Smart Buildings Research Articles

Enhancing Energy Efficiency With Smart Building Energy Management System Using Machine Learning and IOT

Enhancing Energy Efficiency With Smart Building Energy Management System Using Machine Learning and IOT

On-device Online Learning and Semantic Management of TinyML Systems

Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning (ML). While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This study aims to bridge the gap between prototyping single TinyML models and developing reliable TinyML systems in production: (1) Embedded devices operate in dynamically changing conditions. Existing TinyML solutions primarily focus on inference, with models trained offline on powerful machines and deployed as static objects. However, static models may underperform in the real world due to evolving input data distributions. We propose online learning to enable training on constrained devices, adapting local models toward the latest field conditions. (2) Nevertheless, current on-device learning methods struggle with heterogeneous deployment conditions and the scarcity of labeled data when applied across numerous devices. We introduce federated meta-learning incorporating online learning to enhance model generalization, facilitating rapid learning. This approach ensures optimal performance among distributed devices by knowledge sharing. (3) Moreover, TinyML’s pivotal advantage is widespread adoption. Embedded devices and TinyML models prioritize extreme efficiency, leading to diverse characteristics ranging from memory and sensors to model architectures. Given their diversity and non-standardized representations, managing these resources becomes challenging as TinyML systems scale up. We present semantic management for the joint management of models and devices at scale. We demonstrate our methods through a basic regression example and then assess them in three real-world TinyML applications: handwritten character image classification, keyword audio classification, and smart building presence detection. The results confirm the effectiveness of our approaches from various perspectives, such as accuracy improvement, resource savings, and engineering effort reduction.

Data-Driven and Domain Adaption Framework for Optimizing Energy Usage Forecasting in Smart Buildings Using ARIMA Model

Building energy demand and energy system supply are increasingly balanced by energy storage and short-term DSM. This is necessary due to fluctuating renewable energy supplies and rising building power use. Worldwide, structures utilize tons of energy. Greenhouse gas emissions and operational expenses decrease with construction energy efficiency. Forecasting and optimization algorithms can solve challenges, including supply chains (inventory optimization), traffic, and sustainable energy system battery/load/production scheduling for carbon-free energy generation. We often solve optimization problems that need forecasting due to uncertain future values. Predicting and optimizing are challenging; therefore, little research has been done. Our method uses building energy modeling professionals' data to forecast neighboring building types for new or unknown building types. After training, we utilize the models to estimate energy usage for the k-closest building types and combine the predictions using a weighted average. We used time-series decomposition to detect uncertainty and a hybrid model to close this gap: The concepts encode static features and predictable patterns in time-series simulation results. The model learns latent performance differences and calibrates output using outcomes and history records. Historical data predicts public building energy ARIMA use. Our method covers data processing, training, validation, and forecasting. We measured our method. Mixed integer linear optimization and ARIMA projected most accurately.

Optimal layout scheme of fire detectors and operation condition monitoring technology in urban integrated substation

Aiming at the problem that it is difficult to obtain the life cycle law and detection coverage of fire detectors and the low reliability of fire alarm process in urban integrated substation, an optimal layout scheme and operation condition monitoring technology of fire detectors based on local outlier factor and neural network are proposed. Divide fire zones by equipment functions, calculate the number of fire detectors required by using ground area and indoor height, and integrate building automation system to create substation fire alarm system; The signal noise of fire detector equipment is eliminated by wavelet transform, the fuzzy characteristic analysis model of fire detector operation state is established, the local outlier factor is simulated as abnormal data, the past operation data is trained by BP(Back propagation) neural network, the difference between the current state and the normal operation state is obtained, and the detector operation state monitoring is completed. The operation reliability of fire detector is analyzed from the aspects of temperature and smoke perception. The simulation results show that the proposed technology can successfully obtain the life cycle law and detection coverage of fire detector, and ensure the safe use of urban integrated substation. In addition, through a series of experiments, the feasibility and effectiveness of this technology in practical applications are verified.The maximum time consumption of detector working condition monitoring in this method is only 3.3 s. The fitting degree of the method in this paper is 5.7 ml/m3, and the fitting degree of the actual smoke curve is 5.7 ml/m3. The temperature of the method in this paper is lower than 33 °C. In the future development, the introduction of more advanced self-diagnosis technology can be real-time monitoring and analysis of the status of the detector itself, timely detection of faults or anomalies, and automatic alarm or notification of maintenance personnel for repair and maintenance.

The Main Barriers Limiting the Development of Smart Buildings

Smart buildings play a key role in the complex ecosystem of cities and are often subject to barriers that limit their development. Although identifying these barriers is fundamental to creating an enabling environment for this segment’s expansion, few works aim to identify these challenges. This work has two main objectives: (1) to research the main barriers limiting the development of new smart building projects and (2) to prioritize these barriers from the perspective of professionals with experience in the field. We adopted an exploratory approach common in research that focuses on identifying and prioritizing variables related to a phenomenon, which is based on two main actions: obtaining information through a careful literature review and consulting professionals who work in the concerned field. The results showed that professionals assessed the 23 barriers identified through bibliographic research as important, with the most important being related to lack of qualified professionals, shortage of government policies, higher initial and construction costs, macroeconomic barriers and access to financing, high cost of intelligent systems and technologies, regulatory barriers, lack of knowledge about the current and potential benefits of smart buildings, and more complex design and construction.

Occupancy Estimation from Blurred Video: A Multifaceted Approach with Privacy Consideration

Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings’ energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants’ privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants’ privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants’ visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants’ visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system.

An ontology for automated fault detection & diagnostics of HVAC using BIM and machine learning concepts

This paper presents an ontology for AFDD (Automated Fault Detection and Diagnostics) of HVAC (Heating, Ventilation, and Air conditioning) systems in buildings called “AFDDOnto”. Presently, the AFDD models are mainly data-centric and often lack semantic information such as contextual information and spatial information; additionally, configuration information, analysis, and results used for model development are lost once developed. This impedes an effective mechanism for tracking changes and updating the model for future developments and use cases. The Proposed ontology can be used for AFDD model development, tracking changes, analytics, visualization, and digital twinning by enabling integration of BIM with BAS/BMS (Building Automation System/Building Management System) concepts and secondly to store AFDD configuration and analytics in the AFDDOnto. Select competency questions are constructed using SPARQL queries to access the proposed knowledge model. The proposed ontology has been tested against different measures using multiple metrics and a case study and further validated using a semi-structured survey of experts. Applied AI engineers, Facility managers, Asset managers, and building owners aiming to develop AFDD models for HVAC systems can benefit from adopting this ontology for HVAC maintenance, including analysis, model development, and knowledge management.

Development of sustainable conductive cementitious composite using graphite-coated spent catalyst waste

This study presents an innovative approach to developing sustainable conductive composites by coating graphite onto the surface of spent catalyst waste through nano-surface engineering techniques. The process ensures uniform adsorption of graphite onto the surface of the spent catalyst waste particles, followed by oven treatment and milling. This results in better integrity and effective bonding, leading to the production of graphite-coated spent catalyst waste (G-SCW). Scanning electron microscopy indicates the successful coating of spent catalyst waste with graphite. The research investigates the effect of G-SCW on the cementitious properties of paste and mortar. Incorporating G-SCW results in acceptable workability and setting time, while the compressive strength increases at early and later stages, with up to 20 % G-SCW content. The addition of G-SCW in the mortar significantly reduces the electrical resistivity, resulting in a 63 % reduction in resistivity compared to the reference mix, thereby enhancing the conductivity. Hydration studies confirm the presence of pozzolanic reaction in blended paste, as evidenced by a decrease in calcium hydroxide content. The sustainability assessment indicates a substantial reduction in embodied carbon and possibly producing mortar with lower cement content. These findings suggest great potential for developing sustainable conductive mortar with G-SCW, enabling smart building construction, and supporting sensor networks for structural health monitoring.

Detection and Determination of User Position Using Radio Tomography with Optimal Energy Consumption of Measuring Devices in Smart Buildings

The main objective of the research presented in the following work was the adaptation of reflection-radar technology in a detection and navigation system using radio-tomographic imaging techniques. As key aspects of this work, the energy optimization of high-frequency transmitters can be considered for use inside buildings while maintaining user safety. The resulting building monitoring and control system using a network of intelligent sensors supported by artificial intelligence algorithms, such as logistic regression or neural networks, should be considered an outcome. This paper discusses the methodology for extracting information from signal echoes and how they were transported and aggregated. The data extracted in this way were used to support user navigation through a building, optimize energy based on presence information, and increase the facility’s overall security level. A band from 5 GHz to 6 GHz was chosen as the carrier frequency of the signals, representing a compromise between energy expenditure, range, and the properties of wave behavior in contact with different types of matter. The system includes proprietary hardware solutions that allow parameters to be adjusted over the entire range and guarantee adaptation for RTI (radio tomography imaging) technology.

Comparative Analysis of the Impact of Advanced Information Technologies on the International Real Estate Market

This study examines the impact of advanced information technologies, such as big data, the Internet of Things (IoT), and artificial intelligence (AI), on the global real estate market. Through comparative analysis of real estate markets in North America, Europe, and Asia, and specific case studies from the US, UK, and China, we quantified the effectiveness of these technologies from 2018 to 2023. Our findings show that big data analytics improved market forecasting accuracy by 15.2%, IoT in smart buildings reduced energy consumption by 10.3% and enhanced security by 17.5%, and AI in property management increased operational efficiency by 17.6% and reduced maintenance costs by 13.2%. North America showed the highest technology acceptance, with a 20%-30% improvement in market efficiency, followed by Europe at 15%-25%, and Asia at 10%-20%. The successful application of these technologies depends on policy environment, market acceptance, and enterprise management. This research offers valuable insights and quantitative evidence for policymakers, industry practitioners, and researchers, highlighting the need for future studies on the integrated effects and long-term impacts of technology convergence.

A Review of Machine Learning

The ever-growing complexity of engineering systems and the vast amount of data generated necessitate advanced data analysis techniques like machine learning (ML). This review explores the motivations and advantages of integrating ML into real-world engineering applications. The paper highlights the limitations of traditional deterministic models in handling intricate interactions within modern engineering systems. ML offers significant benefits including improved efficiency, enhanced decision-making, advanced automation and control, adaptability, and cost savings. Real-world applications across various disciplines are explored, including image recognition and computer vision in self-driving cars, predictive maintenance for optimizing equipment lifespans, structural health monitoring for identifying potential damage, and signal processing for control systems in airplanes and traffic lights. The review concludes by summarizing key findings from case studies on autonomous vehicle navigation, smart grid optimization, wind turbine fault detection, and HVAC system optimization in smart buildings. These case studies showcase the power of machine learning techniques like deep learning, reinforcement learning, sensor fusion, and ensemble learning in achieving superior performance and efficiency in various engineering domains. Key Words: Machine Learning, Engineering Applications, Artificial Intelligence, Big Data, Predictive Maintenance, Robotics, Structural Design, Smart Grids, Fault Detection, Optimization, Control Systems.

A Numerical Analysis Based Internet of Things (IOT) and Big Data Analytics to Minimize Energy Consumption in Smart Buildings

The new wave of performant technology devices generates massive amounts of data. These devices are used in cities, homes, buildings, companies, and more. One of the reasons for digitalizing their tasks is that over the past few years, there has been an interest in reducing carbon emissions and increasing energy efficiency to create a friendly ecosystem and protect nature. One of which granted the explosion of data. After deploying these new devices, a significant increase in the use of the other face of energy to implement the components of the new devices was noticed. Above all, the interconnection of these intelligent devices is the central concept of the Internet of Things (IoT). This domain has widened the possibilities for the interconnection of building management systems (also named Smart Grids) and devices for better energy management. Furthermore, its potential is realized only after organizing and analyzing a large amount of data. Real-time management and maintenance of big data are critical to improving energy management in buildings. The benefits of big data analytics go beyond savings on electricity bills. It can provide comfort for building users and extend the life of building equipment, enhancing the value of commercial buildings. Intelligent interconnection of a building’s technical installations (lighting, heating, hot water, photovoltaic installations, etc.) not only allows for connected management of this equipment but also meets high energy efficiency criteria that indicate an increase in comfort and energy savings. With building automation, the technical installations of a building interact optimally. In this article, we will simulate an intelligent building based on the Cisco packet tracer software. To better manage the energy consumption of our project, we will focus on the processing of data in real-time, especially since we will have a massive amount of data generated by the sensors.

Applying OPTICS with and without PCA for fault detection of fan coil units using building automation system data

In building operations, Heating, Ventilation, and Air Conditioning (HVAC) system faults lead to substantial energy waste. Due to the rapid growth in the availability of sensing data and advancements in computational technology, AI-based methods have emerged as a powerful tool for Fault Detection and Diagnosis (FDD) of HVAC systems, significantly enhancing the efficiency of building operations. Several studies support supervised learning methods for FDD; however, these are limited in practice due to their reliance on labeled datasets, which are generally unavailable. In this context, unsupervised methods, notably those using cluster analysis, show great promise, particularly for analysing Building Automation System (BAS) data. This paper presents a methodology using OPTICS – an ordered clustering algorithm – for fault detection and explores the impact of PCA on its accuracy for fault detection, using both published and full-scale historical building data. While beneficial for reducing computational costs and enhancing noise reduction — thereby generally improving the clarity of cluster differentiation — PCA’s dimensionality reduction may result in the loss of critical information, leading to some clusters being less discernible or entirely undetected. These overlooked clusters could be indicative of underlying faults, and their obscurity represents a significant limitation of PCA when identifying potential faults in complex data.

The Impact of IoT on Smart City Infrastructure in India

Today, we live in a society where digital communication systems are so advanced that by the mid 2050’s the world will be completely filled by smart buildings on the internet highway like those portrayed in science fiction movies. This is possible because of tech savvy tiny sensors or Internet of Things (IoT) devices embedded in the smart network in order to collect meaningful information about the citizens and their interaction with the ubiquitous environment. Smart sustainable cities are modelled on the Internet of Things blending ICT for better standard of living and citizen services. IoT will modify indoor temperatures and switch off unnecessary lights in homes, detect snags in equipment and fix machines remotely, and provide commuters with information on traffic jams on the go. IoT in smart cities can be implemented by the development authorities but the stake holders of this task are citizens, the municipal government and private organizations engaged in this partnership. Using sophisticated ICT systems makes the city smarter and improves community building and connectivity by interweaving all the components of a city. Privacy and security are the two watch words when it comes to capturing citizens’ data. This data needs to be anonymised before it reaches the civic authorities. This study evaluates the advantages and disadvantages of using IoT in Indian smart cities after discussing an international case study of a smart sustainable city.

Online Energy Management Framework for Smart Buildings With Low-Complexity Estimators

Online Energy Management Framework for Smart Buildings With Low-Complexity Estimators

Editorial: Advanced technologies for smart buildings and infrastructure (Part 2) – addressing Sustainable Development Goals

Editorial: Advanced technologies for smart buildings and infrastructure (Part 2) – addressing Sustainable Development Goals

Correction: Artificial intelligence assisted IoT-fog based framework for emergency fire response in smart buildings

Correction: Artificial intelligence assisted IoT-fog based framework for emergency fire response in smart buildings

A scaled dirichlet-based predictive model for occupancy estimation in smart buildings

A scaled dirichlet-based predictive model for occupancy estimation in smart buildings

Innovations in architectural and environmental design: From methodologies to technology and cultural sensitivity

This comprehensive paper delves into the multifaceted world of contemporary architectural and environmental design, exploring an array of methodologies that emphasize sustainability, technological innovation, and socio-cultural integration. It begins by discussing the Integrated Design Process (IDP), which fosters early-stage collaboration across various disciplines, illustrated by the example of Seattles Bullitt Center. The paper then explores the principles of Biophilic Design, highlighting its positive impacts on human health and well-being, as seen in Singapores Khoo Teck Puat Hospital. The concept of Adaptive Reuse is explored next, emphasizing its role in sustainable urban development, with the Tate Modern in London serving as a case study. Further, the paper examines Green Building Technologies, Digital Design and Simulation Tools like BIM, and Smart Building Systems, showcasing their contributions to sustainable and efficient building management. The final sections discuss the importance of Community Engagement, Cultural Sensitivity in Design, and Inclusive Design, stressing the need for architecture that resonates with community values and is accessible to all. Through these discussions, the paper provides an insightful look into the current trends and future directions in architecture and environmental design, advocating for a holistic approach that balances functionality, sustainability, and cultural relevance.

Establishment of a smart building assessment framework in the context of smart cities

PurposeThis study proposes an assessment framework for improving smart building performance in the broader context of smart city development, considering dimensions like environmental sustainability, building characteristics, intelligence, computation management and analytics. The framework is crafted to guide future research, aligning with the growing emphasis on sustainability and intelligence in evolving urban landscapes within smart cities.Design/methodology/approachIn the initial phase, the concepts of “Smart City” and “Smart Buildings” are analyzed through a systematic literature review, considering the impact of governance on city sustainability and growth, along with the role of public policies in transforming buildings and cities. The empirical research evaluates innovation levels in small and medium-sized European cities, proposing a new framework with validated dimensions and sub-dimensions. This validation involves input from international experts through a Focus Group.FindingsThe key research findings validate the new proposed assessment framework for smart buildings within smart city development. The experts’ insights align with and support the dimensions identified in the bibliographic research, providing a comprehensive understanding of the role of smart buildings in sustainable urban development.Originality/valueThis framework not only provides insights for a new model with specific dimensions and sub-dimensions but also serves as a guide for formulating strategies and policies to enhance innovation in these settings. The value of this approach is strengthened by the validation and consolidation process involving international experts in the field.