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The integration of smart buildings (SBs) into smart cities (SCs) is critical to urban development, with the potential to improve SCs' performance. Artificial intelligence (AI) applications have emerged as a promising tool to enhance SB and SC development. The authors apply an AI-based methodology, particularly Large Language Models of OpenAI ChatGPT-3 and Google Bard as AI experts, to uniquely evaluate 26 criteria that represent SB services across five SC infrastructure domains (energy, mobility, water, waste management, and security), emphasizing their contributions to the integration of SB into SC and quantifying their impact on the efficiency, resilience, and environmental sustainability of SC. The framework was then validated through two rounds of the Delphi method, leveraging human expert knowledge and an iterative consensus-building process. The framework's efficiency in analyzing complicated information and generating important insights is demonstrated via five case studies. These findings contribute to a deeper understanding of the effects of SB services on SC infrastructure domains, highlighting the intricate nature of SC, as well as revealing areas that require further integration to realize the SC performance objectives. [ABSTRACT FROM AUTHOR]

This study proposes a simulation-based methodology for estimating the energy saving achievable through the implementation of a just-in-time morning ramp-up procedure in a warehouse (equipped with a heat pump). In this methodology, the operation of the heating supply unit each day is initiated at a different time, aiming at achieving the desired setpoint upon (and not before) the expected arrival of the occupants. It requires the estimation of the ramp-up duration (the time it takes the heating system to bring the indoor temperature to the desired setpoint), which can be provided by machine learning-based models. To justify the corresponding required deployment investment, an accurate estimation of the resulting achievable energy saving is needed. Accordingly, physics-based energy behavior simulations are first performed. Next, various ML algorithms are employed to estimate the ramp-up duration using the simulated time-series data of indoor temperature, setpoints, and weather conditions. It is shown that the proposed pipelines can estimate the ramp-up duration with a mean absolute error of about 3 min in all indoor spaces. To assess the resulting potential energy saving, a re-simulation is conducted using ML-based ramp-up estimations for each day, resulting in an energy savings of approximately 10%. [ABSTRACT FROM AUTHOR]

Background: Smart building, as an emerging building concept, has been a key driving force for the transformation and upgrading of the building industry; Methods: To better understand the latest research progress and trends in the field of smart building, this study uses CiteSpace 6.2.R4 bibliometric software to visualize, analyze, and interpret the literature related to the field of "Smart Building" in the WoS database from 2014 to 2023; Results: As a cross-sectoral and multidisciplinary field, smart building has received significant attention in recent years, with a rapid growth in the number of publications. International cooperation is strong, with China, the United States, and South Korea leading in the number of publications, but there is still room for enhanced collaboration among institutions. Keyword analysis shows that technology and humanized design are both crucial, and emerging technology has become the current research hotspot. Conclusions: The field of smart building has gained global attention, and more breakthroughs will be made in improving building efficiency, reducing energy consumption, and enhancing the user experience. This development is moving towards a smarter and more sustainable direction that will bring greater benefits to human life and the environment. [ABSTRACT FROM AUTHOR]

The rise of the Internet of Things (IoT) has enabled the development of smart cities, intelligent buildings, and advanced industrial ecosystems. When the IoT is matched with machine learning (ML), the advantages of the resulting enhanced environments can span, for example, from energy optimization to security improvement and comfort enhancement. Together, IoT and ML technologies are widely used in smart buildings, in particular, to reduce energy consumption and create Intelligent Energy-Efficient Buildings (IEEBs). In IEEBs, ML models are typically used to analyze and predict various factors such as temperature, humidity, light, occupancy, and human behavior with the aim of optimizing building systems. In the literature, many review papers have been presented so far in the field of IEEBs. Such papers mostly focus on specific subfields of ML or on a limited number of papers. This paper presents a systematic meta-survey, i.e., a review of review articles, that compares the state of the art in the field of IEEBs using the Prisma approach. In more detail, our meta-survey aims to give a broader view, with respect to the already published surveys, of the state-of-the-art in the IEEB field, investigating the use of supervised, unsupervised, semi-supervised, and self-supervised models in a variety of IEEB-based scenarios. Moreover, our paper aims to compare the already published surveys by answering five important research questions about IEEB definitions, architectures, methods/models used, datasets and real implementations utilized, and main challenges/research directions defined. This meta-survey provides insights that are useful both for newcomers to the field and for researchers who want to learn more about the methodologies and technologies used for IEEBs' design and implementation. [ABSTRACT FROM AUTHOR]

[Introduction] The new generation of the Internet of Things (IoT) is being fostered in the era of Internet of Everything (IoE), realizing its diverse development by selecting and combining new information, functions, and applications. The Energy Internet of Things (Energy IoT) which is based on IoT, envisions a future where physical things are connected through a dynamic network that exchanges information and energy. The Energy IoT is giving rise to new service models and methods for organizing, exchanging, and managing energy; It covers not only new concepts such as Energy-as-a-Service and Prosumer, but also leads to innovative applications in smart buildings, intelligent metering, smart grids, distributed energy, virtual power plants and more. [Method] This paper analyzed the current status of the Energy IoT, including its key industry drivers, potential technologies and applications, challenges and related research areas. [Result] This paper discusses and compares the definitions of Energy Internet and Energy IoT from academic and industry perspectives. And it analyzes some major stages and issues of future research in the Energy IoT. [Conclusion] This paper provides a useful reference for further research and practical applications in the field of Energy IoT.

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Smart buildings and cities are rapidly emerging as solutions to address the challenges of efficiency, urbanisation, and sustainability in the sector. The study proposes deploying data-driven digital twins for smart buildings by utilising the available building's technology and IT infrastructure to complement and augment existing functions. The digital twin will consist of a core data-driven energy model and a 2D visual representation of the building's systems, with the potential for future evolution into a 3D model. This study aims to present a preliminary investigation into the idea of data-driven digital twins in building management towards enhancing the operations of smart buildings and empowering the concept of smart cities. It is demonstrated on a building on the campus of Qatar University. With an emphasis on the air conditioning systems of the building, considering their substantial contribution to overall energy consumption, the study maintains an open approach to also encompass other energy systems within the buildings, and presents a comparative evaluation between simulation-based and data-driven modelling on the case study, as well as an exploration of various machine learning algorithms that can be used. Furthermore, exploring essential smart applications of the building's data-driven digital twin. Practical Application: The study provides a comprehensive exploration of the practical aspects of deploying data-driven digital twins for smart buildings, addressing challenges related to data collection, model development, integration with building infrastructure, and potential limitations. The paper aims to advance the field of facility management and promote smart and sustainable practices in building operations. By contributing to the existing knowledge in facility services and management, our study offers practical guidance towards optimising building performance, reducing energy consumption, and fostering sustainable urban development. [ABSTRACT FROM AUTHOR]

The explicit demand response (DR) is a key program for reinforcing the participation of end customers and making the most out of the potential of the smart grid. The DR is a key topic in the field of buildings to make use of the flexibility that they can offer. However, in order to guarantee the correct functionality of a DR system, it is fundamental to perform interoperability tests among the various components/actors. In this paper, we take into consideration the technological solutions suggested in the framework of the DRIMPAC project to enable the DR in buildings. We consider all actors/devices involved in order to reach the objective of executing a flexibility order by an asset. Following a structured interoperability testing methodology created by the Joint Research Centre, we perform interoperability tests regarding all critical links of the full chain of interacting actors to obtain the DR in buildings. The results show that the system functions properly and the benefits from the DR can be exploited. On the other hand, we provide a concrete example of how to apply the interoperability methodology in the field of testing the DR in buildings. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to provide insight into the trend of AI-BIM integration, which has been studied by scholars around the world. To begin, a systematic review and bibliometric analysis was conducted to investigate English articles published between 2015 and 2022. This paper presents a systematic, scientometric, science mapping analysis through qualitative and quantitative evaluation and co-occurrence methods using VOSviewer, CiteSpace, and Gephi software. Conclusions indicate future research should concentrate on integrating AI and other smart systems with BIM to enhance digitalization and improve outcomes throughout the construction project life cycle. Based on the qualitative and quantitative evaluation of each scope (BIM and AI) and their status quo, this study suggests integrating the following domains with BIM to reduce complexity in the construction industry in the future: robotics, cloud systems, AIOT, digital twins, 4D printing, and block chain. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: As the world is engulfed with the growing impacts of climate change, the integration of climate adaptation measures into building performance requirements is essential. In the era of the fourth industrial revolution, smart buildings are expected to be the next frontier in the realm of building rating systems after sustainability-based one. Smart buildings can play a pivotal role in addressing the evolving challenges of changing climate due to their temporal and spatial cross-scale nature. Methods: This study assesses the integration of climate hazard adaptation options within four prominent smart building rating systems (SBRS). Using a sectoral analysis approach and a 4-point Likert scale, we systematically evaluate the extent to which these rating systems incorporate climate adaptation measures directly or indirectly across multiple building sectors. We identify strengths and weaknesses in each system's approach, highlighting areas where adaptation options are more profoundly addressed and sectors that require further attention. Results: The evaluation results reveal variations in the comprehensiveness of climate adaptation integration among the smart building rating systems. The SRBS show a high level of integration of climate adaptation measures in the urban sectors intrinsically tied to the smart building paradigm, such as communication sector, and the human wellbeing and organization sector. Nevertheless, the study also revealed that SBRS almost universally fall short in covering other vital domains such as building envelope and structure, water and sanitation, and blue and green infrastructure. Discussions: Complementing the SBRS with sustainability rating systems (GBRS) can effectively address the limitations in climate adaptation integration within SBRS. Moreover, the inherent interconnectedness of smart buildings with their surrounding infrastructure and the broader urban environment underscores the importance of the cross-scale consideration in the building rating domain in general and in climate related topics in particular, this interconnectedness also highlights a smart building's reliance on its surrounding context for optimal functionality and the interdependency between the building and urban scale. [ABSTRACT FROM AUTHOR]

Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology.

Over the years, a rapid evolution of smart grids has been witnessed across the world due to their intelligent operations and control, smart characteristics, and benefits, which can overcome several difficulties of traditional electric grids. However, due to multifaceted technological advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers need to understand and adapt to new concepts and research trends. Understanding these new trends in smart grids is essential for several reasons, as the energy sector undergoes a major transformation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the complete potential of modernizing the energy infrastructure. In this regard, this paper presents a comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search, the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and 2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the scholarly literature on a particular topic and provides valuable insights to researchers. The proposed analysis provides key information on emerging research areas, high-impact sources, authors and their collaboration, affiliations, annual production of various countries and their collaboration in smart grids, and topic-wise title count. The information extracted from this bibliometric analysis will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric package in R.

Improving energy efficiency in the building sector is a subject of significant interest, considering the environmental impact of buildings. Energy efficiency involves many aspects, such as occupant comfort, system monitoring and maintenance, data treatment, instrumentation… Physical modeling and calibration, or artificial intelligence, are often employed to explore these different subjects and, thus, to limit energy consumption in buildings. Even though these techniques are well-suited, they have one thing in common, i.e., the need for user cases. This is why we propose to share a part of the large volume of data collected on our modular education building. The building is located on Nanterreʼs CESI Engineering school campus and welcomes approximately 80 students daily. A network of more than 150 sensors and actuators allows monitoring of the physical behavior of the entire building, preserving optimal comfort and energy consumption. The dataset includes the indoor physical parameters and the operating conditions of each system to describe the physical behavior of the building during a year.

In the dynamic landscape of technology, the intersection of Smart Buildings and Internet of Things (IoT) devices continues to hold substantial promise. The integration of billions of smart devices with computing systems empowers the identification, analysis, and influence of various actions in our daily lives. A prevalent approach in IoT implementation involves leveraging Android systems, where Android applications play a pivotal role in front-end management. This paper introduces an innovative Android application designed for the comprehensive management of smart buildings, providing users with real-time data monitoring and control capabilities. The application serves as a bridge, bringing users closer to the operational intricacies of smart building systems. Notably, the application is equipped to send timely alerts when predefined limits for humidity, temperature, or light consumption are surpassed, as well as during specific time periods with detected motion. These alerts prompt users to make informed adjustments and decisions, enhancing overall user engagement and responsiveness. The proposed system architecture comprises five distinct layers: Python scripts for sensor data generation, JSON files for data storage, a Flask-based Application Programming Interface (API) for data analysis and conversion, SQLite for efficient data storage, and the Android application for user interaction. This multi-layered framework ensures seamless integration and robust functionality throughout the smart building management process. [ABSTRACT FROM AUTHOR]

Research efforts focused on Smart Building (SB) development have concentrated on the automation of resources within intelligent environments towards an enhanced experience for occupants. The process converts everyday manual activities into automatic actions, such as turning on lights upon entering a room, activating air conditioning on hot days, and switching off a television when there are no viewers. This article addresses a case study on context-aware monitoring conducted at the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) of the University of São Paulo. The focus is on a Fog layer of the IoT operating closer to sensors and reducing communication delays. Concepts of context-aware systems, multi-agent systems, ontology, and MQTT protocol were considered for the implementation of the intelligent system on-site. The programming languages used were Java and Python, leveraging libraries and frameworks dedicated to such technologies. A scalable system that does not compromise computational resource use and maintains responsiveness with low data exchange latency is proposed and tests checked the intelligent behavior of the laboratory under specific conditions using temperature, luminosity, and presence of a person as parameters. [ABSTRACT FROM AUTHOR]

The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building sector while considering several variables (energy consumption/generation, weather information, etc.) that impact energy use. It performs a comparative analysis of various machine learning (ML) models based on different data resolutions and time steps ahead (15 min, 30 min, and 1 h with 4-step-, 2-step-, and 1-step-ahead, respectively) to identify the most accurate prediction method. Performance assessment showed that models like histogram gradient-boosting regression (HGBR), light gradient-boosting machine regression (LGBMR), extra trees regression (ETR), ridge regression (RR), Bayesian ridge regression (BRR), and categorical boosting regression (CBR) outperformed others, each for a specific resolution. Model performance was reported using R 2 , root mean square error (RMSE), coefficient of variation of RMSE (CVRMSE), normalized RMSE (NRMSE), mean absolute error (MAE), and execution time. The best overall model performance indicated that the resampled 1 h 1-step-ahead prediction was more accurate than the 15 min 4-step-ahead and the 30 min 2-step-ahead predictions. Findings reveal that data preparation is vital for the accuracy of prediction models and should be model-adjusted. [ABSTRACT FROM AUTHOR]

Over the years, a rapid evolution of smart grids has been witnessed across the world due to their intelligent operations and control, smart characteristics, and benefits, which can overcome several difficulties of traditional electric grids. However, due to multifaceted technological advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers need to understand and adapt to new concepts and research trends. Understanding these new trends in smart grids is essential for several reasons, as the energy sector undergoes a major transformation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the complete potential of modernizing the energy infrastructure. In this regard, this paper presents a comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search, the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and 2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the scholarly literature on a particular topic and provides valuable insights to researchers. The proposed analysis provides key information on emerging research areas, high-impact sources, authors and their collaboration, affiliations, annual production of various countries and their collaboration in smart grids, and topic-wise title count. The information extracted from this bibliometric analysis will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric package in R. [ABSTRACT FROM AUTHOR]

Power management in several sectors poses the problem of conserving the consumed power while satisfying the imposed conditions It is considered as a proactive control and management of the organization's energy consumption to save use and reduce energy expenses. Therefore, there is an actual need to include smart energy management systems in buildings in order to reduce the consumed energy. In this work, a comparative analysis is presented to evaluate deep and machine-learning approaches in the context of intelligent models for conserving power in smart buildings. The deep learning model is structured by using Deep Neural Networks (DNN), while machine learning models are represented by Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Naive Bayes (NB). These models adopt three classes: full (power in full consumption), select (power in partial consumption), and shout down (no power consumption). Moreover, feature reduction methods of Boruta and Principal Component Analysis (PCA) are implemented to reduce the complexity of the models. The proposed models are trained and tested using a measured dataset for a building. Comparison results of the proposed models showed that the Random Forest attracts more attention regarding classification accuracy by 100% and a reasonable classification time of 1.23 seconds. The effectiveness of the comparative analysis which indicating the highest accuracy results for RF makes it as a suitable model to be implemented as an optimal one in real-time power management systems. [ABSTRACT FROM AUTHOR]

Urbanization increases electricity demand due to population growth and economic activity. To meet consumer’s demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand imbalance by accurately predicting the energy consumption. This paper presents a comprehensive literature review of forecasting methodologies used by researchers for energy consumption in smart buildings to meet future energy requirements. Different forecasting methods are being explored in both residential and non-residential buildings. The literature is further analyzed based on the dataset, types of load, prediction accuracy, and the evaluation metrics used. This work also focuses on the main challenges in energy forecasting due to load fluctuation, variability in weather, occupant behavior, and grid planning. The identified research gaps and the suitable methodology for prediction addressing the current issues are presented with reference to the available literature. The multivariate analysis in the suggested hybrid model ensures the learning of repeating patterns and features in the data to enhance the prediction accuracy. [ABSTRACT FROM AUTHOR]

In the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a predictive model leveraging Machine Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics, including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance, achieving accuracies of 96.7 % and 9.64 % respectively. In contrast, the SVM algorithm demonstrates inferior performance with a R2 of 81.1 %. These findings underscore the predictive capability of the RF and XGBOOST model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems. Further research could explore the practical applications of these findings in building design and operation.

In the face of escalating climate challenges, environmental sustainability has greatly become an urgent and non-negotiable priority, necessitating revolutionary advancements in energy management to reduce carbon footprints and drive profound improvements in overall sustainability. This paper presents an advanced optimization framework, PST-CESS, for managing power-sharing among multiple tenants within the centralized energy storage system (ESS). Our thorough evaluation demonstrates that the centralized ESS facilitated by PST-CESS substantially exceeds the performance of individualized ESS systems in pivotal areas such as peak load reduction, variability mitigation, and financial profitability. Specifically, the centralized ESS model achieves up to a 44.05% reduction in annual peak load for certain tenants and reduces electricity consumption variability by up to 57.67%. From a financial perspective, the centralized ESS model delivers remarkable advantages, reaching a break-even point in just 2.48 years, compared to the 5.08 years required for individualized ESS systems, even when accounting for battery capacity loss costs, also known as battery degradation costs. These results highlight the centralized ESS approach as a more economically advantageous and efficient solution, providing superior financial returns and optimized energy management for multi-tenant buildings. The strategic benefits and compelling evidence presented in this study strongly support the widespread adoption of centralized ESS models to maximize both economic and environmental benefits, establishing a new standard for sustainable energy management.

The coronavirus (COVID-19) has emerged as one of the most serious issues. Researchers and officials are considering the implementation of several social distancing methods to detect potentially contaminated individuals. Nevertheless, limited social distancing methods have been discovered for tracking, scheduling, and monitoring vehicles for smart buildings. In these methods, people are tested on a regular basis in testing facilities every few days. This suggests that there may be untested infected individuals exhibiting active symptoms. Furthermore, since pandemics comparable to COVID may exhibit a range of symptoms that fluctuate throughout the day. For this reason, each time a vehicle requests entry into the facility, a real-time test or check must be performed. This study proposes a real-time vehicle social distancing decision system for managing the number of vehicles (RT-VSDD) that adds an additional testing method besides the traditional testing phase (test reports from test centers) which is a real-time vital health check during the building access request phase in order to reduce the risk of unidentified infected individuals. The concept of low-risk area and high-risk area in the building is introduced in this study where the method classifies the vehicles based on the risk levels and sends them to the targeted area. The system proposed in this study is identified as vehicle social distancing (VSD) system and is designed specifically for COVID pandemic. The performance evaluation of the proposed work has been performed using MATLAB simulations. 100 vehicles were assumed in the presented scenario with 5% untested, 20% positive, 75% negative, 30% high temperature, and 70% low temperature. When compared with the benchmark work, 40% of vehicles were classified as high risk and 55% were low risk by the proposed system, and 20% and 75% by the benchmark work. Only 5% of vehicles were denied access using the proposed system and 25% by the benchmark work. The total waiting vehicles rate was 25% and 11% in favour of the proposed work for a total waiting time of 100 minutes. The threshold value for the maximum vehicle allowed was reached 26 times by the proposed work against 13 times only by the benchmark work. 95% of vehicles were allowed access using the proposed technique, while only 75% were able to access the building using the benchmark technique. It is anticipated that the suggested system design will facilitate a reduction in the infection rate within buildings, reduces the negative economic impact, and manage the building access effectively for various industries and government sectors.

Population growth has become a serious problem in many countries, especially Egypt. Which leads to an increase in the population area and an increase in buildings, which then leads to several problems, including large energy consumption, increased pollution, traffic congestion, and others. Therefore, many governments have resorted to using technology and applying it to build smart buildings to help save energy by using renewable energy to improve its impact on the environment, improve the quality of life of citizens, provide security and safety, and so on. The selection of smart buildings depends on many criteria. Since this problem is described as a multi-criteria decision-making (MCDM) problem, MCDM methods will be used in this paper. A hybrid method is presented to evaluate smart buildings. The first method, MEREC, was used to calculate the weights of criteria, and the VIKOR model was used for ranking alternatives. Then applying those weights to the CoCoSo, COPRAS, and TOPSIS methods for making comparisons using Spearman`s correlation coefficients for ranking these four methods. All methods used are applied in the T2NN environment.

Background: Smart building, as an emerging building concept, has been a key driving force for the transformation and upgrading of the building industry; Methods: To better understand the latest research progress and trends in the field of smart building, this study uses CiteSpace 6.2.R4 bibliometric software to visualize, analyze, and interpret the literature related to the field of “Smart Building” in the WoS database from 2014 to 2023; Results: As a cross-sectoral and multidisciplinary field, smart building has received significant attention in recent years, with a rapid growth in the number of publications. International cooperation is strong, with China, the United States, and South Korea leading in the number of publications, but there is still room for enhanced collaboration among institutions. Keyword analysis shows that technology and humanized design are both crucial, and emerging technology has become the current research hotspot. Conclusions: The field of smart building has gained global attention, and more breakthroughs will be made in improving building efficiency, reducing energy consumption, and enhancing the user experience. This development is moving towards a smarter and more sustainable direction that will bring greater benefits to human life and the environment.

This study proposes a simulation-based methodology for estimating the energy saving achievable through the implementation of a just-in-time morning ramp-up procedure in a warehouse (equipped with a heat pump). In this methodology, the operation of the heating supply unit each day is initiated at a different time, aiming at achieving the desired setpoint upon (and not before) the expected arrival of the occupants. It requires the estimation of the ramp-up duration (the time it takes the heating system to bring the indoor temperature to the desired setpoint), which can be provided by machine learning-based models. To justify the corresponding required deployment investment, an accurate estimation of the resulting achievable energy saving is needed. Accordingly, physics-based energy behavior simulations are first performed. Next, various ML algorithms are employed to estimate the ramp-up duration using the simulated time-series data of indoor temperature, setpoints, and weather conditions. It is shown that the proposed pipelines can estimate the ramp-up duration with a mean absolute error of about 3 min in all indoor spaces. To assess the resulting potential energy saving, a re-simulation is conducted using ML-based ramp-up estimations for each day, resulting in an energy savings of approximately 10%.

This paper introduces an efficient barrier model for enhancing smart building surveillance in harsh environment with thin walls and structures. After the main research problem of minimizing the total number of wall-recognition surveillance barriers, we propose two distinct algorithms, Centralized Node Deployment and Adaptation Node Deployment, which are designed to address the challenge by strategic placement of surveillance nodes within the smart building. The Centralized Node Deployment aligns nodes along the thin walls, ensuring consistent communication coverage and effectively countering potential disruptions. Conversely, the Adaptation Node Deployment begins with random node placement, which adapts over time to ensure efficient communication across the building. The novelty of this work is in designing a novel barrier system to achieve energy efficiency and reinforced surveillance in a thin-wall environment. Instead of a real environment, we use an ad hoc server for simulations with various scenarios and parameters. Then, two different algorithms are executed through those simulation environments and settings. Also, with detailed discussions, we provide the performance analysis, which shows that both algorithms deliver similar performance metrics over extended periods, indicating their suitability for long-term operation in smart infrastructure. [ABSTRACT FROM AUTHOR]

Parramatta is a city on the move and the face of global Sydney. The geographical centre of Sydney and a hub of ideas, culture and commerce. Parramatta is fast becoming a world class centre of excellence in education, health, research and innovation. It is a home to big thinkers, growing families and ambitious people. People who come from across the globe to live, work and play in our thriving global metropolis. Liveability underpins everything we do at Parramatta. Our city is designed to support the health and wellbeing of residents and visitors. A city designed for people, with a brand new beating heart, our Parramatta Library at PHIVE. The paper will show the basic features of the library and the awards it received for its role, importance and design as a smart and green building. [ABSTRACT FROM AUTHOR]

Population growth has become a serious problem in many countries, especially Egypt. Which leads to an increase in the population area and an increase in buildings, which then leads to several problems, including large energy consumption, increased pollution, traffic congest ion, and others. Therefore, many governments have resorted to using technology and applying it to build smart buildings to help save energy by using renewable energy to improve its impact on the environment, improve the quality of life of citizens, provide security and safety, and so on. The selection of smart buildings depends on many criteria. Since this problem is described as a multi-criteria decision-making (MCDM) problem, MCDM methods will be used in this paper. A hybrid method is presented to evaluate smart buildings. The first method, MEREC, was used to calculate the weights of criteria, and the VIKOR model was used for ranking alternatives. Then applying those weights to the CoCoSo, COPRAS, and TOPSIS methods for making comparisons using Spearman's correlation coefficients for ranking these four methods. All methods used are applied in the T2NN environment. [ABSTRACT FROM AUTHOR]

In the light of the adverse effects of climate change, data analysis and Machine Learning (ML) techniques can provide accurate forecasts, which enable efficient scheduling and operation of energy usage. Especially in the built environment, Energy Load Forecasting (ELF) enables Distribution System Operators or Aggregators to accurately predict the energy demand and generation trade-offs. This paper focuses on developing and comparing predictive algorithms based on historical data from a near Zero Energy Building. This involves energy load, as well as temperature data, which are used to develop and evaluate various base ML algorithms and methodologies, including Artificial Neural Networks and Decision-trees, as well as their combination. Each algorithm is fine-tuned and tested, accounting for the unique data characteristics, such as the presence of photovoltaics, in order to produce a robust approach for One-Step-Ahead ELF. To this end, a novel hybrid model utilizing ensemble methods was developed. It combines multiple base ML algorithms the outputs of which are utilized to train a meta-model voting regressor. This hybrid model acts as a normalizer for any new data input. An experimental comparison of the model against unseen data and other ensemble approaches, showed promising forecasting results (mean absolute percentage error = 5.39%), particularly compared to the base algorithms. [ABSTRACT FROM AUTHOR]

It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices. [ABSTRACT FROM AUTHOR]

Improved energy performance and occupant comfort are driving building design decisions due to the increasing demand for sustainable and green buildings. However, despite the variety of technological developments in other fields, the range of solutions to improve building performance is limited. One of the main limitations for an early designer is a performance evaluation method to facilitate the design process. This paper offers a new shading performance optimization process that can help designers evaluate both daylighting and energy performance and generate optimized and flexible designs that can be further improved by implementing user-specific automation. The proposed performance optimization method utilizes parametric design, building simulation models, and Genetic Algorithms. Common shading design systems are explored through parametric design, and daylighting and energy modeling simulations are performed to evaluate shading device performance. Genetic Algorithms are used to identify design options with optimal energy and daylighting performance. A case study is conducted to verify the effectiveness of the overall process. Results are used to analyze the influence of design decisions among different shading designs. Finally, future directions in both shading design and energy optimization are presented. [ABSTRACT FROM AUTHOR]

This study sought to identify the dimensions and the significant critical factors capable of enhancing Smart Building Technologies' (SBTs') implementation for smart building projects in developing countries. A desk literature review is first conducted to identify and categorize the potential factors. It is further analyzed using partial least square structural equation modelling (PLS-SEM) based on 227 valid data from experts in Ghana. The study revealed four underlying dimensions (i.e., 'processes and control'[PC], 'people and skills'[PS], 'methods and techniques'[MT], and 'knowledge sharing'[KS]) consisting 14 significant critical factors capable of enhancing SBTs implementation for smart building projects, with the top three comprising 'appropriate procedures/practices for managing smart building projects (MT3)', 'appropriate tools/techniques to guide smart building projects to their delivery (MT2)', and 'skills and experience required to pick project team members for smart building projects (PS1)'. Further analysis with PLS-SEM revealed a significant positive effect of the four underlying dimensions and their positive interrelationships toward framework development. Besides the unique contribution of this study to the knowledge body, it also provides project managers and a construction design team with a structured knowledge of the skills, expertise, attitudes, decision-making, processes, control mechanisms, and effective delivery of smart building projects in developing countries. [ABSTRACT FROM AUTHOR]