Smart Buildings Research Articles

Application of Intelligent Building Design Combining CAD Technology and Machine Learning Models

Application of Intelligent Building Design Combining CAD Technology and Machine Learning Models

"Research on the Application of Visible Light Communication in IoT System Configuration"

The growing need for seamless connectivity on the internet of things (IoT) paradigm has fueled the development of innovative technologies. This research deeply focuses on the integration of visible light communications (VLC) to configure the wireless sensor network (WSN) nodes in an IoT system. VLC, an emerging technology that uses visible light to transmit data, is not limited in frequency and maximizes security in wireless data transmission. Furthermore, due to the use of light-emitting diodes (LEDs) in almost every aspect of our daily lives, VLC is providing massive connectivity for various types of large-scale IoT communications, from machine to machine, vehicle to infrastructure, infrastructure-to-vehicle, chip-to-chip as well as device to device. This article presents an investigation of the multifaceted applications of VLC, especially its role in configuring wireless fidelity Wi-Fi connectivity for smart devices and their location in the system within the IoT framework. In addition to conventional data transmission capabilities, VLC has emerged as a key technology for transmitting configuration data and location data, thereby supporting an accurate positioning system. This study shows the effective interaction between VLC and IoT, shedding light on the applications of VLC in IoT systems, especially in smart building

Surrogate-assisted multi-objective evolutionary feature selection of generation-based fixed evolution control for time series forecasting with LSTM networks

Surrogate-assisted multi-objective evolutionary algorithms are powerful techniques to solve computationally expensive multi-objective optimization problems. In this paper, we propose a direct fitness replacement method with generation-based fixed evolution control to implement a multi-objective evolutionary algorithm that uses a surrogate model for wrapper-type feature selection, where long short-term memory is established as the learning algorithm. The importance of the work and its benefits lie in the need to reduce the excessive computational time required by conventional wrapper-type feature selection methods based on multi-objective evolutionary algorithms and LSTM networks, maintaining or improving the predictive capacity of the models. We analyze the use of incremental learning to update the surrogate model, in comparison with the conventional non-incremental learning approach. We applied these methods in real-life time series forecasting of air quality, indoor temperature in a smart building and oil temperature in electricity transformers. Multi-step ahead predictions of the forecast models obtained with different meta-learners of the surrogate model were compared by using the Diebold–Mariano statistical test on a multi-criteria performance metric. The proposed method outperformed other approaches for feature selection including, among others, methods based on surrogate-assisted multi-objective evolutionary algorithms developed by the authors in previous research, other surrogate-assisted deterministic methods for feature selection and the conventional wrapper-type feature selection method based on LSTM, improving the prediction on test dataset by 23.98%, 34.61% and 13.77%, respectively.

Impact of Gamified Learning Using Smart Building Blocks on Early Childhood Development

Impact of Gamified Learning Using Smart Building Blocks on Early Childhood Development

Demand side management strategy for smart building using multi-objective hybrid optimization technique

This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %.

Integration of Smart Home and Building Automation Systems in Virtual Reality and Robotics-Based Technological Environment for Neurorehabilitation: A Pilot Study Protocol.

Technological innovation has revolutionized healthcare, particularly in neurological rehabilitation, where it has been used to address chronic conditions. Smart home and building automation (SH&BA) technologies offer promising solutions for managing chronic disabilities associated with such conditions. This single group, pre-post longitudinal pilot study, part of the H2020 HosmartAI project, aims to explore the integration of smart home technologies into neurorehabilitation. Eighty subjects will be enrolled from IRCCS San Camillo Hospital (Venice, Italy) and will receive rehabilitation treatment through virtual reality (VR) and robotics devices for 15 h per day, 5 days a week for 3 weeks in the HosmartAI Room (HR), equipped with SH&BA devices measuring the environment. The study seeks to optimize patient outcomes and refine rehabilitation practices. Findings will be disseminated through peer-reviewed publications and scientific meetings, contributing to advancements in neurological rehabilitation and guiding future research.

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building’s energy consumption.

Smart Building Material with Near‐Infrared‐Triggered Reversible Color Switching

Smart materials and devices play an increasingly important role in improving modern life due to their richer functions, enhanced controllability, and improved biocompatibility. Herein, calcium silicate hydration (C–S–H) is integrated with layered [Co(NCS)2(pyrazine)2]n metal–organic frameworks (Co–MOF) and polymeric skeleton to fabricate the smart building material (M–C–S–H). C–S–H was anchored on the polymeric skeleton to reinforce the network structure for enhanced mechanical strength and toughness. Simultaneously, the highly porous skeleton structure enables the uniform distribution of layered Co–MOF, which could maintain its molecular form to endow the M–C–S–H with the photoresponsive color‐switching performance. The M–C–S–H as a smart building material exhibits the color change between light blue and black blue upon near‐infrared illumination on/off within a wide temperature range to achieve personalized wall and building decoration. Taking advantage of the low cost, rapid response, outstanding reversibility, and good color stability, M–C–S–H could provide lively, dynamic, and changeable decorating effects for walls and buildings.

Research on the application of BIM technology in intelligent building technology

With the continuous progress of society and science, building technology is also developing in the direction of intelligence, and the introduction of BIM technology has greatly promoted the development of intelligent buildings in China. The application of BIM technology in the field of intelligent construction is of great significance. It optimises project quality management, accelerates the building delivery process, and improves the operation and maintenance management efficiency of buildings. It is foreseeable that with the arrival of the intelligent era, BIM technology will continue to play an important role and bring more opportunities and challenges to the construction industry. Relevant practitioners should also actively learn and apply this technology to promote the development of the construction industry. This paper is based on a number of typical engineering cases, analyses a large number of existing literature, and reads various working reports and policies issued by the government. It introduces the role of BIM technology in project quality management, intelligent construction delivery and operation and maintenance management, with a view to providing reference for relevant practitioners.

Research on application of multimodal data fusion in intelligent building environment perception

With the rapid development of the building industry, intelligent buildings benefit from its safety, energy saving, environmental protection and integration and other advantages have been widely loved by people, most operators also realize the importance of intelligent buildings to bring people humanized and customized services, and in order to realize the personalized service of the building, multi-modal data fusion is an effective method. On the other hand, in today’s Internet of Things society, many practical applications need to deploy a large number of sensing equipment for data collection and processing, so as to carry out high-quality monitoring of the physical world, but due to the inherent limitations of these hardware equipment and the influence of factors such as the environment, single mode data often cannot be completely and comprehensively monitored to the physical world’s changing characteristics. In this development context, multi-modal data fusion has become a research hotspot in the field of machine learning. Based on this, this paper proposes a one-stage fast object detection model with multi-level fusion of multi-modal features and end-to-end characteristics for building indoor environment perception, and conducts experimental analysis on the performance of the model. The verification results show that the accuracy of the proposed method is 50.7% and the running speed is 0.107 s, which has better performance than the existing detection methods.

A two-step identification framework for building thermal dynamic based on high fluctuation performance data: A case of office with variable refrigerant flow system

Thermal dynamics model plays a crucial role of advanced control in the intelligent building. Grey box method is widely used in the modeling of building thermal dynamics based on the operation data of air-conditioner. The performance of direct-expansion (DX) system fluctuated severely during its operation, which leads to the difficulties in identification and evaluation of the building thermal model. In this paper, a two-step identification framework for second-order thermal resistance-capacity network of building based on DX system data is proposed. A case study is conducted on an office building equipped with variable refrigerant flow system in Foshan, China. The results show that the proposed framework can identify the parameters rationally. The model achieves high accuracy for both indoor temperature (Mean absolute errors of 0.74°C) and cooling load prediction (Mean absolute errors of 4453.86W) in a short time interval of 10 minutes. Additionally, the best complexity of the grey-box model for case building and the data acquisition interval is determined. Finally, an improved set of evaluation metrics for thermal load evaluation is proposed and discussed.

The Application of Intelligent Architectural Design in Energy Saving and Sustainable Development

In today's context, energy conservation, emissions reduction, and sustainable development are highly prioritized, and the architectural industry must recognize the value of intelligent buildings to maintain stable growth amidst fierce competition. Intelligent design not only enhances the energy efficiency of buildings, achieving energy savings and emissions reductions, but also significantly improves their safety and convenience, providing a more comfortable living environment for residents. Moreover, it is a key force driving technological innovation in the architecture industry, leading it towards more efficient, environmentally friendly, and sustainable development. In terms of application principles, intelligent architectural design emphasizes coordination, integrity, and flexibility. It requires considering the harmonious unity between architecture and the environment, ecosystems, and human needs during the design process, treating the building as an integrated system, and focusing on the flexibility and adjustability of the design to accommodate potential future changes. In specific applications, intelligent designs for natural lighting, ventilation, and electrical systems are important manifestations of intelligent architectural design. These applications, through intelligent control systems and devices, achieve efficient management and adjustment of the building environment, thereby enhancing the building's energy efficiency and contributing to sustainable development.

IoT based Efficient and Secure Building Architecture with Constrained Application Protocol (CoAP)

The Internet of Things (IoT), one of the newest technologies, has great potential for many industry businesses. Smart building technology is one of the biggest IoT industries. IoT is used on a regular basis in many different industries. It is used in a variety of contexts, including physical defense, e-health, smart buildings, grids, cities, homes, asset management, and transportation management. IoT-level, networked smart buildings offer affordable assistance. Comfort, accessibility, security, and energy management are necessary for commercial space. Naturally, IoT-based solutions can satisfy these requirements. With the depletion of the energy supply and the increase in demand for energy, there has been a growing focus on energy usage and building maintenance. This paper uses emerging IoT technology to demonstrate a smart building design that is both safe and energy-efficient. Constrained Application Protocol (CoAP) is a crucial internet transmission protocol that assigns a unique address to every device. Data is transferred using this application layer protocol outside of secure channels. While there are several recommended techniques to protect sensitive data transfer, one likely way to improve the security aspect of CoAP for authentication and end-to-end security is through the use of Datagram Transport Layer Protection (DTLS).This paper propose a smart building design in which the Internet of Things is used to control the operation of all technological components. The simulation results show that compared to the situation of employing Message Queuing Telemetry Transport (MQTT), adopting CoAP in a smart building saves energy use by around 34.86%. In order to increase security in a smart building, this article also explains how to include the DTLS protocol inside of CoAPutilising enhancements from the Certificate Authority (CA).Finally, using the ContikiCooja (CC) simulator to simulate system performance and data collecting in terms of latency, throughput, and resource consumption, CoAP is utilized to modify orderer and peer nodes for performance research in smart building.

Dynamic numerical modeling and performance optimization of solar and wind assisted combined heat and power system coupled with battery storage and sophisticated control framework

Incorporating distributed renewable energy sources into heating, power, and cooling systems is facilitating the drive toward intelligent building energy solutions. However, the inherent uncertainties associated with controllable renewable resources pose challenges for the thermo-economic scheduling of smart buildings. Nonetheless, the flexibility inherent in smart buildings can be leveraged through various market mechanisms. Hence, this research introduces a sustainable energy-building system driven by an autonomous solar/dish Stirling engine (SDSE) and wind turbine combined with a sophisticated control strategy and battery storage, which is designed to provide heat and power requirements. The proposed control strategy is also devised to optimize energy generation, ensuring prudent conservation and minimal waste, thereby amplifying overall efficiency and financial viability. The meticulous design of the system is accomplished through advanced MATLAB/Simulink® modeling. A thorough technical sensitivity analysis meticulously hones design parameters, revealing optimal operational thresholds. Simulation outcomes unveil a consistent Stirling engine (SE) efficiency, achieving a pinnacle of 35 %, while the SDSE attains over 28 %, respectively. The horizontal axis wind turbine, encompassing 100 kW and 500 kW modules, demonstrates power coefficients spanning from 0.18 to 0.09, with corresponding land area requirements ranging from 4.22 m2 to 21.10 m2, emphasizing the pivotal roles of module power and land area optimization. This paper also casts a spotlight on the environmental repercussions of the system, illustrating its potential to avert up to 30,000 kg of CO2 emissions per kWh/year. Moreover, the levelized cost of electricity ranges from 0.13 to 0.16 $/kWh, accompanied by an hourly cost that fluctuates between 3 $/h and 40 $/h, respectively. Conclusively, the developed modeling can be regarded as a significant stride in the realm of hybrid renewable energy systems, replacing the conventional photovoltaic/wind models with cutting-edge solar/wind configurations managed by a sophisticated control strategy and battery storage system.

AR based Indoor Navigation System

Indoor navigation within college campuses poses unique challenges due to complex building layouts and varying levels of accessibility. This paper presents a student project aimed at developing an Augmented Reality based indoor navigation system (AR-INS) specifically tailored for college campuses. The system integrates computer vision, depth perception and Google’s ARCore techniques to provide users with real-time navigation guidance using their smartphones or AR-enabled devices. We describe the system architecture, implementation details, and evaluation results from user testing conducted on our college campus. The project highlights the feasibility of utilizing AR technology for enhancing campus navigation experiences and provides valuable insights for future research and development in this domain. This project addresses the growing need for efficient indoor navigation, which can significantly improve the overall user experience and accessibility in indoor spaces. The AR-INS is not only user- friendly but also adaptable to various environments, making it a valuable addition to smart building infrastructure and enhancing the quality of life for users. The implementation of this system can have an impact on how people navigate complex indoor spaces, and give an solution for complex indoor spaces. Keywords—AR-INS, computer vision, depth perception Google’s ARCore

Valuation of Internet of Energy (IoE) Platforms in Smart Cities: A Hybrid Multi-Criteria Decision Making Approach

A smart city is a place where people live using computing, sensing, and networking technology in a variety of ways, including smart buildings, smart homes, smart transportation, smart workplaces, smart offices, smart industries, and smart agriculture. These smart technologies need to connect so; we need to use the Internet of Things (IoT) that has applications in many areas of our smart lives. IoT has been used due to the growing usage of renewable energy sources and the challenges of network management. Internet of Energy (IoE) integrates the IoT and smart grid capabilities. The basic role of smart grids or IoE is an essential element in creating smart cities. In this work, we present some IoE platforms that represent the alternatives and their criteria. The proposed study treats the uncertainty of multi-criteria decision-making (MCDM) modified by single value triangular neutrosophic number (SVTrN) by choosing the optimal IoE platform based on the criteria importance through inter-criteria correlation (CRITIC) method and multi-attributive ideal-real comparative analysis (MAIRCA). The proposed model proved its effectiveness by applying it to an experimental case. The results show that the DCC (A3) platform is the best one.

Ultralight Three-Layer Gradient-Structured MXene/ Reduced Graphene Oxide Composite Aerogels with Broadband Microwave Absorption and Dynamic Infrared Camouflage.

Infrared and radar detectors posed substantial challenges to weapon equipment and personnel due to their continuous surveillance and reconnaissance capabilities. Traditional single-band stealth devices are insufficient for dual-band detection in both infrared and microwave bands. To overcome this limitation, a gradient-structured MXene/reduced graphene oxide (rGO) composite aerogel (GMXrGA) is fabricated through a two-step bidirectional freeze casting process, followed by freeze-drying and thermal annealing. GMXrGAexhibits a distinct three-layered structure, with each layer playing a crucial role in microwave absorption. This deliberate design amplifies both the efficiency of microwave absorption and the material's effectiveness in dynamic infrared camouflage. GMXrGA displays an ultralow density of 5.2 mg∙cm-3 and demonstrates exceptional resistance to compression, enduring 200 cycles at a maximum strain of 80%. Moreover, it shows superior microwave absorption performance, with a minimum reflection loss (RLmin) of -60.1dB at a broad effective absorption bandwidth (EAB) of 14.1GHz (3.9-18.0GHz). Additionally, the aerogel exhibits low thermal conductivity (≈26 mW∙m-1∙K-1) and displays dynamic infrared camouflage capabilities within the temperature range of 50-120°C, achieving rapid concealment within 30 s. Consequently, they hold great potential for diverse applications, including intelligent buildings, wearable electronics, and weapon equipment.

Application of Trustworthy IoT Technology in Fast Communities: Smart Access Control

As a response to the current development status and existing problems in the ubiquitous and trustworthy aspect of the Internet of Things (IoT) in the construction of smart community systems in Bangladesh, this paper presents the trustworthy technology strategy of the ubiquitous and trustworthy IoT intelligent system. Additionally, it provides a list of the demonstration application practice of trustworthy IoT technology at the level of smart communities through three different application scenario project construction cases. These cases include smart access control, smart buildings' construction, and smart parks' construction.

Deterrents to the IoT for smart buildings and infrastructure development: A partial least square modeling approach

Implementing Internet of things (IoT) technology in the context of intelligent buildings and infrastructure development has garnered significant attention within the construction sector. Nonetheless, the implementation of IoT could be improved by assessing various barriers. The purpose of this study was to examine the obstacles related to the adaptation of IoT techniques within the construction sector, as well as the effects on the advancement of intelligent building and infrastructure systems. The study employed a mixed-method approach involving exploratory factor analysis (EFA) and structural equation modeling (SEM) to determine six types of barriers: knowledge, technical, standardization, creativity, complexity, and economics. The study revealed that the implementation of IoT for developing smart construction and infrastructure in the construction sector was significantly influenced by all six constructs. The results of this study offer significant ramifications for the field. The study underscores the necessity for heightened consciousness and instruction regarding the advantages of implementing IoT. The study posits that the technical barriers, including interoperability, modernization of legacy infrastructure, and coordination and collaboration difficulties, require attention from the industry. The study highlights the significance of establishing industry-wide standards and protocols for implementing IoT and regulatory and legal frameworks. Finally, the study underscores the necessity for augmented funding and financing options for IoT endeavors. Subsequent study endeavors may expand upon the present findings by delving into the barriers encountered by alternative sectors and nations and assessing the efficacy of the suggested measures in this investigation.

An Optimal Regime of Energy Management for Smart Building Clusters With Electric Vehicles

An Optimal Regime of Energy Management for Smart Building Clusters With Electric Vehicles