2019· International Journal of Modern Research in Science & Engineering· 0 citations
TL;DR
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.
Abstract
Rapid urbanization and the demand for energy-efficient buildings have driven the need for intelligent HVAC systems. Traditional systems rely on fixed or manual controls, limiting their ability to adapt to changing environmental and occupancy conditions. This paper presents 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. The proposed system continuously monitors indoor conditions such as temperature, humidity, air quality, and occupancy in real time. It applies supervised learning for temperature prediction and reinforcement learning for adaptive control, enabling optimized HVAC operations based on both historical and real-time data. The architecture includes data acquisition, preprocessing, model training, and intelligent control, with considerations for scalability, security, and interoperability. Performance evaluation through simulations and real-world testing demonstrates significant energy savings, reduced carbon emissions, and improved occupant comfort compared to conventional systems. Additionally, the system supports fault detection, predictive maintenance, and reduced downtime. Overall, this work contributes to smart building development by providing an efficient, scalable AI-driven HVAC framework that promotes sustainable and cost-effective building management.
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
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
Commercial buildings are among the largest and least intelligent energy users in the modern economy, with heating, ventilation, and air-conditioning (HVAC) systems alone often accounting for more than half of their total energy consumption. For decades, the control of HVAC systems has relied primarily on static building management systems (BMSs) and manual adjustments, using fixed setpoints and schedules and periodic readjustment. Inefficient control has led to significant energy waste. As cities decarbonize and grids integrate more variable renewable energy, buildings must transform from passive energy consumers into intelligent, flexible, and grid-supporting assets. Recent advances in sensing, optimization, and artificial intelligence (AI) pave the way for this future. This article traces the evolution from well-tuned BMSs and supervisory analytics to optimized control using model predictive control (MPC) and advanced learning and looks ahead to the next generation of HVAC control systems capable of sensing occupancy, operating across spaces, interacting with the grid, and scaling to different buildings. We illustrate how real-time AI control can dynamically manage HVAC systems across zones and buildings, adjusting setpoints based on occupancy, weather, and energy prices to reduce waste, lower costs, and create new revenue streams through services, such as demand response (DR). As a case study, we highlight an AI-based automation system developed by the Massachusetts Institute of Technology (MIT) for its campus buildings. This system utilizes graph learning and reinforcement learning to capture limited room occupancy, local weather, and thermal interactions across multiple zones. In ongoing pilot projects, it has demonstrated significant energy savings by adjusting zone-level heating and cooling setpoints. Linking such academic innovations with a deployable and BMS-compatible software layer is key to transforming commercial buildings into intelligent and sustainable players in a clean energy future.
You Lin, Leslie K. Norford, J. Gregory et al.· IEEE Energy Sustainability M...· 0 citations
Residential buildings are consistently reported to account for a large share of global electricity consumption, with lighting and heating, ventilation and air-conditioning (HVAC) systems constituting the dominant loads. This paper critically reviews the literature on Internet of Things (IoT)-enabled autonomous lighting and HVAC optimisation in smart homes, tracing the evolution of home energy management from manual and rule-based control to sensor-driven, edge-capable architectures. The review examines conceptual foundations of smart home energy management systems, IoT communication architectures, autonomous lighting and HVAC control strategies, hybrid manual–voice–autonomous control schemes, and empirical studies on occupancy sensing and load management. Recurring limitations identified across the literature include the fragmented treatment of lighting and HVAC as isolated subsystems, weak real-time occupancy adaptation, over-reliance on cloud connectivity, and shallow load-level energy visibility. These gaps motivate the development of an integrated, edge-resilient, and cost-effective IoT architecture capable of coordinating lighting, thermal comfort, precision load monitoring and renewable energy analytics within a single residential system.
LIVINGSTONE ADUKU, IKENNA CALISTUS DIALOKE, ABUBAKAR SURAJO IMAM· International Journal of Eng...· 0 citations
The growing energy demand, acceleration of urbanization and expansion of industrial operations have raised the demand for intelligent energy management strategies for achieving energy efficiency and cost savings while minimizing carbon emissions. Most traditional energy management systems are based on rule based control structures and statistical methods that are not able to adjust to dynamic occupancy patterns, varying environmental conditions, and complex industrial processes. In recent years, a new technique, namely Machine Learning (ML), has emerged as a viable solution to predict, optimize, and automatically control energy use in smart buildings and industrial systems. The paper examines the various ML techniques for energy optimization in detail, and categorizes them into five areas: energy optimization for HVAC systems, energy optimization for lighting control, integration of renewable energy systems, energy optimization for industrial processes, and predictive maintenance. The proposed framework involves the combination of IoT sensors, real-time data collection, data preprocessing, feature engineering, development of ML models, and optimization algorithms, all aimed at realizing intelligent energy management. A set of supervised, unsupervised, deep learning, and reinforcement learning algorithms is examined, such as Random Forest, Support Vector Machine, XGBoost, Artificial Neural Network, Long Short-Term Memory network and Deep Reinforcement Learning with regard to their prediction accuracy, computational efficiency and energy saving requirement. As shown in the comparative analysis, advanced ML models consistently outperform traditional methods in predicting energy consumption, detecting consumption trends and reducing equipment idle time, as well as optimizing operational schedules. The results show that an energy optimization approach based on ML can greatly contribute to energy efficiency, operational cost savings, comfort of the occupants, and sustainable production in industry. The paper also presents the challenges and opportunities that are currently being faced, such as data quality, model interpretability, scalability, cybersecurity, and real-time deployment, and suggests future research directions, including the application of explainable AI, edge computing, digital twins, federated learning and autonomous energy management systems. In this study, the researchers give an overview of emerging ML techniques and practical lessons to the researchers and practitioners to develop intelligent, scalable and sustainable solutions for energy optimization in next-generation smart buildings and industrial facilities.
Prince Raj, Ankur Priyadarshi, Rajesh Kumar et al.· International journal of com...· 0 citations
With the development of modern energy systems, there is a need to operate increasingly intelligent electricity systems. Smart grids are advanced electricity networks that allow for integrating new technologies, increasing the reliability and performance of the power system, addressing the issues of renewable energy sources, growing electricity consumption, and real-time monitoring of the operating processes. Artificial Intelligence (AI) can be applied in different areas of smart grid to make the electricity system more reliable and competitive. The methods include Machine Learning, Deep Learning, Artificial Neural Networks, Reinforcement Learning, Fuzzy Logic, and Expert Systems. They are used to perform demand side management, predict electricity consumption, forecast renewable energy production, detect and diagnose grid failures, optimize maintenance, enhance protection and security, and manage energy consumption. However, there are challenges for implementing AI in the smart grid, such as data privacy, security, complexity, lack of transparency, and legislation. The current research explores the different aspects of applying AI technologies to smart grids. The paper provides some of the most common applications of AI and discusses the challenges and future outlook for AI-driven smart grids. The study emphasizes the significance of artificial intelligence for developing the next-generation smart grid.
A. Faiz, A. S, A. T· International Journal of Res...· 1 citation