Smart Buildings Research Articles

Microstrip patch antenna for energy harvesting in smart buildings

Microstrip patch antenna for energy harvesting in smart buildings

Low-voltage and high-contrast polymer dispersed liquid crystals enabled by ferroelectric fluoro-copolymer doped polyimide film

Smart windows incorporating polymer dispersed liquid crystal (PDLC) technology provide user-friendly operation and play a significant role in intelligent buildings and building energy conservation. Long standing issues of the PDLCs such as high driving voltage, low contrast ratio, and narrow viewing angle hinder their applications. Here PDLCs are sandwiched between the hybrid substrates composed of the ferroelectric polyvinylidene fluoride trifluoroethylene (P(VDF-TrFE)) copolymer and the homogeneous polyimide. The high dielectric constant and ion-trapping ability of ferroelectric copolymers increase the effective voltage drop in the PDLCs. Concurrently, the local electric field created by large dipole moment of ferroelectric copolymers triggers the alignment of liquid crystals (LCs) along the applied field. These synergistic effects contribute to a substantial decrease in the driving voltage of PDLCs. In the opaque state, the random dipole moments in ferroelectric copolymers disrupt the LC orientations near hybrid substrates, thereby increasing the light scattering and reducing the transmission of PDLCs. Consequently, the proposed PDLCs exhibit a contrast ratio ∼2× higher compared to the conventional PDLCs at normal incident. Moreover, the proposed PDLCs demonstrate a wider viewing angle in the transparent state compared to conventional PDLCs. This development promises energy-efficient, eco-friendly, and cost-effective smart windows.

An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting

An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting

Leveraging digital platforms to improve Public Health in hospital smart buildings

Abstract The integration of digital platforms into hospital infrastructure represents a significant leap forward in public health management. This session explores how smart hospital buildings, equipped with advanced digital technologies, can enhance public health outcomes through more effective disease surveillance, improved environmental controls and enhanced patient care management. Smart hospital buildings utilize a range of new technologies such as IoT sensors, AI-driven analytics and big data solutions to monitor and optimize various aspects of hospital operations and care. These technologies enable real-time data collection on environmental conditions, patient health metrics and resource utilization, creating an ecosystem that not only supports the immediate health needs of patients but also contributes to broader public health goals. The session will address four key areas where digital platforms can make an impact: -Disease Surveillance and Management: Digital tools can track health data trends and patterns within hospital settings, enabling early detection of infectious outbreaks and more precise tracking of patient outcomes. -Environmental Monitoring: IoT sensors can continuously assess conditions such as air quality, temperature and humidity, which are critical for preventing hospital-acquired infections and ensuring a safe environment for patients and staff. -Resource Optimization: AI algorithms can analyse data to optimize resource allocation, ensuring that medical supplies and human resources are used efficiently, which is crucial in health emergencies. -Patient-Centred Care: Digital platforms can facilitate personalized patient care through real-time health monitoring and data analysis, ensuring timely medical interventions and better health management. This session will highlight case studies demonstrating successful implementation of digital technologies in smart hospital buildings and discuss the challenges and lessons learned from these initiatives.

8.F. Scientific session: Promoting Public Health with healthy smart buildings

Abstract The Workshop titled “Promoting Public Health with healthy smart buildings” - proposed and developed by the three EUPHA Section: Urban Public Health (URB), Public Mental Health (MEN) and Environmental Health (ENV) - is aimed to foster the dialogue between designers (architects and urban planners), Public Health experts (operators, professionals and epidemiologists), policy/decision makers and buildings’ users, to establish a multidisciplinary approach for understanding together how to create and manage healthy living indoor environments (both housing and complex constructions/public buildings). According to the main Conference topic “Sailing the waves of European public health: exploring a sea of innovation”, the workshop mainly addresses both the “Climate emergency/Environment and health/Urban health” and the “Mental health” EPH24 conference topics. The workshop purpose is to explore the effects of the housing conditions, according to “umbrella” exposure of the quality of the buildings, stressing topics like emissions, qualities, functional features, accessibility, supportive, Indoor Environmental Quality, Indoor Air Quality, and health implications on users’ well-being. In fact, time spent at home and in the indoor environment increased due to the COVID-19 experience, and social-sanitary emergencies are expected to grow due to the urbanization phenomenon. Thus, the role of the physical environment in which we live, study, and work, has become of crucial importance, as the literature has recently highlighted. The workshop program includes inputs which they argue current experiences, emerging practices and scientific outcomes related to the healthy Buildings, coming from four different European countries. From Sustainable Development Goals, in “Healthy air for healthy minds”, to Indoor Environmental Quality implications, in “How to promote health in indoor living and working spaces: quali-quantitative analysis to evaluate the indoor air quality through users’ perception and low-cost sensors”. From experience-based works related to specific functions, in “Leveraging digital platforms to improve Public Health in hospital smart buildings”, to other specific public buildings’ experiences, in “Novi Sad cultural stations as healthy buildings for wellbeing concept”. Key messages • explore the relationship between housing conditions, Indoor Environmental Quality, and the implications on users’ well-being, like “umbrella” exposure of the quality of the buildings. • establish a multidisciplinary approach for understanding together how to create and manage healthy living indoor environments, at private and public scale.

Framework for Implementation of Building Automation Control Programs for Industrial Heating and Cooling Systems

This article proposes a novel framework for the rapid implementation of automation programs in industrial heating and cooling systems. The global push for sustainability necessitates significant infrastructural transformations within these systems, which currently rely heavily on fossil fuels and are responsible for 75% of industrial final energy consumption. Our research highlights the critical role of design patterns and object-oriented programming principles to address the complex integration of additional energy converters and storage into automation programs. By leveraging design patterns, our framework encapsulates the intricacies of various components, such as actuators, sensors, and storage, within a comprehensive object-oriented model that also allows the integration of different control strategies. Qualitatively, this approach enhances the reusability, scalability, and adaptability of automation programs. Therefore, quantitatively, our framework enables a more resilient and efficient energy system. The framework is validated through its application to a complex, cross-linked industrial heating and cooling system at the ETA Research Factory of the Technical University of Darmstadt. Using the developed framework reduces implementation effort significantly due to its consistent and modular structure resulting from the reusable design patterns.

Web Server Based Electrical Control System Analysis for Smart Buildings

Energy management in smart buildings still faces challenges in optimizing energy use, particularly for high-load devices, such as HVAC systems and lighting. Conventional control systems are often inadequate for optimizing energy usage based on the operational needs of the building. This study aims to develop and analyze a web server-based electrical energy control system that can be accessed in real time to improve the energy efficiency of smart buildings. This study employed a quasi-experimental method by implementing a web server-based control system in a smart building and comparing the energy consumption before and after the application of the system. The results show that the system reduces the energy consumption by up to 25%, particularly for HVAC systems and lighting. The most significant energy savings occurred during off-peak hours, when the system automatically reduced power for unnecessary devices. The implications of this research suggest that a web server-based control system not only enhances energy efficiency and reduces operational costs, but also provides greater flexibility in energy management through more adaptive and responsive remote control. This research contributes to the development of more sustainable energy management technologies for smart buildings, with wide potential applications in commercial and institutional building scenarios

Enhanced Hybrid U-Net Framework for Sophisticated Building Automation Extraction Utilizing Decay Matrix

Automatically extracting buildings from remote sensing imagery using deep learning techniques has become essential for various real-world applications. However, mainstream methods often encounter difficulties in accurately extracting and reconstructing fine-grained features due to the heterogeneity and scale variations in building appearances. To address these challenges, we propose LDFormer, an advanced building segmentation model based on linear decay. LDFormer introduces a multi-scale detail fusion bridge (MDFB), which dynamically integrates shallow features to enhance the representation of local details and capture fine-grained local features effectively. To improve global feature extraction, the model incorporates linear decay self-attention (LDSA) and depthwise large separable kernel multi-layer perceptron (DWLSK-MLP) optimizations in the decoder. Specifically, LDSA employs a linear decay matrix within the self-attention mechanism to address long-distance dependency issues, while DWLSK-MLP utilizes step-wise convolutions to achieve a large receptive field. The proposed method has been evaluated on the Massachusetts, Inria, and WHU building datasets, achieving IoU scores of 76.10%, 82.87%, and 91.86%, respectively. LDFormer demonstrates superior performance compared to existing state-of-the-art methods in building segmentation tasks, showcasing its significant potential for building automation extraction.

A weighted hybrid indoor positioning method based on path loss exponent estimation

With the rapid development of the Internet of Things (IoT), location-based services (LBS) have gained significant attention due to their widespread applications in daily life. This paper addresses the indoor target positioning problem in wireless sensor networks (WSNs). A weighted constrained linear least squares algorithm based on path loss exponent estimation (PLE-WCLLS) with received signal strength (RSS) and angle of arrival (AoA) hybrid measurements is proposed. To address the challenges of unknown transmission power and path loss exponent (PLE), the proposed method employs a linear least squares (LLS) estimation approach based on the ranging maximum likelihood (ML) estimation model to estimate both parameters. Subsequently, a confidence weight adjustment strategy is designed to reduce positioning errors. To handle the highly non-convex and nonlinear nature of the RSS/AoA hybrid optimization model, a linearization method based on Taylor series expansion is presented. Accurate target position estimation is achieved by solving a constrained quadratic programming problem. The effectiveness of the proposed algorithm is validated through numerical simulations and experimental evaluation in a real indoor environment. Compared to traditional positioning methods, the PLE-WCLLS algorithm improves positioning accuracy by 13.2%, and it performs exceptionally well even in scenarios with fewer sensor nodes. This gives it broad application prospects in areas such as IoT device management, personnel tracking in smart buildings, and asset localization in industrial automation.

Retraction Note: video analytics-based intelligent surveillance system for smart buildings

Retraction Note: video analytics-based intelligent surveillance system for smart buildings

Influence of Internet of Things in Smart Building: An Empirical Study

-Internet of Things has gained a ton of hype in a very short period and is being embraced by changed areas to achieve its potential by getting progressed level of mechanization the individual business-based exercises to receive the rewards. The business land is one such area that numerous nations are zeroing in on changing their structures and foundation to have the urban areas contributing towards to become as 'smart cities'. The smart building is the place where the schools, workplaces, private buildings coordinate IOT for improving the abilities and effectiveness of the structure foundation. The basic drivers that have empowered for keen structures in the commercial centres are meeting the objectives of corporate social obligation, income development and working on functional productivity. With the interest for sending of keen structures on the ascent, there have been many key patterns that have arisen making it a push to have changed change for digitalizing the business structures, homes, and so on through powerful keen arrangements. The paper basically investigates the job of IOT in smart building, the trends of smart building, and highlights the interests of public in IOT and smart building Key Words:Internet of Things (IOT), Smart Building, Smart City.

Incorporation of Artificial Intelligence in Enhancing Quality of Life in Smart Cities

Rapid urbanization and low residential resources in cities are serious issues that are making city life difficult day by day. The development of smart cities is becoming a need of the present era due to the swift increase in population and environmental issues globally. Smart cities are being introduced in different regions of the world with the incorporation of latest technologies. The incorporation of Artificial Intelligence (AI) is one of the tools that can be used in smart building and cities. AI technologies are transforming public safety, trash management, healthcare, traffic control, and resource management, making cities more sustainable, effective, and responsive to their citizens' demands. There are still lack of awareness in some areas of the world on the efficacy of smart building and construction that is impacting negatively on the economy and growth of those countries.; such as Pakistan is one of those countries that is facing serious challenges due to increased population, urban migration, and poor management of natural resources. The need of planning smart strategies for smart building is very crucial to manage population and housing issues. Smart buildings and cities provide unique and convenient facilities to its residents so that they can contribute positively towards the economy of country. This paper focuses at important areas where AI has the most effects in order to investigate how integrating AI improves quality of life in smart cities. The aim is to highlight artificial intelligence's contribution to improving urban operations, streamlining resource management, and advancing sustainability. Additionally, potential concerns about privacy, data security, and fair access will be discussed. In order to show how AI-driven innovations like predictive analytics, machine learning, and IoT-enabled systems are changing the urban environment, the study synthesizes existing research and real-world examples. The evaluation also covers how AI promotes smart government, tailored urban services, and citizen involvement. The conclusion emphasizes that although AI has great potential to improve the quality of life in smart cities, implementation of the technology must be done in a balanced way to prioritize inclusive policies and ethical concerns for the general welfare of residents.

Multi-Criteria Decision Analysis of Wireless Technologies in WPANs for IoT-Enabled Smart Buildings in Tourism

The increasing demand for energy-efficient and interconnected smart buildings, particularly in the tourism sector, has driven the adoption of advanced wireless technologies. IoT technologies are crucial in this evolution, improving modern buildings’ functionality and operational efficiency. This study investigates the utilization of various wireless technologies within Wireless Personal Area Networks (WPANs), including Bluetooth BLE 4.2, Bluetooth BLE 5.0, ZigBee, and Z-Wave, in smart buildings. A multiple-criteria decision-making (MCDM) approach, specifically the PIPRECIA-S model, was applied to evaluate these technologies based on criteria such as device connectivity, mobility, low energy consumption, scalability, flexibility, and interoperability. Simulations using the PIPRECIA-S model were conducted to assess technology performance across various real-world scenarios. The results indicate that ZigBee (0.2942) and Bluetooth BLE 5.0 (0.2602) provide superior performance in terms of energy efficiency and scalability, followed by Z-Wave (0.2550) and Bluetooth BLE 4.2 (0.1906). These findings provide decision-makers with data-driven recommendations for selecting the most suitable wireless technologies for smart buildings.

A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building

Short-term load forecasting is critical in managing energy production, domestic consumption, and grid integration. Short-term load forecasting is an essential tool for managing and controlling the processes required to make buildings smarter. Short-term load forecasting at this level of the network is extremely difficult due to the high uncertainty in load demand. This study presents a novel approach for predicting building loads using deep learning and non-intrusive load monitoring (NILM) techniques, aimed at enhancing smart building energy management. The methodology involves the integration of Recurrent Neural Networks (RNNs) to effectively capture temporal patterns in power consumption data, which are critical for accurate load forecasting. The model uses a feature matrix created from power signals to improve its forecast accuracy. The results show that the suggested model greatly outperforms standard models, with an overall accuracy of 97.2 %. The model's robustness is proven through comprehensive experiments, which demonstrate higher performance in short-term load forecasts. Furthermore, a comparison with existing models demonstrates the proposed approach's efficacy in recognizing and predicting load fluctuations, making it a useful tool for smart grid applications.

Collaborative design approach to identify research and innovation needs within the European smart building community.

Upgrading the level of smartness in buildings can significantly contribute to improve our quality and sustainability of living, through increased energy efficiency, optimised resource management, and improved air quality and comfort. However, the fragmented nature of the sector makes it challenging to identify what is technically, socially and economically achieved today in Europe in terms of building smartness, and what should be developed and financially supported tomorrow to accelerate the roll-out of smart and energy efficient buildings. This paper introduces the collaborative process developed to involve a large community of experts in detecting and formalising research and innovation gaps related to smart buildings. This process is composed of four successive steps: 1) a communication phase to invite volunteer experts to join the proposed task forces; 2) The organisation and facilitation of online brainstorming workshops to identify research & innovation (R&I) gaps; 3) the collective drafting of a white paper synthesising the brainstorming outcomes; and 4) an open consultation to collect additional external feedback before finalising the white paper. The collaborative process developed was tested over 18 months and implemented on 12 different topics relying on 27 brainstorming workshops. Building on the collective knowledge of 135 participants, it enabled identification a significant series of R&I gaps related to smart buildings. The collective sessions as well as the open consultation phases showed overall some clear convergence on the gaps identified. It can therefore be concluded the outcome of the collaborative process reached a consensus among the targeted innovation community. The feedback collected on the process, shows that the frequency, duration and attendance of the brainstorming workshops proposed were very relevant, while the selection of online participatory tools could still be improved. This process could be replicated in other frameworks where research and innovation gaps are sought for.

Transparent Photochromic Coatings for Smart Windows and Information Storage

AbstractOrganic–inorganic composite photochromic coatings receive significant attention in energy utilization and conservation due to their easy application and rapid photoresponse. However, their high sensitivity to moisture limits practical applications. This study fabricates organic–inorganic composite functional coatings using a simple one‐pot method, producing materials with excellent photoresponsive, water resistance, and thermal insulation. Unmodified phosphotungstic acid serves as the inorganic photochromic filler, while methacrylate derivatives act as the polymer matrix, resulting in coatings with high initial transparency (over 85%). After 5 min of UV exposure, the samples shift to nearly non‐transmittable states in the visible range and maintain color depth through 14 coloring‐erasing cycles. Unlike polyvinylpyrrolidone substrates, which absorb moisture and soften quickly, this coating has a static water contact angle of up to 91.7°, showing excellent water resistance. Additionally, the composite material provides effective heat insulation in simulated chamber experiments, lowering the temperature by 15 °C compared to untreated chambers. In summary, this composite coating, with its excellent thermal insulation, rapid light responsiveness, stable cycling performance, and outstanding waterproof capabilities, proves highly suitable for applications such as smart windows, information storage, and building energy efficiency.

State-of-the-art of mechanical properties of 3D printed concrete

The utilization of 3D printing in civil engineering has expedited the progression of intelligent construction and building industrialization. A plethora of studies in recent years have contributed to the advancement of 3D printed concrete technology. In contrast to traditional concrete construction techniques, 3D printed concrete technology offers additional benefits such as cost-effectiveness, reduced environmental impact, and a simpler process. The mechanical properties of 3D printed concrete are crucial for its application in building structures. This article provides a comprehensive review of the latest research endeavors in 3D printed concrete, covering both research and application. Firstly, this manuscript examines the size parameters and fabrication methods of the specimens utilized for mechanical performance tests. Subsequently, the mechanical properties of 3D printed concrete are described, and the effect of time-gap as a significant parameter on the performance of 3D printed concrete is discussed. Finally, the existing 3D printed concrete reinforcement technologies and their applications are summarized and categorized based on fibers and steel bars. Moreover, the key technical obstacles in the application of 3D printed concrete in building structures are identified. The ultimate objective of this article is to chart a course for the development and implementation of 3D printed concrete in building structures.

Assessing energy savings and visual comfort with PDLC-based smart window in an Istanbul office building: A case study

Lighting applications in architectural structures have a high share in energy consumption. Inefficient lighting applications and technologies will increase energy consumption and do not guarantee the visual comfort desired by users. Recognizing the importance of user comfort, this study takes a deeply user-centric approach in proposing feasible lighting control methods that can improve energy efficiency in smart and carbon-neutral future buildings and change daylight's benefits in favor of the user. Smart glass technology, which can adjust the color and transmittance of the light in response to external stimuli (applied voltage) and can be dynamically modulated, changing window and door characteristics, has high potential in the market for modern societies. This paper develops an innovative lighting control method to improve energy efficiency and visual comfort and reduce energy consumption. The daily performance of the proposed control methods regarding illuminance, uniformity factor, and energy consumption is experimentally analyzed in a test bench modeling an office environment with IG80 glass (for comparison), artificial lighting source (LED lamp), and polymer-dispersed liquid-crystal (PDLC)-based smart window. The research methodology involved a detailed analysis of the relationship between performance metrics and dimmer percentages and comparing the energy consumption performance of the lighting methods with other design parameters and technological advances. This algorithm prioritizes user comfort and considers the PDLC-based smart window light transmittance and LED lamp dimmer control depending on daylight. The results show that the proposed algorithm can increase energy savings by up to 22 %. While efficient lighting control is closely related to weather conditions and indoor specifications, it has similar energy consumption performance compared to research into design parameters and technological advances, eliminating further efforts.

Simultaneous Energy Optimization of Heating Systems by Multi-Zone Predictive Control—Application to a Residential Building

Climate change has made energy management a global priority. In France, the Grenelle Environment has set very ambitious progress targets for positive-energy buildings, particularly in terms of reducing and managing energy consumption. However, effective energy management in multi-zone buildings presents significant challenges, particularly when considering the inter-zone dynamics and heat transfer. This study examines multi-zone heating control, using a data-driven model for predictive indoor temperature modeling in intelligent buildings taking into account the influence of interconnected adjacent zones. The research methodology uses dynamic thermal simulation, parallel predictive models based on multiple linear regressions, and a multi-objective non-dominated sorting genetic algorithm II (NSGA-II) for the optimization process, which evaluates various generated heating strategies. This research introduces an approach to improve building energy efficiency by considering inter-zone dynamics and reducing heating-related energy consumption compared to a conventional heating strategy. By applying this model predictive control on a simulated case, a reduction in energy consumption due to heating is observed while respecting thermal comfort. This work contributes by implementing a method that independently controls temperatures in different building zones simultaneously while applying distinct constraints to each zone. This approach empowers occupants to manage heating consumption based on their preferences, ensuring personalized comfort. In addition, a comparison was made using a model that did not account for inter-zone interactions. This comparison demonstrates that incorporating these interactions into the predictive model enhances the effectiveness of the model predictive control approach. The multi-zone approach was also validated experimentally by using real experimental data, demonstrating significant reductions in energy consumption.

Smart Building, Nuisance Electrical Failure, Remote Monitoring and Fault Recovery System

Smart Buildings are the fastest growing buildings compared to conventional buildings and are expected to grow at a rate of 8.3% for the next decay. The global commercial building automation market was valued at US$32.96 billion in 2020 and is expected to reach US76.49 billion by the year 2031. The implementation of technological advancement, demand for energy-efficient construction, low operational cost, and occupant comfortability have helped Smart Building to grow. However, Smart Buildings are still facing issues, from various sensor network diversity, clear definitions of Smart Buildings, and continuous smartness during full or partial electrical internal fault occurrence. This indicates the building is smart as long as a continuous power supply is present. Thus, this research study investigated Smart Building's drawbacks and designed a circuit to be a solution to one of the drawbacks to rectify the issues. The design provides full monitoring for real and nuisance failures and enables the performance of fault recovery procedures remotely. The circuit interfaces with the safety Residual Current Devices (RCD) and the wireless smart Wi-Fi-controlled socket outlets. Food industries, hotels, and restaurants could be the beneficiaries of this circuit. This is due to cold stores and fridges, which can lead to significant production loss/reduction when failure time increases.