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Journal articles on the topic 'IoT-enabled HVAC'
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Researcher. "THE FUTURE OF HVAC SYSTEMS: AI, CLOUD, AND THE INTERNET OF THINGS (IOT)." International Journal of Computer Engineering and Technology (IJCET) 14, no. 2 (2024): 269–78. https://doi.org/10.5281/zenodo.14507176.
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The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and cloud computing is transforming the HVAC industry, creating fully automated, efficient, and sustainable systems. This paper explores how IoT-enabled sensors, AI algorithms, and cloud-based platforms are redefining HVAC operations by enabling real-time monitoring, predictive maintenance, and energy optimization. It highlights the role of these technologies in advancing smart building concepts and discusses the challenges, including data security and integration costs, as well as the opportunities presented by their adoption. Emerging trends and future innovations in HVAC technologies are also examined, emphasizing their potential to drive sustainability and improve indoor environments.
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Vosughi, Amirkhosro, Mengran Xue, and Sandip Roy. "Occupant-Location-Catered Control of IoT-Enabled Building HVAC Systems." IEEE Transactions on Control Systems Technology 28, no. 6 (2020): 2572–80. http://dx.doi.org/10.1109/tcst.2019.2936804.
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Hossain Sikdar, Mohammad Shahadat, Md Shahadat Hossain, Saleh Mohammad Mobin, Adar Chowdhury, and Sharif Md Yousuf Bhuiyan. "ADVANCEMENTS IN SMART AND ENERGY-EFFICIENT HVAC SYSTEMS: A PRISMA-BASED SYSTEMATIC REVIEW." American Journal of Scholarly Research and Innovation 3, no. 01 (2024): 01–19. https://doi.org/10.63125/ts16bd22.
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This systematic review provides a comprehensive analysis of the advancements, challenges, and future directions of smart HVAC systems by examining 125 peer-reviewed articles following the PRISMA methodology. The study focuses on five critical areas: IoT-enabled HVAC systems, machine learning algorithms for predictive maintenance, renewable energy integration, data privacy and security concerns, and the economic feasibility of implementation. The findings highlight that IoT-enabled HVAC systems significantly enhance energy efficiency by leveraging real-time data from interconnected sensors, leading to energy savings between 20% and 40% through adaptive heating, cooling, and ventilation strategies. Additionally, machine learning algorithms in predictive maintenance play a crucial role in reducing system failures, cutting unscheduled maintenance costs by up to 40%, and improving energy efficiency by 15% to 25% through automated fault detection and optimized maintenance scheduling. Furthermore, renewable energy integration in HVAC systems, particularly hybrid solutions combining solar, geothermal, and wind energy, has demonstrated remarkable potential, with some systems achieving energy savings of up to 60%. However, challenges such as high upfront installation costs, the need for advanced energy storage solutions, and infrastructure limitations continue to hinder widespread adoption. The review also identifies data privacy and security vulnerabilities as a major concern in IoT and AI-powered HVAC systems. 20 reviewed articles highlight risks associated with data transmission, unauthorized access, and cyber threats, emphasizing the need for robust encryption techniques, blockchain-based security, and AI-driven authentication mechanisms to enhance protection and user trust. Moreover, the economic feasibility of smart HVAC solutions remains a key factor influencing adoption. While these systems require higher initial investments, findings from 15 reviewed studies indicate that the return on investment (ROI) typically falls within five to seven years, particularly for large-scale commercial applications where energy cost savings and predictive maintenance benefits accumulate more rapidly. Policy incentives, tax rebates, and government subsidies have also been recognized as crucial enablers for accelerating adoption, making smart HVAC technologies more accessible for residential and small business users. This review synthesizes existing knowledge, evaluates emerging trends, and identifies persistent barriers in smart HVAC technologies. The findings provide valuable insights for researchers, industry stakeholders, and policymakers to develop scalable, cost-effective, and energy-efficient HVAC solutions. Future research should focus on enhancing system interoperability, improving data security frameworks, and developing innovative financing models to facilitate the transition toward more sustainable and intelligent HVAC solutions in the built environment.
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Attallah, Mohammad. "Advancing HVAC Quality and Performance through State-of-the-Art Sensing Technology." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5824–44. https://doi.org/10.22214/ijraset.2025.71300.
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This study explores the integration of advanced sensing technologies into HVAC systems to improve efficiency and performance, with a focus on Northwest Indiana casinos. The project addresses challenges faced by traditional systems, such as inconsistent temperature control, high energy consumption, and poor air quality, which impact guest comfort and operational costs. Using a controlled testing environment and EnergyPlus simulation software, the research modeled casino-specific HVAC loads while incorporating IoT-enabled sensors, infrared thermal imaging, and AI-based optimization. Infrared cameras identified inefficiencies such as air leaks and duct blockages, while Schlieren imaging visualized airflow distribution to address zoning problems. Data was collected in 5-minute intervals across a 1-hour period to evaluate thermal comfort, airflow consistency, and energy use. Results demonstrate a 15% reduction in simulated energy consumption, enhanced airflow uniformity, and improved indoor air quality through better ventilation control. This research highlights the potential for intelligent HVAC technologies to enhance sustainability, reduce operational costs, and elevate guest experiences in high-occupancy environments.
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Colaco, Lindsey Velany, Dr Virani Hassanali, and Mr Mohinish Naik. "Development of IoT Enabled Energy Saving Smart Lighting and HVAC System for Building Environment." Journal of Energy Engineering and Thermodynamics, no. 36 (September 18, 2023): 1–12. http://dx.doi.org/10.55529/jeet.36.1.12.
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Most of the appliances used in office buildings such as the room lights, fans and Air Conditioners (AC) are manually operated, where one has to switch ON and OFF these appliances. If one tends to forget to turn OFF these switches, it will impose a major challenge in building economic stimulus for their fiscal development. It can thereby increase the amount of energy consumed and thus increasing the electricity bills. Therefore, there is an urgent need for improvement and use of a smart lighting system. This paper introduces the energy- saving, easy-to-install, wireless and low cost IoT based smart office lighting system which is suitable for installation in the office or departmental company. It uses an infrared human motion sensor (PIR Sensor) working in coordination with other sensors. When no one is there in the space, all lights, fans and Air Conditioners (AC) are turned OFF; otherwise they are turned ON to save energy. The same can be controlled via IoT and the internet. The system can easily realize energy saving and easy control of LED lighting.
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Muhammad, Shahriyar Islam, Chan Kah-Yoong, Sofea Azmi Anas, Pang Wai-Leong, and Wong Sew-Kin. "Internet of things-enabled smart controller for polymer dispersed liquid crystals films." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4708–20. https://doi.org/10.11591/ijece.v13i4.pp4708-4720.
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The evolvement of smart glass technology has gained a lot of interest through its energy-saving potential as one of the heating, ventilating, and airconditioning (HVAC) system. This paper focuses on polymer dispersed liquid crystal (PDLC) film, a smart glazing film that changes its opacity in response to an electrical impulse. The power consumption of the smart film is considerably small. However, improper handling of the smart film such as not turning off the film after usage can lead to energy wastage. Hence, connecting the smart film to an internet of things (IoT) controller would be one of the possible solutions to ensure that the film is maintained properly. The objective of the work here is to develop a smart, low cost and efficient IoT-enabled smart controller for PDLC films with energy-saving features. In pursuance of materializing this concept, this paper delineates the design of a smart controller for the PDLC films. The implementation of the IoT features, NodeMCU, and environmental sensors enabled the smart film to be capable of switching automatically. In addition, voice-command features were also incorporated into the controller. With the successful development of the IoT smart controller, the PDLC films can operate autonomously and wirelessly.
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Islam, Muhammad Shahriyar, Kah Yoong Chan, Anas Sofea Azmi, Wai Leong Pang, and Sew Kin Wong. "Internet of things-enabled smart controller for polymer dispersed liquid crystals films." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4708. http://dx.doi.org/10.11591/ijece.v13i4.pp4708-4720.
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<p><span lang="EN-US">The evolvement of smart glass technology has gained a lot of interest through its energy-saving potential as one of the heating, ventilating, and air-conditioning (HVAC) system. This paper focuses on polymer dispersed liquid crystal (PDLC) film, a smart glazing film that changes its opacity in response to an electrical impulse. The power consumption of the smart film is considerably small. However, improper handling of the smart film such as not turning off the film after usage can lead to energy wastage. Hence, connecting the smart film to an internet of things (IoT) controller would be one of the possible solutions to ensure that the film is maintained properly. The objective of the work here is to develop a smart, low cost and efficient IoT-enabled smart controller for PDLC films with energy-saving features. In pursuance of materializing this concept, this paper delineates the design of a smart controller for the PDLC films. The implementation of the IoT features, NodeMCU, and environmental sensors enabled the smart film to be capable of switching automatically. In addition, voice-command features were also incorporated into the controller. With the successful development of the IoT smart controller, the PDLC films can operate autonomously and wirelessly.</span></p>
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Wisdom Ebirim, Kehinde Andrew Olu-lawal, Nwakamma Ninduwezuor-Ehiobu, Danny Jose Portillo Montero, Favour Oluwadamilare Usman, and Emmanuel Chigozie Ani. "LEVERAGING PROJECT MANAGEMENT TOOLS FOR ENERGY EFFICIENCY IN HVAC OPERATIONS: A PATH TO CLIMATE RESILIENCE." Engineering Science & Technology Journal 5, no. 3 (2024): 653–61. http://dx.doi.org/10.51594/estj.v5i3.863.
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Efficient management of heating, ventilation, and air conditioning (HVAC) systems is paramount for mitigating energy consumption and enhancing climate resilience in modern infrastructure. This review explores the significance of leveraging project management tools to optimize energy efficiency within HVAC operations, thus fostering a pathway towards climate resilience. The integration of project management methodologies offers a systematic approach to address challenges associated with energy consumption in HVAC systems, thereby facilitating informed decision-making processes. Firstly, the review delves into the pressing need for energy conservation in HVAC operations amidst escalating concerns over climate change and its adverse impacts. The imperative to minimize energy consumption in buildings, particularly through HVAC systems, is highlighted as a pivotal step towards achieving climate resilience and sustainability goals. Subsequently, the review outlines the role of project management tools in orchestrating effective strategies to enhance energy efficiency within HVAC operations. By employing techniques such as project scheduling, risk management, and resource allocation, project managers can streamline the implementation of energy-efficient measures, thereby optimizing HVAC system performance and reducing carbon emissions. Moreover, the review underscores the importance of data analytics and technological advancements in augmenting the efficacy of project management tools for energy efficiency in HVAC operations. Leveraging real-time data monitoring, predictive analytics, and IoT-enabled devices enables proactive maintenance and continuous optimization of HVAC systems, thereby maximizing energy savings and bolstering climate resilience. Furthermore, the review elucidates the potential benefits and challenges associated with the adoption of project management tools for energy efficiency in HVAC operations. While improved cost-effectiveness, environmental sustainability, and operational performance emerge as primary benefits, challenges such as initial investment costs, technological complexities, and organizational inertia necessitate careful consideration. This review advocates for the integration of project management tools as a viable approach to foster energy efficiency in HVAC operations, thereby paving the way towards enhanced climate resilience and sustainability in built environments. By embracing innovative methodologies and technological solutions, stakeholders can mitigate energy consumption, reduce greenhouse gas emissions, and fortify infrastructure against the impacts of climate change.
 Keywords: HVAC, Climate Resilience, Project Management, Energy, Review.
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Villani, Lorenzo, Martina Casciola, and Davide Astiaso Garcia. "Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy." Buildings 15, no. 7 (2025): 1041. https://doi.org/10.3390/buildings15071041.
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This study presents an integrated approach for adapting building energy systems using Machine Learning (ML), the Internet of Things (IoT), and Building Information Modeling (BIM) in a hotel retrofit in Italy. In a concise multi-stage process, long-term climatic data and on-site technical documentation were analyzed to create a detailed BIM model. This model enabled energy simulations using the Carrier–Pizzetti method and supported the design of a hybrid HVAC system—integrating VRF and hydronic circuits—further enhanced by a custom ML algorithm for adaptive, predictive energy management through BIM and IoT data fusion. The study also incorporated photovoltaic panels and solar collectors, reducing reliance on non-renewable energy sources. Results demonstrate the effectiveness of smart energy management, showcasing significant potential for scalability in similar building typologies. Future improvements include integrating a temporal evolution model, refining feature selection using advanced optimization techniques, and expanding validation across multiple case studies. This research highlights the transformative role of ML, IoT, and BIM in achieving sustainable, smart, and efficient building energy systems, offering a replicable framework for sustainable renovations in the hospitality sector.
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Ali Hadi Abdulwahid. "IoT-Cloud Smart Campuses Enabling Real-Time Intelligent Resource Automation." Journal of Information Systems Engineering and Management 10, no. 41s (2025): 69–76. https://doi.org/10.52783/jisem.v10i41s.7751.
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This study proposes an IoT-based smart campus framework integrating Arduino microcontrollers, cloud services, and sensor networks to automate traffic control, adaptive lighting, smart rooms, and irrigation systems. The system aims to transform traditional campus management by enhancing operational efficiency, sustainability, and safety through real-time data analytics. Objectives: Use of Arduino microcontrollers to automate processes in campus and remote monitoring over cloud through MQTT protocols to optimize resource utilization. Method: Sensor networks (motion, gas, RFID), microcontrollers (Arduino Mega, NodeMCU) and wireless staff (ESP8266) was applied to collect real-time data. Cloud integration (CloudMQTT) and REST APIs enabled centralized control. Automated algorithms adjust lighting, HVAC, traffic signals, and irrigation based on sensor feedback. Results: Some key metrics are as follows: 40% in energy savings in lighting, 35% global water savings towards irrigation, 98.7% in RFID accuracy and 40% decrease on global traffic wait time (on peak hours). Conclusion: The IoT substantially enhances campus management. Future work will combine AI with air quality sensors
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Simon, János. "Possible implementations of the cloud-based data-centric IoT pilot platforms for smart cities and agriculture." Analecta Technica Szegedinensia 12, no. 1 (2018): 39–44. http://dx.doi.org/10.14232/analecta.2018.1.39-44.
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The Internet of Things (IoT) is starting to appear everywhere in many shapes and forms. But security is one of the most crucial thing that could trip up the growth of the IoT. Following security principles used in enterprise computing can help clear that issue. Already there are more connected devices than people on the planet, according to leading researchers in this area. By 2020, there will be 50 billion connected devices, outnumbering people by more than 6 to 1. Most of these devices will be controllable over the Internet, and they will increasingly be responsible for collecting and transmitting sensitive data. Today consumers might own an app that collects information on their exercise routine. In a few years, those same people might have an Internet-enabled medical device that continually delivers data to their doctor. In the wrong hands, data from home management systems could be used to assess user’s whereabouts. Likewise, businesses could be vulnerable when they connect things like HVAC, irrigation, or commercial appliances.
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Jaymin Pareshkumar Shah. "Integration of AI-based predictive maintenance for energy-efficient mechanical systems." World Journal of Advanced Engineering Technology and Sciences 11, no. 2 (2024): 664–73. https://doi.org/10.30574/wjaets.2024.11.2.0153.
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Predictive maintenance enabled by Artificial Intelligence (AI) transforms mechanical systems by improving their reliability levels as well as energy efficiency attributes. The conventional maintenance methods that include reactive and preventive measures repeatedly produce inefficient energy usage together with elevated operation expenses. Using AI alongside machine learning predictive maintenance transforms real-time sensor data into predictions which help maintainers schedule optimal maintenance times. The proactive system helps prevent downtime and cuts down energy loss and delivers improved operational results. Current industrial applications benefit from AI methods made up of deep learning and IoT-enabled data analytics and digital twins to anticipate anomalies and detect faults in HVAC systems and production facilities as well as power generation facilities. The ongoing implementation challenges involve poor quality data as well as cybersecurity threats together with difficult integration between systems. Self-learning AI models combined with edge computing and automated intelligent systems will enable better predictive maintenance through future advancements which will generate more sustainable and energy-efficient mechanical systems.
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Li, Wenzhuo, Hangxin Li, and Shengwei Wang. "An event-driven multi-agent based distributed optimal control strategy for HVAC systems in IoT-enabled smart buildings." Automation in Construction 132 (December 2021): 103919. http://dx.doi.org/10.1016/j.autcon.2021.103919.
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Teh, Kathleen Jo Lin, Halim Razali, and Chin Haw Lim. "Field Investigation of Thermal Comfort and Indoor Air Quality Analysis Using a Multi-Zone Approach in a Tropical Hypermarket." Buildings 15, no. 10 (2025): 1677. https://doi.org/10.3390/buildings15101677.
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Indoor environmental quality (IEQ), encompassing thermal comfort and indoor air quality (IAQ), plays a crucial role in occupant well-being and operational performance. Although widely studied individually, integrating thermal comfort and IAQ assessments remains limited, particularly in large-scale tropical commercial settings. Hypermarkets, characterised by spatial heterogeneity and fluctuating occupancy, present challenges that conventional HVAC systems often fail to manage effectively. This study investigates thermal comfort and IAQ variability in a hypermarket located in Gombak, Malaysia, under tropical rainforest conditions based on the Köppen–Geiger climate classification, a widely used system for classifying the world’s climates. Environmental parameters were monitored using a network of IoT-enabled sensors across five functional zones during actual operations. Thermal indices (PMV, PPD) and IAQ metrics (CO2, TVOC, PM2.5, PM10) were analysed and benchmarked against ASHRAE 55 standards to assess spatial variations and occupant exposure. Results revealed substantial heterogeneity, with the cafeteria zone recording critical discomfort (PPD 93%, CO2 900 ppm, TVOC 1500 ppb) due to localised heat and insufficient ventilation. Meanwhile, the intermediate retail zone maintained near-optimal conditions (PPD 12%). Although findings are specific to this hypermarket, the integrated zone-based monitoring provides empirical insights that support the enhancement of IEQ assessment approaches in tropical commercial spaces. By characterising zone-specific thermal comfort and IAQ profiles, this study contributes valuable knowledge toward developing adaptive, occupant-centred HVAC strategies for complex retail environments in hot-humid climates.
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Jagadish, B., P. K. Mishra, M. P. R. S. Kiran, and P. Rajalakshmi. "A Real-Time Health 4.0 Framework with Novel Feature Extraction and Classification for Brain-Controlled IoT-Enabled Environments." Neural Computation 31, no. 10 (2019): 1915–44. http://dx.doi.org/10.1162/neco_a_01223.
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In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled environments (BCE), which uses the classified MI task to assist disabled persons in controlling IoT-enabled environments such as lighting and heating, ventilation, and air-conditioning (HVAC). The first method for classification involves a simple and low-complex classification framework using a combination of regularized Riemannian mean (RRM) and linear SVM. Although this method performs better compared to state-of-the-art techniques, it still suffers from a nonnegligible misclassification rate. Hence, to overcome this, the second method offers a persistent decision engine (PDE) for the MI classification, which improves classification accuracy (CA) significantly. The proposed methods are validated using an in-house recorded four-class MI data set (data set I, collected over 14 subjects), and a four-class MI data set 2a of BCI competition IV (data set II, collected over 9 subjects). The proposed RRM architecture obtained average CAs of 74.30% and 67.60% when validated using datasets I and II, respectively. When analyzed along with the proposed PDE classification framework, an average CA of 92.25% on 12 subjects of data set I and 82.54% on 7 subjects of data set II is obtained. The results show that the PDE algorithm is more reliable for the classification of four-class MI and is also feasible for BCE applications. The proposed low-complex BCE architecture is implemented in real time using Raspberry Pi 3 Model B+ along with the Virgo EEG data acquisition system. The hardware implementation results show that the proposed system architecture is well suited for body-wearable devices in the scenario of Health 4.0. We strongly feel that this study can aid in driving the future scope of BCE research.
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Su, Bing, Shengwei Wang, and Wenzhuo Li. "Impacts of uncertain information delays on distributed real-time optimal controls for building HVAC systems deployed on IoT-enabled field control networks." Applied Energy 300 (October 2021): 117383. http://dx.doi.org/10.1016/j.apenergy.2021.117383.
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Piras, Giuseppe, Sofia Agostinelli, and Francesco Muzi. "Smart Buildings and Digital Twin to Monitoring the Efficiency and Wellness of Working Environments: A Case Study on IoT Integration and Data-Driven Management." Applied Sciences 15, no. 9 (2025): 4939. https://doi.org/10.3390/app15094939.
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Quality and efficiency of the work environment are essential to the well-being, health and productivity of employees. Despite the increasing focus on these aspects, many workplaces currently do not fully meet the needs and expectations of employees, with negative consequences for their well-being and productivity. The research aims to develop a system based on the Smart Building and Digital Twin paradigm, focusing on the implementation of various IoT components, the creation of automation flows for energy-efficient lighting, HVAC and indoor air quality control systems, and decision support through real-time data visualization enabled by user interfaces and dashboards integrating the geometric and information model (BIM). The system also aims to provide a tool for both monitoring and simulation/planning/decision support through the processing and development of machine learning (ML) algorithms. In relation to emergency management, real-time data can be acquired, allowing information to be shared with users and building managers through the creation of dashboards and visual analysis. After defining the functional requirements and identifying all3 the monitorable quantities that can be translated into requirements, the system architecture is described, the implementation of the case study is illustrated and the preliminary results of the first data collection campaign and initial estimates of future forecasts are shown.
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Satish Chitimoju. "The role of reinforcement learning in autonomous architectural optimization and energy efficiency." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 3358–73. https://doi.org/10.30574/wjarr.2024.24.3.3907.
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The surge of worldwide energy requirements and the necessity of sustainable architecture have made artificial intelligence optimization methods more important than ever. Reinforcement Learning (RL) is a fundamental method to develop better energy performance in architecture through data-based adaptive decision systems. Real-time operation capabilities of RL models allow them to change architectural parameters dynamically, optimizing energy consumption and building performance output. Autonomous architectural design benefits from applying RL technology, which enhances sustainability, improves material efficiency and minimizes environmental effects. This research analyzes different RL optimization methods that enhance building efficiency through their capacity to produce energy-efficient designs. Various case studies demonstrate how RL technology leads to successful results in smart HVAC control systems, daylight optimization systems, and material selection processes. This research examines different implementation obstacles in RL utilization in architecture, such as sophisticated algorithms, difficulty achieving stable results, and real-time adjustments. This study examines how RL operates with IoT-enabled smart buildings, particularly in intelligent energy management. RL develops crucial possibilities for sustainable architecture through its ability to create learning structures that improve themselves automatically. The study demonstrates how architectural advances from RL need combined efforts between architects, engineers, and AI researchers to produce effective solutions. RL-based research explores potential solutions and future growth to demonstrate its potential for building the next generation of intelligent energy-efficient buildings. The research boosts sustainable architectural development by discovering efficient methods to defend environmental responsibility during urban modernization.
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Satish, Chitimoju. "The role of reinforcement learning in autonomous architectural optimization and energy efficiency." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 3358–73. https://doi.org/10.5281/zenodo.15266720.
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The surge of worldwide energy requirements and the necessity of sustainable architecture have made artificial intelligence optimization methods more important than ever. Reinforcement Learning (RL) is a fundamental method to develop better energy performance in architecture through data-based adaptive decision systems. Real-time operation capabilities of RL models allow them to change architectural parameters dynamically, optimizing energy consumption and building performance output. Autonomous architectural design benefits from applying RL technology, which enhances sustainability, improves material efficiency and minimizes environmental effects. This research analyzes different RL optimization methods that enhance building efficiency through their capacity to produce energy-efficient designs. Various case studies demonstrate how RL technology leads to successful results in smart HVAC control systems, daylight optimization systems, and material selection processes. This research examines different implementation obstacles in RL utilization in architecture, such as sophisticated algorithms, difficulty achieving stable results, and real-time adjustments. This study examines how RL operates with IoT-enabled smart buildings, particularly in intelligent energy management. RL develops crucial possibilities for sustainable architecture through its ability to create learning structures that improve themselves automatically. The study demonstrates how architectural advances from RL need combined efforts between architects, engineers, and AI researchers to produce effective solutions. RL-based research explores potential solutions and future growth to demonstrate its potential for building the next generation of intelligent energy-efficient buildings. The research boosts sustainable architectural development by discovering efficient methods to defend environmental responsibility during urban modernization.
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Bajwa, Ammar, Faria Jahan, Ishtiaque Ahmed, and Noor Alam Siddiqui. "A SYSTEMATIC LITERATURE REVIEW ON AI-ENABLED SMART BUILDING MANAGEMENT SYSTEMS FOR ENERGY EFFICIENCY AND SUSTAINABILITY." American Journal of Scholarly Research and Innovation 03, no. 02 (2024): 01–27. https://doi.org/10.63125/4sjfn272.
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This systematic review has demonstrated that Artificial Intelligence (AI) plays a transformative role in Smart Building Management Systems (SBMS), enhancing energy efficiency, predictive maintenance, and sustainable automation. By analyzing 472 high-quality studies, this research has identified that AI-driven HVAC optimization, lighting control, solar energy forecasting, and demand-side energy management significantly reduce energy consumption, with reported efficiency improvements ranging between 20-50%. The review also highlights that reinforcement learning (RL) and deep learning (DL) models outperform traditional rule-based systems by dynamically adjusting building operations based on real-time sensor data, occupancy patterns, and environmental conditions. AI-powered fault detection and predictive maintenance further improve building operations by reducing unexpected system failures, lowering maintenance costs by up to 35%, and extending equipment lifespan. Moreover, the study underscores the growing potential of hybrid AI models integrating IoT, blockchain, and cloud computing in enabling real-time energy monitoring, decentralized energy trading, and secure automation. Despite these advancements, the review also reveals critical research gaps, particularly the lack of large-scale empirical validation, challenges in AI scalability, and the need for interdisciplinary collaboration to enhance AI’s effectiveness in sustainable building design. While theoretical and simulation-based studies provide strong evidence of AI’s benefits, real-world pilot projects, regulatory frameworks, and cross-sector collaborations are essential for AI-driven smart building technologies to achieve widespread adoption. Addressing these challenges through industry-academia partnerships, policy support, and further longitudinal research will be key to ensuring that AI-powered SBMS can drive long-term sustainability, operational efficiency, and energy resilience in modern smart infrastructure.
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Mistry, Vrushank. "The Role of IoT in Enhancing HVAC Control Systems." Journal of Biosensors and Bioelectronics Research, March 30, 2023, 1–5. http://dx.doi.org/10.47363/jbber/2023(1)115.
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This comprehensive research paper investigates the paradigm shift in Heating, Ventilation, and Air Conditioning (HVAC) control systems propelled by the transformative integration of the Internet of Things (IoT). In an era marked by the convergence of digital technologies, the infusion of IoT into HVAC systems heralds a new era of dynamic, interconnected control mechanisms. This study undertakes a thorough examination of the evolving landscape, shedding light on the profound advancements, discernible benefits, and nuanced challenges intrinsic to harnessing IoT for the augmentation of HVAC control systems. The journey begins by elucidating the fundamental shifts catalyzed by the assimilation of IoT in HVAC systems. The traditional boundaries of HVAC control are transcended as interconnected devices seamlessly communicate, fostering an environment where each component becomes an intelligent node in a networked ecosystem. Real-time data exchange becomes the bedrock, facilitating a level of monitoring and control hitherto unseen. The paper explores the intricacies of this interconnectedness, unveiling the potential for granular control and adaptability that IoT ushers into HVAC operations. A focal point of this research is the exploration of the tangible benefits that arise from this symbiosis of IoT and HVAC control. The paper meticulously examines how real-time monitoring empowers system operators with unprecedented insights into performance metrics, energy consumption patterns, and environmental conditions. Harnessing this wealth of data, IoT-equipped HVAC systems demonstrate an unparalleled capacity for adaptive control, responding dynamically to fluctuating demands and external variables. The consequential improvements in energy efficiency and resource utilization contribute not only to operational cost savings but also align with global sustainability objectives. However, in the pursuit of technological advancement, challenges inevitably emerge. This research critically evaluates the impediments and challenges inherent in the integration of IoT into HVAC control systems. Security concerns, data privacy issues, and the evolving landscape of technology standards are among the multifaceted challenges explored in depth. The paper endeavors to provide a nuanced understanding of these challenges, offering insights that can inform the development of robust and resilient IoT-enabled HVAC control systems
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Mohammadi, Mohsen, Ghiwa Assaf, and Rayan H. Assaad. "Integrating immersive virtual reality (VR) technologies and multimodal IoT-enabled wireless sensor networks for real-time smart human-centered HVAC building system interaction and thermal comfort assessment and visualization." Smart and Sustainable Built Environment, February 11, 2025. https://doi.org/10.1108/sasbe-04-2024-0117.
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PurposeBy harnessing technology developments such as Internet-of-Things (IoT)-enabled intelligent sensors and immersive virtual reality (VR) experiences, facility managers can access real-time, precise information on thermal comfort-related indicators through virtual facility models. While prior research studies have developed key technologies for improving the understanding of thermal comfort and its impact on the occupants’ well-being and productivity, there remain areas yet to be explored, especially in relation to integrating both real-time data from multimodal IoT-enabled smart sensors and VR technologies. Hence, this study demonstrates the potential of integrating IoT and VR technologies for real-time thermal comfort assessment and visualization as well as user interaction with HVAC systems to enhance thermal comfort.Design/methodology/approachTo develop the proposed integrated analytical framework in this paper, various steps were implemented. First, four multimodal IoT-enabled sensing stations were created and installed to collect real-time thermal comfort-related data (i.e. temperature and relative humidity). Second, a VR environment was developed using the Unity engine to offer an immersive experience. Third, the real-time data from the IoT-enabled sensing stations was integrated into the VR environment by transmitting it to the cloud via the MQTT protocol server, and various programming scripts were developed to provide multiple functionalities to the users, including visualizing the thermal comfort along the entire indoor space as well as interacting with and controlling the cooling and heating HVAC systems. Fourth, the applicability and effectiveness of the developed framework was validated and evaluated by 92 participants using a survey questionnaire.FindingsThe obtained survey results validated the importance and effectiveness of the developed framework on various aspects including graphical satisfaction, spatial presence, involvement, experienced realism, low-to-no cybersickness and overall application satisfaction, among others. More specifically, the findings reflected that the participants’ average scores for graphical satisfaction, sense of spatial presence, involvement and experienced realism were 4.69, 4.61, 4.71 and 4.53 out of 5, respectively. Hence, the results showed that the visualization capabilities of the developed framework serve as a powerful feature that enables a comprehensive visualization of thermal comfort variations across the entire room/office space. Also, the results showed that there were no statistically significant differences between the responses of participants with prior VR experience with those from participants with limited-to-no prior VR experience, thus further highlighting the usefulness of the proposed technology not only for experienced users but also for users from different skills and background.Originality/valueThis research has the potential to revolutionize the way built environments are managed and interacted with, where facility managers can monitor, assess and visualize thermal comfort in real-time as well as interact with the HVAC systems and control multimodal IoT devices in the real-world from a distance through virtual facility models. The proposed framework’s ability to provide dynamic and continuously updated assessments of thermal conditions in real-time positions it as a valuable tool for prompt adjustments to optimize occupants’ comfort levels. Ultimately, the proposed framework provides an intuitive and immersive platform to manage thermal comfort, thus promoting healthier, more productive and eco-friendly indoor environments.
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-, Akshat Bhutiani. "Advanced Video & Image Processing Algorithms for Enhancing Predictive Maintenance in IoT – Enabled Smart Infrastructure." International Journal For Multidisciplinary Research 5, no. 6 (2023). https://doi.org/10.36948/ijfmr.2023.v05i06.22346.
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The integration of predictive maintenance algorithms into IoT enabled smart infrastructure has revolutionized asset management by enhancing operational efficiency, reducing downtime, and lowering maintenance costs. This paper explores advanced video and image processing algorithms tailored to address unique challenges of predictive maintenance in smart infrastructure systems. By leveraging techniques such as object detection, anomaly recognition, and pattern analysis, these algorithms enable the accurate monitoring of critical components like energy systems, HVAC units and industrial machinery. The proposed solutions incorporate state-of-the-art imagine processing frameworks, including convolutional neural networks (CNNs) and optical flow analysis, optimized for deployment on edge devices to ensure real-time analysis and minimal latency. Experimental results validated on diverse IoT datasets, demonstrate significant improvements in fault detection accuracy and system improvements.
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Ramadan, Montaser N. A., Mohammed A. H. Ali, and Mohammad Alkhedher. "Federated Learning-Enabled IoT Framework for Indoor Air Quality and HVAC Optimization in Healthcare Buildings." Journal of Building Engineering, April 2025, 112758. https://doi.org/10.1016/j.jobe.2025.112758.
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Ejenakevwe, Kevwe Andrew, Junke Wang, Yilin Jiang, Li Song, and Kini L. Roshan. "Automated Fault Detection and Diagnosis of Airflow and Refrigerant Charge Faults in Residential HVAC systems using IoT-Enabled Measurements." Science and Technology for the Built Environment, July 10, 2023, 1–20. http://dx.doi.org/10.1080/23744731.2023.2234231.
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Mohammadi, Mohsen, Ghiwa Assaf, and Rayan H. Assaad. "Real-time Spatial Mapping and Visualization of Thermal Comfort and HVAC Control by Integrating Immersive AR Technologies and IoT-Enabled Wireless Sensor Networks." Journal of Building Engineering, June 2024, 109887. http://dx.doi.org/10.1016/j.jobe.2024.109887.
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Redder, Florian, Philipp Althaus, Eziama Ubachukwu, et al. "Information and Communication Technologies (ICT) for the intelligent operation of building energy systems: design, implementation and evaluation in a living lab." Energy Informatics 8, no. 1 (2025). https://doi.org/10.1186/s42162-025-00536-2.
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Abstract Successful adaptation to climate change requires resilient, reliable, and efficient energy systems. To unlock energy efficiency potentials in buildings, an intelligent, user-centered approach is vital. However, this requires handling diverse data on the energy system. Therefore, technologies for harmonizing, storing, and visualizing data, as well as managing physical devices and users are needed. This work assesses existing and required Information and Communication Technologies (ICT) for intelligent building energy system operation. We propose an intermediate architecture based on Internet of Things (IoT) core principles and feature insights from its implementation within the Living Lab Energy Campus (LLEC) at Forschungszentrum Jülich. We present an approach for integrating existing ICT components, such as building energy metering and central Heating, Ventilation and Air Conditioning (HVAC) management, and propose a comprehensive data collection and distribution infrastructure. We establish IoT-enabled applications for energy system monitoring, user engagement, advanced building operation, and device identification and management. We evaluate our ICT setup through functional and performance assessments. We find that heterogeneous data can be reliably collected, distributed, and managed using standardized interfaces, state-of-the-art databases, and cutting-edge software components. For the buildings operated through the ICT infrastructure, data transmission availability is above 98.90 %, mean time to repair (MTTR) is less than 2.68 h, and mean time between failures (MTBF) is in the range of 242.67 h to 1092.00 h, evaluated over a period of three months. Our approach promotes the early real-world adoption of intelligent building control prototypes and their sustainable development. We demonstrate the proposed ICT setup through an experimental study that applies a cloud-based Model Predictive Controller (MPC) to a real building space. Our results provide a comprehensive discussion of the required ICT setup for intelligent building energy system control in real-world environments, and highlight important design strategies that reduce the conceptual overhead and facilitate implementation in similar projects.