Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1251-1256· 0 citations· 18 references
Abstract
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.
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.
Mahabala H. N.· International Journal of Mod...· 0 citations
The largest consumption of energy across the globe comes from residential building demographics. According to International Energy Agency nearly these buildings are responsible for around 30-40% of energy consumption across the world. One of the major issues related to energy waste in buildings is due to operation of t...
D. Sreevidya, R. Kishore, M. M. Chowdary et al.· International journal of com...· 0 citations
A smart, scalable architecture that integrates Internet of Things (IoT) technologies and Deep Learning models to improve the accuracy and adaptability of energy consumption forecasting in residential environments and demonstrates remarkable accuracy, achieving a Mean Absolute Error below 5% under diverse conditions.
Javier R. Caparrós, Felipe Romero, Elvira Maeso-González et al.· Dirección y Organización· 0 citations
The review highlights the significance of machine learning for load forecasting and the prediction of energy usage in buildings, and investigates cutting-edge modelling techniques such as digital twin technology, demonstrating its potential to contribute to energy efficiency.
Mekila Mbayam Olivier, Tijani Bounahmidi· Journal of Green Building, C...· 0 citations
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.
Meena Krishnan· International Journal of Mod...· 0 citations
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