Smart Building Management Systems Research Articles

Thermoelectric Generator Applications in Buildings: A Review

With growing concerns about building energy consumption, thermoelectric generators (TEGs) have attracted significant attention for their potential to generate clean, green, and sustainable power. This comprehensive review explores the applications of thermoelectric generators (TEGs) in building systems, focusing on recent advancements from 2013 to 2024. The study examines TEG integration in building envelopes, including façades, walls, windows, and roofs, as well as non-integrated applications for waste heat recovery and HVAC systems. Key findings highlight the potential of TEGs in energy harvesting and thermal management, with façade-integrated systems generating up to 100.0 mW/m² and hybrid LCPV/T-TEG systems achieving overall efficiencies of 57.03%. The review also identifies critical parameters affecting TEG performance, such as solar intensity, thermoelectric arm length, and PCM melting temperature. Despite promising results, challenges remain in improving overall system efficiency, cost-effectiveness, and scalability. Future research directions include developing more efficient thermoelectric materials, optimizing system designs for various climatic conditions, and exploring integration with smart building management systems. This review provides valuable insights for researchers and practitioners working towards more energy-efficient and sustainable building designs using TEG technology.

Solar photovoltaic buildings: The combination of sustainable energy and green buildings

Solar photovoltaic (SPV) buildings are at the cutting edge of renewable energy technology, offering a promising solution to meet our growing energy needs while minimizing environmental impact. This comprehensive review explores the multifaceted realm of solar PV buildings, examining their design, construction, energy performance compared to traditional structures, environmental consequences, and future possibilities. SPV buildings hold significant potential in promoting sustainability and achieving energy self-sufficiency. These structures not only generate clean energy but also contribute surplus electricity to the grid, enhancing community resilience and reducing dependence on traditional energy sources. Design and construction innovation involves implementing advanced architectural designs, utilizing smart building management systems, and incorporating renewable materials. These practices not only improve energy efficiency but also enhance the overall aesthetic appeal of structures. Performance evaluations consistently show that SPV buildings outperform traditional structures in terms of energy efficiency, carbon footprint reduction, and long-term cost-effectiveness. This translates to reduced energy consumption and environmental burdens. Environment issues such as energy-intensive panel production and waste disposal necessitate ongoing research to mitigate any negative environmental impact, thus necessitating technological developments to minimize these ramifications. SPV buildings represent the future of energy-efficient construction, leading the way to a cleaner and more eco-friendly world. Through tireless research and innovation, these structures may soon become mainstream solutions, providing environmental preservation as well as energy independence benefits for society at large.

Smart Class System Prototyping through Sustainable Infrastructure Based on Mobile Application SPACE (Smart Program - Class Architecture) Integrated Smart Building Management System

Optimizing innovation in the use of health technology in infrastructure will directly impact the conditions of social interaction in the community, including within the scope of the school area. The Smart Class concept collaborates with learning carried out in a hybrid way. However, it is not yet fully optimized by digitizing the existing infrastructure with the concept of managing school facilities and infrastructure. The implementation of the Smart Class uses the intelligent building management system (IBMS), where this system integrates all parts of the building with the Internet of Things (IoT), facilities, and infrastructure and can minimize pandemic transmission in the academic sphere by accessing via mobile app. This research aims to analyze the design of the smart program class architecture integrated monitoring system to implement IoT-integrated health protocols, especially in the middle school scope. This research uses qualitative and quantitative methods with descriptive analysis through the development of user research at the system design stage. The output of this innovation is how schools implement digital technology comprehensively, starting from the application of IoT, sensors, and databases in a real-time mobile app environment. There are three main things: the data input, data processing, and output value. The intervention of school needs can be expanded with the integration of the learning management system in the form of integrated learning management the learning process, which is in line with monitoring the use of infrastructure in schools. Through this Smart Class concept, schools can still operate optimally without any applicable health policies and can provide excellent academic services.
 Keywords: smart class system prototyping, sustainable infrastructure mobile application, SPACE smart building management system

Green hospitals face to climate change: Between sobriety and resilience

Climate change poses a critical challenge to global health, influencing social and environmental determinants such as housing, air and water quality, and food security. This article explores the profound impact of climate change on health, projecting an additional 250,000 annual deaths from various climate-related diseases between 2030 and 2050. Healthcare systems significantly contribute to global carbon emissions. The concept of the “Green Hospital” is introduced as a paradigm shift in healthcare, focusing on optimizing resource efficiency and minimizing environmental impact. This concept encompasses renewable energy integration, natural lighting, sustainable materials, green roofs, and smart building management systems. Several challenges remain major, such as medical waste management, water conservation, chemical use, pollution, and plastic usage in healthcare settings. Moreover, obstacles to green hospital initiatives should be resolved, including system redundancy, regulatory compliance, operational demands, financial constraints, and cultural resistance. Conclusively, an urgent reformation of healthcare systems is needed to align with eco-friendly and sustainable practices, highlighting the necessity to reduce CO2 emissions and manage resources and waste more effectively to meet the evolving health needs of a growing and aging global population.

Implementation of smart building management systems for residential complex “PANORAMA”

The European Union (EU) and Ukraine, which is following a precise European integration course, have a priority to improve energy efficiency in order to prevent negative environmental impact and achieve climate neutrality. It is known that more than 40% of the energy produced is accounted for by energy consumption in existing buildings. Analysis of energy consumption reveals a high percentage of energy losses due to poor management, which necessitates the development and implementation of systems and tools for improved energy management. Therefore, the development of energy efficient building management systems using modern engineering and technical solutions to control and optimise energy consumption is a relevant and promising way to solve the issue of energy resources saving, increase energy efficiency and enhance energy independence. Within the framework of the international research grant “A novel decentralized edge-enabled PREsCriptivE and ProacTive framework for increased energy efficiency and well-being in residential buildings” – PRECEPT under the Horizon 2020 programme, smart building management system was developed and implemented at the residential complex “PANORAMA” in Dnipro city, Ukraine.

Models and Methods of Information and Control System Cyber–security for Smart Buildings

Smart building management systems today employ the architectural multi-level creation of a system of unified components. Such a market is dynamic and always expanding, and sometimes the developers of such components stop maintaining their products or even collapse and vanish. It's not always possible to use professional-level components from proven developers when a building automation system (BAS) project budget is limited. The construction and analysis of models that consider the dependability and cyber security aspects of maintenance as well as ways of validating the selection of components, strategies, and service parameters are both necessary steps in finding compromises between the BAS architecture's value and quality. Use of information technology to determine maintenance parameters based on a model and model development approach.

Information System Design Smart Building Management System with Rapid Application Development Method and Laravel Framework

Information System Design Smart Building Management System with Rapid Application Development Method and Laravel Framework

Smart Building Management System (SBMS) for Commercial Buildings—Key Attributes and Usage Intentions from Building Professionals’ Perspective

Smart buildings conserve energy and create a responsive, comfortable, and productive indoor environment for users and occupants. As a crucial component of smart buildings, smart building management system (SBMS) should provide a wide range of functions and bring about the intended benefits upon successful deployment. This paper identifies salient SBMS attributes and explores key factors influencing building professionals’ intention to use the system in commercial buildings. Responses were collected from 327 Hong Kong building professionals. Data were analyzed by exploratory factor analysis and structural equation modeling based on the refined Unified Theory of Acceptance and Use of Technology (UTAUT). Exploratory factor analysis shows that intelligent building operations and safety and recovery readiness are two dimensions of SBMS emerged. Specifically, intelligent building operations include intelligent and optimal scheduling of building systems, monitor and control of building facilities, having an intelligent and interactive interface, and enabling alarm settings and automatic notifications, showing the importance on the application of electrical engineering in smart building management. Structural equation model (SEM) results indicate that facilitating conditions affect habit, hedonic motivation, social influence, performance expectancy and effort expectancy. Additionally, habit, hedonic motivation and effort expectancy significantly affect building professionals’ intention to use SBMS. Practical implications of SBMS attributes for energy management and the ways in which SBMS is encouraged to be used by building professionals are given.

Energy Minimized Computation Offloading With Popularity-Based Cooperation in 5G mMTC Networks

Due to enhancements in Internet of Things (IoT) technology, users can now control numerous IoT devices that are integrated for a system (e.g., smart building management system, smart factory, etc.) through mobile user equipment (UE). As the number of controllable IoT devices increases, the amount of data that needs to be processed has increased along with the energy consumption. Since many IoT devices and most UEs are battery operated, minimizing the energy consumption is very important. One solution is to have a multiaccess edge computing (MEC) system conduct the computation instead of the IoT devices and UEs to help save their energy. In this article, an energy-optimized offloading approach that uses MEC computing support to process cooperative tasks is investigated. An optimization problem model is developed to minimize the energy consumption of IoT devices and UEs that have service delay limits. The numerical results demonstrate that the proposed IoT and UE popularity-based energy optimization (IPEO) scheme provides a better performance compared to the conventional MEC offloading methods.

A Smart Building Resource Prediction, Navigation and Management System Supported by Radio Tomography and Computational Intelligence

This article presents research results on a smart building prediction, navigation and asset management system. The main goal of this work was to combine all comfort subsystems, such as lighting, heating or air conditioning control, into one coherent management system supported by navigation using radio tomographic imaging techniques and computational intelligence in order to improve the building’s ability to track users and then maximize the energy efficiency of the building by analyzing their behavior. In addition, the data obtained in this way were used to increase the quality of navigation services, improve the safety and ergonomics of using the room access control system and create a centralized control panel enriched with records of the working time of individual people. The quality of the building’s user habit learning is ensured by a network of sensors collecting environmental data and thus the setting values of the comfort modules. The advantage of such a complex solution is an increase in the accuracy of navigation services provided, an improvement in the energy balance, an improvement in the level of safety and faster facility diagnostics. The solution uses proprietary small device assemblies with implementation of popular wireless transmission standards such as Bluetooth, Wi-Fi, ZigBee or Z-Wave. These PANs (personal area networks) are used to update and transmit environmental and navigation data (Bluetooth), to maintain the connection of other PANs to the master server (Wi-Fi) and to communicate with specific end devices (ZigBee and Z-Wave).

Smart building management system: Performance specifications and design requirements

In a smart building, physical and computational elements are integrated to create an environment that is energy-efficient, comfortable, and safe for its occupants. The design and development of smart buildings is a complicated task. Every smart building is a unique structure from the requirements and characteristics standpoints. Therefore, achieving reliability and real-time adaptation to environmental conditions are some of the challenges involved in smart building development. Resolving these issues requires deep insights into control theory, machine learning, system specifications, and design requirements. To address this need, this paper proposes a real-time management system for controlling various aspects of smart buildings (indoor conditions, comfort criteria, security, safety, and costs), and also presents the performance specifications, design requirements, and operating constraints for these systems. The study aims to address two less-attended problems in the related literature of building management and control. First, only a few studies have attempted to include real-time learning of buildings' subjective parameters in the model-based control design. Second, to the best of the authors' knowledge, smart building management studies are primarily focused on optimizing thermal or visual aspects of buildings, and little attention is given to the simultaneous management of all building subsystems and objectives; i.e., considering buildings' physical models, environmental conditions, comfort specifications, and occupants’ preferences and safety in the design. Accordingly, in this paper, we combine machine learning with model-based control approaches to incorporate subjective environmental parameters into the building management structure. In addition, another benefit of this study is that it integrates model-based and learning-based control schemes in a unified management structure for controlling various aspects of building performance. The proposed building management system can be applied to a variety of smart buildings in which the building parameters can be monitored and self-tuned using a well-defined set of control inputs.

A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

People usually spend several hours per day inside buildings, and they require great amounts of energy and resources to operate. Although there are numerous studies about smart buildings, there is still a need for new intelligent techniques for efficient smart building management. This paper proposes the use of Wi-Fi network association information as a basis for the design of intelligent systems for smart buildings. We propose a unified experimental methodology to evaluate machine learning (ML) models on their capacity to accurately predict Wi-Fi access point demand for energy-efficient smart buildings. The evaluation involves the use of multiple classification and regression models using a variety of configurations and algorithms. We conducted an experimental analysis using our proposed methodology to determine which ML models provide the best performance results using data collected from a large scale Wi-Fi network located at Fluminense Federal University (UFF) over a period of 6 months. The proposed methodology enables the user to evaluate and to create ML models for energy efficient smart building management systems. We achieved 86.69% accuracy for occupancy prediction using classification techniques and RMSPE (Root Mean Squared Percentage Error) of 0.29 for occupancy count prediction using regression techniques.

IoT and Big Data Analytics for Smart Buildings: A Survey

Abstract The processes of digital transformation have involved a variety of socio-technical activities, with the objective of increasing productivity, safety and quality of execution, sustainable development, collaborative working and solutions for the sustainable smart city. The major digital trends, changing the building sector and revealing new trends of understanding information technologies to integrate in this sector. Current smart building management systems incorporate a variety of sensors, actuators and dedicated networks. Their objectives are to observe the condition of specific areas and apply appropriate rules to preserve or improve comfort while saving energy. In this paper, we propose a review of related works to IoT, Big Data Analytics in smart buildings.

CLIP: Carbon Dioxide testing suitable for Low power microelectronics and IOT interfaces using Room temperature Ionic Liquid Platform

Health and safety considerations of room occupants in enclosed spaces is crucial for building management which entails control and stringent monitoring of CO2 levels to maintain acceptable air quality standards and improve energy efficiency. Smart building management systems equipped with portable, low-power, non-invasive CO2 sensing techniques can predict room occupancy detection based on CO2 levels exhaled by humans. In this work, we have demonstrated the development and proof-of-feasibility working of an electrochemical RTIL- based sensor prototype for CO2 detection in exhaled human breath. The portability, small form factor, embedded RTIL sensing element, integrability with low-power microelectronic and IOT interfaces makes this CO2 sensor prototype a potential application for passive room occupancy monitoring. This prototype exhibits a wide dynamic range of 400–8000 ppm, a short response time of ~10 secs, and a reset time of ~6 secs in comparison to commercial standards. The calibration response of the prototype exhibits an R2 of 0.956. With RTIL as the sensing element, we have achieved a sensitivity of 29 pF/ppm towards CO2 at ambient environmental conditions and a three times greater selectivity towards CO2 in the presence of N2 and O2. CO2 detection is accomplished by quantifying the capacitance modulations arising within the electrical double layer from the RTIL- CO2 interactions through AC- based electrochemical impedance spectroscopy and DC- based chronoamperometry.

Применение методик проецирования многомерных данных для исследования данных от систем управления «умным» зданием

Применение методик проецирования многомерных данных для исследования данных от систем управления «умным» зданием

IoT and Big Data Analytics for Smart Buildings: A Survey

Abstract The processes of digital transformation have involved a variety of socio-technical activities, with the objective of increasing productivity, safety and quality of execution, sustainable development, collaborative working and solutions for the sustainable smart city. The major digital trends, changing the building sector and revealing new trends of understanding information technologies to integrate in this sector. Current smart building management systems incorporate a variety of sensors, actuators and dedicated networks. Their objectives are to observe the condition of specific areas and apply appropriate rules to preserve or improve comfort while saving energy. In this paper, we propose a review of related works to IoT, Big Data Analytics in smart buildings.

Using transfer learning for smart building management system

In building management, energy optimization is one of the main concern that needs to be automated. For automation, an intelligent system needs to be developed. However, an intelligent system needs to be trained in a large dataset before it can be used reliably. In this paper, we present a transfer learning scheme to develop an intelligent system for smart building management system. Specifically, the intelligent system is able to count human inside a room, which can be utilized to adaptively adjust energy usage in a room. The transfer learning scheme employs a deep learning model that is pretrained on ImageNet dataset. To enable the human counting capability, the model is trained on a dataset specifically collected for human counting case.

BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction

Running costs of buildings represent a significant outlay for all businesses, thus finding a way to run facilities as efficiently as possible is vital. IoT-enabled Building Management Systems provide means for process and resource usage automation leading to overall efficiency improvements. Inferring spatial and temporal occupancy in all its forms (binary, numerical or continuous) is one of the key contextual inputs required for smart building management systems. In this work, we showcase design, implementation and experimental validation of a smart building occupancy detection and forecasting solution. The presented solution comprises three main building blocks: (1) A fog computing indoor positioning system (BLEMAT — Bluetooth Low Energy Microlocation Asset Tracking) which, combined with wireless access network monitoring processes, produces indoor location information in a semi-unsupervised manner; (2) Data analysis and pattern searching pipelines responsible for fusing data coming from different smart building and networking systems and deriving information on temporal and spatial occupancy patterns; (3) Long short-term memory (LSTM) neural networks trained to predict occupancy patterns in different areas of a smart building. Data analysis and neural network training are conducted on real-world smart building dataset which authors provide in public online repository. Experimental validation confirms that the proposed solution can provide actionable occupancy detection and prediction information, required by smart building management systems.

Smart-building management system: An Internet-of-Things (IoT) application business model in Vietnam

The accelerating significance and prevalence of IoT (Internet-of-Things) have been recently appreciated by academicians and policy makers. The emergence of ubiquitous sensors, smart devices and broad-band Internet capacity has enabled the integration of networks for synchronous data collecting and processing, which ultimately facilitates prompt decision-making and physical responses to changes in a real-time manner. The virtual interconnectedness of humans and objects exerts managerial efficiency and emotional comfort for network operators, end users and other third party actors, which results in the embrace of IoT-based platforms in production and consumption. The skyrocketing urbanization has chronically caused dense concentration of population in buildings, which yields market prospects for smart-building management system solutions based on IoT applications. Nevertheless, IoT application business models have remained nascent in emerging and transitional economies. In this paper, the authors exploit the Business Model Canvas to assess an IoT start-up in Vietnam and to establish an output Business Model Canvas for entrepreneurs. Findings indicate high competitive advantages of local enterprises and propose vertical integration in joint ventures as the entry strategy for foreign investors.

Data analysis and visualization framework in the manufacturing decision support system of COMPOSITION project

Abstract The EU Factory of the Future (FoF) project COMPOSITION (Ecosystem for COllaborative Manufacturing PrOceSses – Intra- and Interfactory Integration and AutomaTION) aims to develop an Integrated Information Management System (IIMS) in order to combine physical world, simulation, planning and forecasting data for enhancing re-configurability, scalability and optimisation of resources and processes inside a factory. In addition, the second aim of this project is to extend locally integrated information management systems into an ecosystem that supports the interchange of data and services between factories and their suppliers, i.e., by incorporating and inter-linking both supply and value chains. In this paper, we present an overview of the COMPOSITION project with a particular focus on the incorporation of data analytics tools with the Manufacturing Decision Support System (MDSS) so as to enhance its functionalities. Furthermore, the connection of data analytics tools with a smart Building Management System (BMS) and Digital Factory Modelling (DFM) techniques for data propagation are presented as well. We show how state-of-the-art techniques and methodologies from the research fields of big data analytics, deep learning and simulation/forecasting are incorporated into the MDSS, and how the system is designed in order to accommodate the data-driven needs of a manufacturing environment.