Intelligent Building Management Research Articles

Application of energy combined thermal comfort in intelligent building management in complex environments

The efficient operation of heating ventilation and air conditioning systems relies on advanced control strategies. However, current control methods are often limited by issues such as uncertain system parameter information and spatial coupling constraints related to the supply rate of the air supply fan. To this end, an energy joint thermal comfort management method for complex environments in multiple regions is proposed. The long-term total cost minimization of the system is established, and then the Lyapunov optimization technology is used to design the distributed control algorithm. Simulation validation shows that the proposed method reduces the energy cost by an average of 11.24% compared to other methods with a thermal discomfort cost coefficient of 0. The average temperature deviation in the area is improved by 0.15 °C and 0.68 °C, respectively. The method saves more than 10% of the total energy cost under different thermal perturbations with an average total temperature deviation of 0.04 °C. The results indicate that the proposed energy joint thermal comfort management method can flexibly balance energy costs and user thermal comfort without knowing any prior information of system parameters, which can also greatly protect user privacy information. This method has application value in the control of heating ventilation and air conditioning systems in complex environments such as commercial buildings.

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

SSIOE: Self-Supervised Indoor Occupancy Estimation for Intelligent Building Management

This paper aims to estimate indoor occupancy given the real-time observed signals from the existing sensors; next, as a practical application, we build a dynamic control schedule for energy saving based on the estimated indoor occupancy. However, several issues need to be addressed. First, it is impossible to train the model with rich labels due to the expensive labeling cost. Second, manual annotation of the continuous occupancy rate is complex. Third, the mapping relationship between sensor data and occupancy will change in the long run. In this paper, we proposed a new algorithm named Self-Supervised Indoor Occupancy Estimation (SSIOE) to overcome the challenges. Specifically, our training scheme aims to (I) generate a set of pseudo labels in a simple way to mark the time periods believed to be either in a high or low occupancy state and (II) utilize these sparse labels for training a network to infer the continuous occupancy ratio. By reformulating the problem as a Wasserstein-distance-like estimation, SSIOE is a novel learning-based method that can rely only on the weak/sparse labels of either “high-occupancy” or “low-occupancy” and learn to estimate the continuous occupancy ratio. Furthermore, to deal with the scarce annotation problem, we proposed a novel physical constraint loss to model the physical prior. (III) Last but not least, to strengthen the adaptability, we integrated Model-Agnostic Meta-Learning (MAML) to train the model for dynamic model updating. Experimental results show that SSIOE can provide reliable occupancy estimation and flexibly adapt to various control modes without retraining the model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the real-time occupancy estimation problem, which is crucial for building management systems to arrange lighting and air conditioning. A dynamic schedule adapted to the occupancy would help balance the user’s comfort and energy saving; however, installing the occupancy sensors raises additional costs. Therefore, this paper proposes SSIOE, a label-free machine learning algorithm that can learn to estimate the continuous occupancy ratio given the sensor status in real time. Prior works trained upon supervised machine learning methods suffer from heavy manual annotation workload, while the unsupervised machine learning approaches fail to provide continuous occupancy estimation values. This paper addresses the above challenges by proposing a novel self-supervised training scheme. Preliminary experiments suggest that this approach is feasible to work in real-world buildings. However, it should be noted that the occupancy estimated by SSIOE is a ratio (0.0-1.0), which is relative to the maximum and minimum occupancy of the training data (or fine-tuning data). For example, if the maximum number of occupants in the training data is 200 and the minimum number of occupants is 50, then the estimated occupancy value of 1.0 is about 200 occupants, and the occupancy value of 0.0 is about 50 occupants. When the maximum and minimum occupants of the space change, the model needs to be updated to capture the new mapping of the estimated occupancy ratio to the number of occupants. In future research, we plan to further improve and simplify the adaptation ability of SSIOE by continuously and automatically updating the system with the latest collected as the system runs.

Technical framework of energy-saving construction management of intelligent building based on computer vision algorithm

At present, China's urbanization transformation is accelerating, the construction industry is developing rapidly, and people's requirements for buildings are also increasing, which leads to the increasing energy consumption. Therefore, energy-saving construction in the construction industry is imminent. During the actual life cycle of a building project, including planning, design, construction and later operation and maintenance, energy consumption will be generated in the above processes. Based on this background, this research realized the design of intelligent building management and information system for energy conservation projects by introducing computer vision algorithm. The system is integrated with laws and regulations, economic benefits, technical steps, quality management, cost management, schedule management and other modules. The design process is relatively complex. Therefore, this work adopts modern computer vision algorithm technology and uses relevant tools to collect, store, and analyze a large amount of information in each stage of energy-saving project management process and realize information management from project approval, design, drawing review, and construction to acceptance. Finally, this paper builds system interface layer, system function layer, and database support layer. The system measurement shows that the building energy consumption can be predicted easily, accurately and stably by inputting the energy consumption data collected by each part of the target building. The system realizes the combination of qualitative and quantitative research on energy conservation regulation and improves the informatization level of China's energy conservation research and construction. This paper designs a kind of intelligent building energy-saving management system by studying computer vision algorithm.

ADAPTIVE TECHNOLOGIES IN INTERNATIONAL AIRPORT ARCHITECTURE IN THE XXI CENTURY

The article considers with the architecture of airports adapted to complex natural and anthropogenic conditions. The study has identified the methods used in modern architecture and engineering solutions to create sustainable airport complexes. Examples of artificial land formation, incorporation of dynamic structures and intelligent building management systems have been described as evolutionary solutions affecting the functioning of facilities.

SLICE - Solar Lightweight Intelligent Component for Envelopes: application for the ICARO pavilion

In the ongoing race to reduce polluting emissions, a significant contribution is made by the introduction of intelligent building management systems. The European directive 2002/91 on energy performance in buildings already invited us to look at a new generation of almost zero energy buildings. Research in the field of multifunctional facades offers the opportunity to provide an immediate response to improving air quality and, at the same time, optimizing the use of resources. In fact, adaptive facades can be traced to one of the most effective strategies to efficiently manage the interactions between external and internal environments to maximize winter heating, summer shading and natural ventilation, sound insulation, daylight transfer, glare-free, and interior comfort for the occupants. Nowadays, adaptive enclosures are not only able to interact with the environment and the user but allow the energy needs of buildings to be reduced by integrating technologies to produce energy from renewable sources. The contribution will present the latest developments of an innovative kinetic device for architecture, carried out by the University of Catania; it is the result of a multidisciplinary research project involving important local industrial companies. The component consists of a composite material substrate with integrated high-efficiency photovoltaic technology. The results shown below made it possible to evaluate the use of the designed component in light and stand-alone architectures, representing a valid solution to reduce the impact on the territory even in fragile contexts.

An Analytical Model for the Impact of Building Control and Automation Upgrade on Space Heating Energy Efficiency

Intelligent building management systems are proven to lead to energy savings and are an integral component of smart buildings. The procedures developed in the EN standards describe the methodology for calculating the energy savings achieved by improving the automation and control levels of separate services in building systems. However, although this method is used in practice, it is rarely applied or investigated by the research community. Typically, energy savings resulting from a single automation improvement intervention in a building heating system are observed, while the holistic view of combined automation upgrades is not considered. Therefore, the purpose of this study was to assess the energy savings resulting from several upgrades to control levels in the heating system components of the building. In addition, this research provides a rationale for the impact of multiple automation and control options for heating systems as well as examines the difference in energy savings. Finally, an analytical model is developed and demonstrated to assess the feasibility of building automation and control upgrades, by determining the allowed investment according to a set of predefined indicators.

A closed-loop data-fusion framework for air conditioning load prediction based on LBF

Accurate air conditioning load prediction is a key component of intelligent building management system for ensuring energy saving and safe operation of air conditioning system. In order to improve the prediction accuracy, a particle filter (PF) load prediction fusion estimation method based on long short-term memory (LSTM) and back propagation neural network (BP) is proposed. Firstly, spearman correlation analysis is used to select the influencing factors with high correlation as feature input. Aiming at the problem that the original signal is easy to be disturbed by noise and the data features are not obvious, locally weighted scatterplot smoothing (LOWESS) method is used to denoise the data to improve the data quality for further accurate prediction. Secondly, the data-driven air conditioning load state-space representation is established, which takes air conditioning load as the state variable and takes the load features collected by the sensor in real-time as the input variables. Thirdly, combined with the space representation method of air conditioning load based on LSTM-BP, PF is introduced to estimate the air conditioning load by using the fusion model. Meanwhile, the output load value of BP is fed back to the fusion model as the observation value to update the state-space representation of air conditioning load. Finally, two practical cases are used to verify the effectiveness of the method. The results indicate that the proposed method can provide more accurate and robust air conditioning load prediction.

Efficient People Counting in Thermal Images: The Benchmark of Resource-Constrained Hardware

The monitoring of presence is a timely topic in intelligent building management systems. Nowadays, most rooms, halls, and auditoriums use a simple binary presence detector that is used to control the operation of HVAC systems. This strategy is not optimal and leads to significant amounts of energy being wasted due to inadequate control of the system. Therefore, knowing the exact person count facilitates better adjustment to current needs and cost reduction. The vision-based people-counting is a well-known area of computer vision research. In addition, with rapid development in the artificial intelligence and IoT sectors, power-limited and resource-constrained devices like single-board computers or microcontrollers are able to run even such sophisticated algorithms as neural networks. This capability not only ensures the tiny size and power effectiveness of the device but also, by definition, preserves privacy by limiting or completely eliminating the transfer of data to the cloud. In this paper, we describe the method for efficient occupancy estimation based on low-resolution thermal images. This approach uses a U-Net-like convolutional neural network that is capable of estimating the number of people in the sensor’s field of view. Although the architecture was optimized and quantized to fit the limited microcontroller’s memory, the metrics obtained by the algorithm outperform the other state-of-the-art solutions. Additionally, the algorithm was deployed on a range of embedded devices to perform a set of benchmarks. The tests carried out on embedded processors allowed the comparison of a wide range of chips and proved that people counting can be efficiently executed on resource-limited hardware while maintaining low power consumption.

Intelligent OPC Building Information Management System Based on IoT

With the rapid development of artificial intelligence, computers and Internet of Things technologies, building intelligence is becoming more and more popular. However, as the various functional subsystems and equipment of intelligent buildings are connected to each other, the traditional management system needs to monitor and modulate more and more programs, which will have a great impact on the interoperability of the subsystems. Therefore, a more complete and powerful management system is needed. OPC provides a unified standard that can effectively solve this problem. Therefore, the purpose of this article is to design an intelligent OPC building information management system based on the Internet of Things. This article first summarizes the development history and status quo of OPC technology, and then extends the design principles of building information management system based on OPC. Based on its design principles, a detailed analysis of the various subsystems of the building information management system, such as fire protection, intrusion prevention, monitoring, access control, and central air conditioning, is carried out. This article systematically explained the application of PID in the building information management system. And use comparative analysis method, observation method and other research forms to conduct experimental research on the intelligent OPC building information management system based on computer Internet of things. The research shows that compared with the traditional building management system, the intelligent OPC-based building management system researched in this paper can transmit information faster and have higher accuracy.

Performance gaps of sustainability features in green award-winning university buildings

Sustainability certifications like BREEAM, LEED, and China's sustainable green building award certification scheme encourage installation of technologies that save mains water and grid electricity. Among these are rainwater harvesting systems, ultralow water use appliances, photovoltaic panel systems, and intelligent building management systems. In reviewing the performance of two award-winning university buildings over respective periods of sixteen and four years, we found that such systems delivered only 28–71% of their potential resource savings. These performance gaps arose from various technical and social issues (pump failures, tank leakages, poor alignment of demand and supply with limited storage, low photovoltaic panel efficiency, poor user acceptance, etc.), but the consequences were exacerbated by inadequate asset management that resulted in long system downtimes, in some cases for 2–5 years. Repair, maintenance, and upgrading expenses then combined with lower than anticipated water and electricity bill savings that ultimately meant that, for the most part, there was no prospect of earning a return on capital expenditures. Continuous monitoring of building water consumption by an external service provider was the most effective resource and cost saving solution in this study, as it required no capital expenditure, and revealed a 1640 l/h leakage, without putting high demands on the building management. In contrast, little value was obtained from 25 water-related sensors installed as part of a “building-as-a-lab” project because of inadequate post hand-over support. Robust post-commissioning operation should become a key criterion for sustainable building innovation, and this should be reflected in green awards and rating systems.

EVALUATION CRITERIA OF THE OFFICE BUILDINGS INTELLECTUALITY

The article defines the main criteria of office buildings intelligence, describes the stages of intelligent buildings formation, analyzes the profitability of the introduction of modern technical means (engineering systems) and architectural planning techniques in office buildings. At different stages of civilization, the concept of "technology" defines the path leading to future progress, and the rate of technology change is directly proportional to the rate of progress. Nowadays, artificial intelligence is extremely important for the functioning of modern office buildings, including the impact on the environment, resource conservation, safety, comfort and life support. According to research based on the works of scientists such as Derek Clements-Croome, Mervi Himannen, Akin Adejimi and others, and based on the analysis of intellectual buildings of the world from the 50s of the twentieth century to the present, 4 stages of intelligent buildings formation were identified. At the first stage (1950-1980) of the formation, separate controllers were introduced. At the second stage (1980-1995), the introduction of the "artificial intelligence" - Building Management System (BMS) - was developed and used. At the third stage (from 1995 to 2010) an intelligent building management system (IBMS) was introduced, which can independently identify threats, look for ways to achieve results and make decisions. Starting from 2010, we can highlight the fourth stage of development of the intelligent buildings, in which, in addition to the availability of the intelligent management system (IBMS), it became necessary to use approaches of sustainable architecture. The research results indicate that the office building should be classified as an "intelligent building" if it meets the following criteria: 1. Has artificial intelligence (IBMS), which autonomously manages the building; 2. Has at least 15 thousand information points, in other words, sensors and controllers, through which information is received from controlled engineering systems about the state of equipment and the environment, the state of building structures, etc.; 3. Complies with the principles of sustainable architecture, when planning and architectural techniques can minimize the negative impact of buildings on the environment through energy efficiency. The article proves that the office buildings intelligence is determined by the availability of both technological means and the optimal architectural concept, which minimize the negative impact of buildings on the environment; improve energy efficiency and conditions of the building exploitation. Therefore, further research of intelligent buildings from the point of architectural view is necessary, because a modern office building must be designed with the ability to adapt to rapid changes in technology and human needs.

Model of an Intelligent Building (House) Management System

Model of an Intelligent Building (House) Management System

Study Of The Evolutionary Development Of Automated Control Systems For Intelligent Buildings And A Description Of The Main Requirements For Wireless Technologies In ACSIB

The intelligence of a building goes hand in hand with advances in building technology that facilitate the integration of different subsystems within a single building and the information integration of a group of buildings. This is because the deeper the building systems are integrated, the greater the diversity, scope, and speed of data generation, the greater the potential for smart decision making. A modern engineering approach to building an Automated Intelligent Building Management System (ACSIB) aims to protect the building from future risks of scaling and reconfiguration problems by addressing communication and integration issues in advance. All this is done using the latest wireless technologies, open protocols, and ready-to-use Internet products available on the market. In parallel, data storage and archiving standards are being introduced to lay the foundations for a data-rich industry that can be used to increase building productivity. The most important trends in the development of automated building management systems are the miniaturization of sensors to increase their use and installation locations, reduce energy consumption, and ideally — the use of sensors not only wireless but also battery-free. Such sensors are powered by natural effects such as solar energy, Peltier effect, etc. As part of the review article, the author highlights the basic concepts and trends in the development of automated control systems for intelligent buildings (ACSIB), touches on the trend of combining ACSIB and the Internet of Things (BIoT) network. The article provides practical examples of the application of the latest approaches and solutions through protocols and technologies adopted in use. Also, the article presents general requirements for wireless technologies for automated control systems for intelligent buildings.

Exergy analysis of day light using fuzzy logic controllers of Jordanian commercial buildings

Saving energy and greenhouse gas emissions in commercial buildings become a very serious request of buildings’ designers and engineering crew. The goal behind the current research is to high light and demonstrate the outcome of utilizing advance and intelligent building management system (IBMS) based in utilizing a fuzzy logic controller that allows the usage and the control of natural light (day light). The fuzzy logic controller (FLC) was sat to control the buildings dimming system while utilizing natural light which normally allows to add outdoors illuminance into the inside ones. This control system is significant mean technique that can be used in intelligent buildings commercial buildings, hence energy savings and consequently, greenhouse gas emissions reduction. The proposed control system relies on enumerating the outdoor and the indoor illuminance and allowing some add-on controllers in order to put into effect a photometric computer that capable of comparing and tasking decisions to regulate rooms lighting fixtures. The article also demonstrates energy savings using this technique and then suggest proper markets for using this controlling system whether during the buildings’ design stage, existing commercial buildings.

Optimization of energy consumption and thermal comfort for intelligent building management system using genetic algorithm

This paper presents a design, simulation and performance evaluation of an optimized model for the Heating, Ventilation and Air-Conditioning (HVAC) systems using intelligent control algorithm. Fanger’s comfort method and genetic algorithms were used to obtain the optimal and initial values. The heat transmission coefficient between internal and external environments were determined depending on several inputs and factors acquired via supervisory control and data acquisition (SCADA) system sensors. The main feature of the real-time model is the prediction of the internal buildings environment, in order to control HVAC system for indoor environment and to utilize the optimum power consumed depending on optimized air temperature value. The predicted air temperature value and Predictive Mean Vote (PMV) value was applied using intelligent algorithm to obtain an optimal comfort level of the air temperature. The optimized air temperature value can be used in HVAC system controller to ensure that the temperature of indoor can reach a specific value after a known period of time. The use of genetic algorithm (GE) ensures that the used power is well below its peak value and maintains the comfort of the user’s environment.

A mixed activity-based costing and resource constraint optimal decision model for IoT-oriented intelligent building management system portfolios

Recent advances in the Internet of Things (IoT) have initiated the development of intelligent buildings. Intelligent buildings are expected to provide benefits such as high efficiency, energy-savings and smart services. IoT-oriented management systems play an important role in providing for the operational optimization of intelligent buildings. The main objective of the present study is to find the key determinants that provide an optimal portfolio of IoT-oriented Intelligent Building Management System (IBMS) adoptive strategies for decision-makers. In an IoT-oriented decision-making model, the IoT characteristics’ measurement for the evaluation and determination of management systems for intelligent building are incorporated through the MCDM method, and incorporates an Activity-Based Costing evaluation and resource constraints into Zero-One Goal Programming in the optimal portfolio selection process. The main results indicate that Disaster Prevention System and Energy Management System would be selected for the office building and Factory Environment Monitoring and Factory Energy Monitoring for the smart factory through the integrated decision model. Simultaneously, the result underlines the significant interrelation between Acts and Regulations and Ecosystem Value Chain to provide government policy making. The main contribution of this study is to provide a new decision model integrating activity-based costing and resource constraints into IBMS optimal portfolio selection.

Nonparametric user activity modelling and prediction

Modelling the occupancy of buildings, rooms or the usage of machines has many applications in varying fields, exemplified by the fairly recent emergence of smart, self-learning thermostats. Typically, the aim of such systems is to provide insight into user behaviour and incentivise energy savings or to automatically reduce consumption while maintaining user comfort. This paper presents a nonparametric user activity modelling algorithm, i.e. a Dirichlet process mixture model implemented by Gibbs sampling and the stick-breaking process, to infer the underlying patterns in user behaviour from the data. The technique deals with multiple activities, such as , of multiple users. Furthermore, it can also be used for modelling and predicting appliance usage (e.g. ). The algorithm is evaluated, both on cluster validity and predictive performance, using three case studies of varying complexity. The obtained results indicate that the method is able to properly assign the activity data into well-defined clusters. Moreover, the high prediction accuracy demonstrates that these clusters can be exploited to anticipate future behaviour, facilitating the development of intelligent building management systems.

Role of Information and Communication Technology in Economic Development of India

Information and Communication Technology (ICT) stresses the role of unified communication and integration of telecommunications, intelligent building management systems and audio-visual systems in modern information technology. In the information age, using information is a prime issue. ICT includes of all technical means that are used to handle information and aid communication. It includes computer and network hardware, middleware and necessary software. Time accessibility of relevant information is vital for economic development of any country. The improvement of economic activity can be brought by improving capacity in terms of enhancing access to information, while also the technical nature of economic activities required that the knowledge of users are constantly updated which can be achieved through their enhanced information seeking behavior by the use of ICTs. Moreover, use of ICTs lead to innovation which further lead to the enhancement of economic growth and human resource development. Thus, the present paper highlights the role of information technology on economic development of the country as a whole. A period of fifteen years is considered for the study Information and Communication Technology index and Economic Development index has been constructed on the basis of Principal Component Analysis. A simple linear regression has also been conducted to examine the impact of ICT on Economic Development in India. Stationarity of the data set are also checked with the help of appropriate unit root testing technique such as Augmented-Dickey Fuller test. The result shows that there has been considerable impact of ICT on Economic Development in India. Policy suggestions are also given on the basis of the findings of the study.

RETRACTED ARTICLE: Intelligent manufacturing model of construction industry based on Internet of Things technology

With the advent of the information age, the Internet of Things (IoTs) technology has been favored by many industries and became another in-depth revolution of information technology industry. Under such an era, the application of the IoTs technology to the construction of intelligent buildings can broaden the practicality of intelligent systems, optimize resource allocation, and enhance the management and service capabilities of intelligent buildings, thereby improving people’s quality of life. On the basis of summarizing and analyzing previous research works, this paper elaborates the development status and future challenges of the IoTs technology, analyzes the impact of IoTs technology on intelligent buildings and intelligent manufacturing, and expounds the integrated system framework of intelligent building and building information model (BIM) technology, and proposes the system design of intelligent building and the architecture model of intelligent manufacturing based on the IoTs technology. The model verification results show that the intelligent manufacturing model of construction industry can realize the integration of human society and physical system and achieve the purpose of real-time management and control of personnel, machines, equipment, and infrastructure within the whole network. The study results of this paper provide a reference for the further research on the intelligent manufacturing model of construction industry based on the IoTs technology.