved communication reliability, and extended operational lifetime compared with conventional battery-powered IoT systems. Furthermore, the integration of cloud and edge computing enables real-time monitoring, predictive analytics, and scalable deployment across diverse IoT applications. Overall, the proposed framework offers a reliable, cost-effective, and environmentally sustainable solution for smart cities, healthcare, industrial automation, environmental monitoring, and precision agriculture, while providing a strong foundation for future research on AI-driven energy optimization and next-generation wireless-enabled self-powered IoT networks.
B. Vaishnavi, Kalasani Siddhartha, Dasarinki Ramprasad· International Journal of Cre...· 0 citations
Sustainable solutions in the built environment have become essential due to rapid urbanization and rising energy demands. Buildings account for nearly 40% of global energy consumption, making them a critical focus for energy efficiency and environmental sustainability. This paper explores AI-driven energy management systems in smart buildings, highlighting their ability to optimize energy use, reduce costs, and minimize environmental impact while maintaining occupant comfort. By integrating technologies such as IoT, machine learning, predictive analytics, and automation, these systems enable real-time monitoring and adaptive energy optimization. The study reviews traditional building management systems and identifies their limitations, proposing a layered architecture involving data acquisition, processing, prediction, and control. Machine learning techniques like ANN, SVM, and Reinforcement Learning are evaluated for energy forecasting and optimization. Findings indicate that AI-based systems can significantly improve energy efficiency, reduce carbon emissions, and enhance comfort, though challenges such as data privacy, system complexity, and initial costs remain. The research provides a practical framework for developing sustainable, energy-efficient smart buildings.
Meena Krishnan· International Journal of Mod...· 0 citations
The adoption rate of electric mobility, renewable energy systems, and smart transportation infrastructures has exacerbated the demand for real-time, high-performance and energy-efficient systems. While high latency, low bandwidth, and low responsiveness are commonly encountered drawbacks in existing cloud-based energy optimization techniques used in these mobility-driven systems. The deployment of Edge AI and IoT technologies will pave the path to efficient, low-latency and real-time distributed renewable energy optimization within the smart sustainable transportation system. This paper presents a detailed survey on the latest trends in AI based renewable energy integration, smart grid, EV, battery management systems, and real-time transportation data analytics. The application of deep learning, reinforcement learning, federated learning, and predictive analytics toward improved renewable energy generation forecasting, smart charging, load balancing, and Vehicle-to-Grid (V2G) co-ordination is also elaborated. Edge computing platforms and IoT devices can support a high-performance real-time monitoring, predictive maintenance and a self-sufficient energy distribution network for smart transportation. The experimental validation on contemporary research works reveal an average 70% reduction in transmission latency, more than 50% increase in the renewable energy consumption, approximately 30% increase in battery lifetime, and nearly 80% reduction in charging infrastructure offline duration for edge systems with AI assistance. Important concerns regarding the edge system’s security, scale-ability, connectivity, privacy, and the direction of future research on enabling smart transportation were also discussed.
Muthukumar Paramasivan, Manikandan Sivasubramanian, K. Alagar et al.· Proceedings of the Instituti...· 0 citations
Environmental monitoring facilitates solutions to major worldwide challenges, including air pollution, climate change, and water resource degradation. Yet, conventional cloud-based IoT systems are unable to provide real-time solutions because of issues like latency, increased energy consumption, and limited scalability. This paper aims to present a positive environmental impact of edge computing for real-time environmental monitoring and provide a sustainable, energy-efficient, low-latency environmental monitoring solution. The Edge Computing Real-Time Environmental Monitoring (ECRM) framework of the paper achieves local data processing and decision-making through the integration of edge intelligence, collective, and low-power machine learning models at edge gateways. The framework achieves system responsiveness and low energy consumption through the integration of energy-aware task scheduling and adaptive data transmission strategies. Processed data for air quality (AQI), CO₂, humidity, and temperature levels substantially reduce the framework's reliance on cloud computing. The framework provides a 39% reduction in energy consumption and a 40% reduction in latency in comparison to established cloud system models. The reductions improve real-time system responsiveness and reduce network traffic. The research demonstrates that combining edge architecture and green computing is a potential solution for sustainable environmental monitoring. The proposed systems align with the goals of computing sustainability and future smart city solutions.
Dawakit Lepcha, Kanchan Thakur· 2026 4th International Confe...· 0 citations
A Smart HVAC system that integrates Artificial Intelligence (AI), Internet of Things (IoT) sensors, cloud-based analytics, and machine learning to enhance energy efficiency, thermal comfort, and reliability is presented.
Suresh Babu Reddy· International Journal of Mod...· 0 citations
The increasing urbanization and energy demand necessitate state-of-the-art building management systems that can maximize energy efficiency while maintaining tenant comfort. Internet of Things (IoT) smart buildings constantly log data on occupancy, operations, and the surrounding environment. In order to derive useful insights from this mountain of data, sophisticated analytical frameworks are required. Modern optimization frameworks that integrate Machine Learning (ML) and the Internet of Things (IoT) lessen the energy consumption of smart buildings without sacrificing performance, sustainability, or user happiness. In the proposed system, sensors that are part of the Internet of Things track things like illumination, temperature, humidity, air quality, occupancy, and equipment performance. Machine learning algorithms examine the data sent by these networked devices, which might be located in a centralised or edge-based analytics platform.
P. Ragupathy, M. O. Sabri, Akila Venkatraman et al.· International Conference on...· 0 citations