Development Of Smart Buildings Research Articles

A hardware-in-the-loop (HIL) testbed for cyber-physical energy systems in smart commercial buildings

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. This study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.

Smart building trend, role, and position: a systematic literature review

<p>Property growth, especially in high-rise buildings in developed and developing countries, has experienced a significant increase. The increasingly expensive resources and the development of information technology encourage the growth of smart buildings in almost all countries, especially in developed and developing countries, and become a trend. The Internet of things (IoT) is the main driver in the development of smart buildings. Property businesses are competing to adopt and implement smart buildings on their properties. This is further strengthened by the development of smart cities in almost all cities around the world, one of the criteria is a smart community. The purpose of implementing smart buildings is to make property management effective and efficient. Using the systematic literature review, this paper will discuss what components must be met for a property called a smart building, then what is the role of smart buildings for an area or community, and how the current smart building trend will be in the future.</p>

Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration.

An emerging reality is the development of smart buildings and cities, which improve residents' comfort. These environments employ multiple sensor networks, whose data must be acquired and processed in real time by multiple rule engines, which trigger events that enable specific actuators. The problem is how to handle those data in a scalable manner by using multiple processing instances to maximize the system throughput. This paper considers the types of sensors that are used in these scenarios and proposes a model for abstracting the information flow as a weighted dependency graph. Two parallel computing methods are then proposed for obtaining an efficient data flow: a variation of the parallel k-means clustering algorithm and a custom genetic algorithm. Simulation results show that the two proposed flow reconfiguration algorithms reduce the rule processing times and provide an efficient solution for increasing the scalability of the considered environment. Another aspect being discussed is using an open-source cloud solution to manage the system and how to use the two algorithms to increase efficiency. These methods allow for a seamless increase in the number of sensors in the environment by making smart use of the available resources.

Bilgi ve İletişim Teknolojilerinin Tasarımda Biçimlenişi : Akıllı Binalar

The advancement of information and communication technologies enables the widespread use of systems that prioritize user comfort, health, and safety. The development of the internet and computer technologies has enabled the use of new systems in architectural design and the construction of smart buildings. The increasing need for using resources efficiently, reducing living costs, and operating healthy, reliable, and environmentally friendly buildings have accelerated these developments. In this study, social and economic factors and the importance of information and communication technologies in the development of smart buildings are summarized. The main criteria in the evaluation of smart buildings are presented under the headings. Five smart buildings are examined from different locations in the world built after the year 2000. The common features of these smart buildings are evaluated by using information and communication technologies, design criteria, sustainability items, and their contributions to increasing the quality of life.

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

The readiness of IoT enabled Smart Buildings in Malaysia

The rapid evolution of the Internet of Things (IoT) has significantly impacted the way of conduct business and influenced current daily lifestyles. Observing this phenomenon, researchers and industry practitioners are increasingly paying attention to the research and development of smart buildings. This research aimed to focus on the readiness of smart buildings in Malaysia and research objectives were set to determine the readiness, potential benefits of implementing smart buildings and the challenges faced in the current state towards adopting smart buildings in Malaysia. Based on the 140 questionnaire survey responses from various construction professionals, descriptive statistics and Kruskal-Wallis test were conducted. This study has contributed to the body of knowledge on understanding on current readiness of Malaysia in the implementation of Smart Building and brought up the main challenges faced towards the adoption of Smart Building. In conclusion, Malaysia has not fully ready to take advantage of the benefits the smart revolution will bring, including greener, safer and more efficient energy use.

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.

Random Forest Learning Based Indoor Localization as an IoT Service for Smart Buildings

More buildings are becoming smart day by day which play a key role in development of smart cities and Internet of Things is essential in the development of such smart buildings. The incorporation of smart infrastructure is taken care right from the design phase of smart buildings itself. One of the crucial smart infrastructures in the development of smart buildings and smart cities is indoor localization. Being aware of the location or movement of people within a building can be very useful in several ways like energy management, location aware marketing services etc. and there are so many such IoT services. So the research problem here is to locate people within a building without using any additional infrastructure. Many research proposals have already been made over the last few years with the goal of predicting location in smart buildings but the real challenge lies with the accuracy of predicted location. User privacy and energy efficiency are also major challenges of indoor localization. Here we propose a random forest based machine learning algorithm that concentrates on improving the location accuracy in indoor localization as an IoT service for smart buildings. The obtained experimental results show 14% better success test prediction percentage in terms of overall deviation.

Smart Building Integration into a Smart City: Comparative Study of Real Estate Development

Smart buildings and smart cities are not the future perspectives anymore—the smart building integration into a smart city is an actual question for today and tomorrow. Development of smart buildings not only enhances the smart city concept but also promotes positivity to the urban development and national economy, and increases the quality of life of the whole population reacting to global challenges of sustainability. The innovative smart building and smart city technologies enable us to overcome these challenges by being employed through all real estate (RE) project development stages. The Evaluation Framework for Real Estate Development in Smart Cities created by the authors provides the possibility to assess the existing as well as to forecast future RE projects integration into a smart city during the whole life-cycle stage. The practical application of the presented evaluation framework was illustrated by the comparative case study. Based on the created smart building integration into a smart city evaluation framework for real estate development, 10 RE projects in Lithuania and over the world were assessed and rated by selected criteria relevant to different RE development stages. The evaluation results revealed that, especially at the design and construction stages, the existing intelligence of RE projects and/or cities is insufficient. Although real estate projects are technologically advanced as single entities, the integration into smart city networks is limited by interoperability capabilities of the cities or by different strategic goals settled by real estate developers.

Designing a Cyber-Physical System for Ambient Assisted Living: A Use-Case Analysis for Social Robot Navigation in Caregiving Centers.

The advances of the Internet of Things, robotics, and Artificial Intelligence, to give just a few examples, allow us to imagine promising results in the development of smart buildings in the near future. In the particular case of elderly care, there are new solutions that integrate systems that monitor variables associated with the health of each user or systems that facilitate physical or cognitive rehabilitation. In all these solutions, it is clear that these new environments, usually called Ambient Assisted Living (AAL), configure a Cyber-Physical System (CPS) that connects information from the physical world to the cyber-world with the primary objective of adding more intelligence to these environments. This article presents a CPS-AAL for caregiving centers, with the main novelty that includes a Socially Assistive Robot (SAR). The CPS-AAL presented in this work uses a digital twin world with the information acquired by all devices. The basis of this digital twin world is the CORTEX cognitive architecture, a set of software agents interacting through a Deep State Representation (DSR) that stored the shared information between them. The proposal is evaluated in a simulated environment with two use cases requiring interaction between the sensors and the SAR in a simulated caregiving center.

Construction of intelligent building sky-eye system based on multi-camera and speech recognition

The use of advanced science and technology to build a smart new community is the need of today’s social development. Smart communities use IoT technology and cloud computing technology to make life in the community more intelligent. Smart buildings can make better use of resources and can save energy and at the same time serve community residents efficiently and conveniently. The development of smart buildings has also led to the progress of smart cities. The degree of intelligence of the community is a manifestation of the level of development of smart cities. Based on the single-camera multi-target tracking system, due to the limitations of the camera’s field of view, it is often impossible to track the moving target, and it is difficult to solve the target occlusion problem in complex scenes. We can use multiple cameras to obtain the information of the target in different perspectives and use the corresponding relationship of the same target in different views to achieve continuous tracking of multiple targets. In this paper, we propose a smart building sky-eye system based on multi-camera recognition technology. The simulation results show that the proposed method has higher efficiency.

Developing a sustainable smart city framework for developing economies: An Indian context

The present research work is one of initial effort to develop a sustainable smart city (SSC) structural framework in developing economies especially in Indian context. To accomplish this objective, the present work employs a hybrid Best Worst Method (BWM) – Interpretive Structural Modelling (ISM) approach. In this work, various key enablers influencing SSC development are extracted through literature. The BWM technique is utilised to identify their intensity of influence and ISM is used to develop a structural framework for successfully executing the SSC development projects. Several major categories of enablers, such as energy and environmental, infrastructural, strategy and policy oriented, informational and technological, social and personal, and mobility possess strong influence on SSC development agenda. The results reveals that sustainable resources management, development of smart buildings, advanced research and development system, and intelligent transportation are the key enablers of the developed framework. The fuzzy MICMAC analysis further assists in computing the driving power and dependence of the enablers. This research work will be beneficial for policy makers and practitioners in understanding of smart city development and would certainly help to grow nations’ economy by generating higher employment, foreign investments, and developing superior technological advancements.

Role of Internet of Things (IoT) in Maximum Power Extraction from BIPV Modules: A Review for Developing Smart Zero Energy Buildings

Evolution of Building Integrated Photovoltaic's (BIPV) technology has resulted in the development of smart buildings as a clean source of electrical energy generation which can either be stand alone or grid connected systems. This paper briefs about the studies on the role of Internet of Things (IoT) in real-time controlling and monitoring of parameters like tilt angles, defected modules, shading effect in BIPV systems etc which decreases the dc power output. In addition, it also describes the use of IoT for maximum power extraction from BIPV modules present in rooftops and facades. Enabling IoT in BIPV installation makes the overall system cost-effective, energy efficient, reliable and can be operated remotely through android apps, smart phones, laptops, PCs, tablets etc via internet which is very easily accessible in recent times. IoT along with BIPV is showing a new direction towards smart generation from renewable energy resources like solar through PV integrated building or construction materials.

An operation optimization and decision framework for a building cluster with distributed energy systems

Driven by the development of smart buildings and smart grids, numerous of research has focused on developing optimal operation strategies for smart buildings with the aims of reducing energy consumption and cost, as well as improving the grid reliability. Unfortunately, most of the studies from smart building perspective only target on a single building with elaborated energy forecasting models. Few of them addresses the effects of multiple buildings on power grid operation. On the other hand, a few studies from smart grid area focus on multiple buildings and their influence on power grid, they usually, however, use simplified linear energy forecasting models, which are hard to guarantee the findings reflecting the cases in real fields. As a result, this research proposes to bridge this research gap, through developing and validating high fidelity energy forecasting models for a building cluster with multiple buildings and distributed energy systems, as well as creating a collaborative operation framework to determining the optimal operation strategies of this building cluster. The operation framework utilizes multi-objective optimizations to determine the operation strategies: building temperature setpoints, energy storage charging and discharging schedules, etc., using particle swarm optimization. Pareto curves for energy cost saving and thermal comfort maintaining are also derived with different thermal comfort requirements. The results from this study show that the developed building cluster collaborative operation framework is able to reduce the energy cost by 12.1–58.3% under different electricity pricing plans and thermal comfort requirements.

Data-driven Predictive Control of Micro Gas Turbine Combined Cooling Heating and Power system

Abstract: Micro gas turbine-based combined cooling, heating and power (MGT-CCHP) system is in an important direction toward the development of smart buildings and district energy systems, which provides a clean, highly efficient and reliable means of producing energy for multiple use. However, the control of MGT-CCHP system is a challenge, due to its behavior such as large thermal inertia, and strong coupling among multi-variables. For this reasons, this paper develops a data-driven predictive controller for the MGT-CCHP system to improve its operating performance. The technique of subspace identification is utilized to construct the predictor directly from the input-output data, which can be used to estimate the future behavior of the system. The predictive controller is then designed to regulate the multi-variable MGT-CCHP system under the input-constraints. The effectiveness of the proposed control approach is demonstrated through simulation results on an 80kw MGT-CCHP simulator.