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The multi-mode communication network provides communication support for the collection, transmission, and processing of energy regulation data and the training of energy regulation models for smart buildings.Digital twin can provide state estimation of computing resources and channel characteristics, assist in the multi-mode communication resource optimization management, and improve the training precision of energy regulation models.However, the digital twin-assisted multi-mode communication resource management of smart buildings still face challenges such as large training error of energy regulation model, coupling of multi-timescale resource allocation, and contradictions between training precision improvement of energy regulation model and energy consumption optimization.Aiming at the above challenges, a multi-timescale communication resource management optimization algorithm based on digital twin and empirical matching learning was proposed.The weighted sum of global model loss function and energy consumption was minimized by jointly optimizing the large-timescale gateway selection and small-timescale channel allocation and power control.Simulation results show that the proposed algorithm can improve the performance of weighted sum of global model loss function and energy consumption, ensure the precise energy regulation requirement and promote the low-carbon operation of smart buildings.

Because of the rising population density, relationships are necessary to raise living standards through sending and receiving a wide range of services. Because of this, many means of object communication—regardless of their nature—are necessary to meet our daily needs. IoT is a network of physical things integrated with sensors, and software to communicate with each other. To establish a good connection, every object considered to be an associate of another object should meet certain requirements including scalability, interoperability, and trustworthiness. IoT security is a challenging task to protect the hardware and networks in the IoT system and a significant constraint to the deployment and realization of IoT. IoT security may include data confidentiality, authentication, access control, anonymity, and trust among services and products. Exchanging trust information is critical for assessing an entity’s trustworthiness. Therefore, trust information must be shared and stored securely to ensure reliability, honesty, and safety. We propose a secure trust management scheme built on blockchain technologies to secure the entire system in transparency, traceability, and material integrity. We implement a blockchain-based trust management architecture for smart buildings that collect node trust proof. It assigns a trust score to each node, securely stores them in an array, then the threshold value is computed using the ID3 Algorithm. IoT threshold value is broadcasted into the blockchain network and stored in the trusted list. According to the findings, our approach encompasses security measures such as tamper-proofing, attack resistance, reliability, and low functionality for IoT in smart buildings.

Introduction: Heritage conservation attracts wide attention worldwide. Jordan has many heritage buildings that were preserved, rehabilitated, and adapted to new functions. However, these conservation efforts could not solve the energy consumption problem or maintain the economic and financial balance, thus reducing building efficiency, slowing down the conservation activity, and forcing us to consider new solutions, especially since Jordan has limited energy sources. Integrating smart technologies in historical buildings is widely effective in achieving the sustainability of their historical, symbolic architectural values. Purpose of the study: We aimed to explore the potential integration of smart technologies in cultural and heritage buildings. Methods: We suggested an alternative solution — adopting the smart building concept in the Abu Jaber Museum in Al-Salt (which recently got on the UNESCO World Heritage List since it has more than 1000 heritage buildings), using an automated lighting control system. Approach: We deployed the qualitative method and case study approach to investigate the potential of adopting the smart building concept in the historical building in Al-Salt, Jordan. The study sheds light on the possibilities of utilizing smart technologies in historical buildings in Al-Salt. The findings indicate that smart technologies can offer great opportunity in preserving the architectural heritage and raising the efficiency of heritage buildings. Novelty: The study provides a framework based mainly on automated lighting systems in historical buildings. For the first time, the focus was on historical buildings in Jordan and their performance.

More energy is consumed by domestic appliances all over the world. By reducing energy consumption, sustainability can be improved in domestic contexts. Several earlier approaches to this problem have provided a conceptual overview of green and smart buildings. This paper aims to provide a better solution for reducing energy consumption by identifying the fields of abnormal energy consumption. It creates a better environment-friendly smart building to adopt the various lifestyles of people. This paper's main objective is to monitor and control the energy efficiency of smart buildings by integrating IoT sensors. This paper mainly analyzes various prime factors that can help to improve energy efficiency in smart buildings. Factors impacting energy consumption are analyzed, and outliers of energy consumption are predicted and optimized to save energy. Various parameters are derived from IoT devices to improve energy efficiency in lighting and HVAC controls, energy monitoring, building envelope and automation systems, and renewable energy. The parameters used in water, network convergence, and electrical and environmental monitoring are also used for improving energy efficiency. This paper uses various IoT devices for monitoring and generating data in and around a smart building and analyzes it by implementing an intelligent Information Communication Technology (ICT) model called the Dynamic Semantic Behavior Data Analysis (DSBDA) Model to analyze data concerning dynamic changes in the environment and user behavior to improve energy efficiency and provide better sustainable lifestyle-based smart buildings. From the analyzed output, the outliers of the power consumption and other abnormalities are identified and controlled manually or automatically to improve sustainability regarding energy use in smart buildings. [ABSTRACT FROM AUTHOR]

Nowadays, smart buildings rely on Internet of things (IoT) technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects. Fog is characterized by low latency with a wider spread and geographically distributed nodes to support mobility, real-time interaction, and location-based services. To provide optimum quality of user life in modern buildings, we rely on a holistic Framework, designed in a way that decreases latency and improves energy saving and services efficiency with different capabilities. Discrete EVent system Specification (DEVS) is a formalism used to describe simulation models in a modular way. In this work, the sub-models of connected objects in the building are accurately and independently designed, and after installing them together, we easily get an integrated model which is subject to the fog computing Framework. Simulation results show that this new approach significantly, improves energy efficiency of buildings and reduces latency. Additionally, with DEVS, we can easily add or remove sub-models to or from the overall model, allowing us to continually improve our designs. [ABSTRACT FROM AUTHOR]

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Most of the energy produced in the world is consumed by commercial and residential buildings. With the growth in the global economy and world demographics, this energy demand has become increasingly important. This has led to higher unit electricity prices, frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management. This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing. This simultaneously reduces the electricity bill and the peaks, while maintaining user comfort in terms of the operating waiting time of appliances. The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming, which involves constraints and consumer preferences. For optimal scheduling, the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm. Two techniques are proposed to manage the trade-off between the conflicting objectives. The first technique is the Pareto-optimal solutions classification using supervised learning methods. The second technique is called the lexicographic method. The simulations were performed based on residential building energy consumption, time-of-use pricing (TOU) and critical peak pricing (CPP). The algorithms were implemented in Python. The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio (PAR) by 28% and 49.32%, respectively, for the TOU tariff rate, and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer's comfort level. [ABSTRACT FROM AUTHOR]

Buildings have now adopted a new dimension: the dimension of smartness. The rapid arrival of connected devices, together with the smart features that they provide, has allowed for the transition of existing buildings towards smart buildings. The assessment of the smartness of the large number of existing buildings could exhaust resources, but some organisations are requesting this regardless (such as the smart readiness indicator of the European Union). To tackle this issue, this work describes a tool that was created to find connected devices to automatically evaluate smartness. The tool, which was given the name SmartWatcher, uses a design-for-purpose natural language processing algorithm that converts verbal information into numerical information. The method was tested on real buildings in four different geographical locations. SmartWatcher is shown to be powerful, as it was capable of obtaining numerical values from verbal descriptions of devices. Additionally, a preliminary comparison of values obtained using the automatic engine and clipboard assessments showed that although the results were still far from being perfect, some visual correlation could be seen. This anticipates that, with the addition of appropriate techniques that refine this algorithm, or with the addition of new ones (with other more advanced natural language processing methods), the accuracy of this tool could be greatly increased. [ABSTRACT FROM AUTHOR]

Long-term care refers to any support, both medical and non-medical, provided to the elderly with a chronic illness or disability due to physical or mental conditions. Since the cost of long-term care insurance is not inexpensive, low-cost devices and sensors can be used to create medical assistance systems to reduce human maintenance costs. The requirement of security and privacy under healthcare information protection is a critical issue for internet of medical things (IoMT) data transmission. In this paper, we designed an IoMT security robot for a long-term care system. The goal of this IoMT security robot is to provide secure transmission of the residents' private information. It is composed of three layers, namely, collection, encryption, and transmission. The function of the IoMT security robot is to first collect data from the patient or the elderly, then provide efficient data encryption, and deliver secured data transmission mechanisms to send the valuable data to the cloud. This IoMT security robot also has a server authentication mechanism, and a support IoT and IoMT devices inspection function. Our evaluation results showed that even when we utilized a low power consumption device like Raspberry Pi, AES algorithm achieved an encrypt and decrypt of 100–100 K bytes under 9 ms, which is a lot better than ECC, which takes about 104 ms. Further, we found that the AES only takes 0.00015 s to decrypt 100 Bytes data, which is way faster than the ECC algorithm, which takes 0.09 s. [ABSTRACT FROM AUTHOR]

With the awakening of health awareness, people are raising a series of health-related requirements for the buildings they live in, with a view to improving their living conditions. In this context, BIM (Building Information Modeling) makes full use of cutting-edge theories and technologies in many domains such as health, environment, and information technology to provide a new way for engineers to design and build various healthy and green buildings. Specifically, sensors are playing an important role in achieving smart building goals by monitoring the surroundings of buildings, objects and people with the help of cloud computing technology. In addition, it is necessary to quickly determine the optimal sensor placement to save energy and minimize the number of sensors for a building, which is a de-trial task for the cloud platform due to the limited number of sensors available and massive candidate locations for each sensor. In this paper, we propose a Fast Sensor Placement Location Optimization approach (FSPLO) to solve the BIM problem in cloud-aided smart buildings. In particular, we quickly filter out the repeated candidate locations of sensors in FSPLO using Locality Sensitive Hashing (LSH) techniques to maintain only a small number of optimized locations for deploying sensors around buildings. In this way, we can significantly reduce the number of sensors used for health and green buildings. Finally, a set of simulation experiments demonstrates the excellent performance of our proposed FSPLO method. [ABSTRACT FROM AUTHOR]

Smart, and ultra-low energy consuming Internet of Things (IoTs), wireless sensor networks (WSN), and autonomous devices are being deployed to smart buildings and cities, which require continuous power supply, whereas battery usage has accompanying environmental problems, coupled with additional maintenance cost. We present Home Chimney Pinwheels (HCP) as the Smart Turbine Energy Harvester (STEH) for wind; and Cloud-based remote monitoring of its output data. The HCP commonly serves as an external cap to home chimney exhaust outlets; they have very low inertia to wind; and are available on the rooftops of some buildings. Here, an electromagnetic converter adapted from a brushless DC motor was mechanically fastened to the circular base of an 18-blade HCP. In simulated wind, and rooftop experiments, an output voltage of 0.3 V to 16 V was realised for a wind speed between 0.6 to 16 km/h. This is sufficient to operate low-power IoT devices deployed around a smart city. The harvester was connected to a power management unit and its output data was remotely monitored via the IoT analytic Cloud platform "ThingSpeak" by means of LoRa transceivers, serving as sensors; while also obtaining supply from the harvester. The HCP can be a battery-less "stand-alone" low-cost STEH, with no grid connection, and can be installed as attachments to IoT or wireless sensors nodes in smart buildings and cities. [ABSTRACT FROM AUTHOR]

Nowadays, the smart city concept is gaining ground worldwide, driven by the development of information technology and IoT devices. As a result, smart buildings are built to provide more efficient, environmentally friendly, comfortable and automated features. The industry of building automation dates back nearly a hundred years. However, with the development of information technology, the size of built-in IT devices is shrinking year by year, and the amount of information generated by individual sensors, IT devices and by the people working in these buildings is fast increasing. Consequently, IT systems in intelligent buildings need to be adequately designed considering several aspects. Cyber security is one of these aspects, and it is the subject of the present research. A poorly designed and protected IT system with many Internet of Things elements creates a massive exposure to cyberattacks and hackers. After all, these systems may include the entire network IT structure, the elevator control and access control systems, the building's HVAC system, or even the parking lot system. In summary, the examination of primary cyber security factors and standards should be considered when designing a smart building. [ABSTRACT FROM AUTHOR]

Considering travel characteristics of electric vehicles, this paper proposes a multi-time scale energy management strategy based on model predictive control for smart buildings with electric vehicles. Firstly, travel characteristics of electric vehicles are analyzed, and Monte Carlo is used to extract the arrival and departure time of electric vehicles, as well as the loading state at the beginning and end of charging. Secondly, the distributed power supply, energy storage and controllable load models of smart buildings are established, and the day-ahead energy management strategy is proposed based on mixed integer quadratic programming. Then, the intra-day rolling optimization strategy based on model predictive control is proposed to realize the dynamic correction of the day-ahead operation scheme. Finally, compared with a variety of energy management strategies, it is shown that the proposed strategy can effectively solve the problem of tie line power fluctuation caused by the day- ahead prediction error, improve the robustness of the system in the scenario of prediction uncertainty, reduce cost of the system, and improve the economy of the system. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Smart workplace Internet of Things (IoT) solutions rely on several sensors deployed efficiently in the workplace environment to collect accurate data to meet system goals. A vital issue for these sensor-based IoT solutions is privacy. Ideally, the occupants must be monitored discreetly, and the strategies for maintaining privacy are dependent on the nature of the data required. This paper proposes a new sensor design approach for IoT solutions in the workplace that protects occupants' privacy. We focus on a novel sensor that autonomously detects and captures human movements in the office to monitor a person's sedentary behavior. The sensor guides an eHealth solution that uses continuous feedback about desk behaviors to prompt healthy movement breaks for seated workers. The proposed sensor and its privacy-preserving characteristics can enhance the eHealth solution system's performance. Compared to self-reporting, intrusive, and other data collection techniques, this sensor can collect the information reliably and timely. We also present the data analysis specific to this new sensor that measures two physical distance parameters in real-time and uses their difference to determine human actions. This architecture aims to collect precise data at the sensor design level rather than to protect privacy during the data analysis phase. [ABSTRACT FROM AUTHOR]

Buildings are responsible for 40% of energy consumption in Europe, and 75% of them are energy inefficient. In this context, the opportunities enabled by improved management and conservation of energy in buildings are huge. There is a clear need to accelerate and finance building renovation investments and leverage smart, energy-efficient technologies if the EU wants to reach climate neutrality by 2050. IoT components enable new possibilities for improving efficiency in Smart Buildings, both in commercial and residential spaces. However, the literature misses some contributions aimed at evaluating the investment in IoT technologies used for improving the energy efficiency of a building used for different purposes. Thus, the objective of the present study is to provide an assessment of the main costs and benefits stemming from IoT technologies installation through retrofitting intervention and evaluate the sustainability of the investment. Data to feed the model were retrieved from academic literature and secondary sources. The results show that the investment can be recovered in the medium-short term. In particular, buildings with high consumption rates are the ones that benefit the most from this solution. The present study contributes to the academic literature by providing a model that considers a mixed building and multiple technologies at the same time. It also provides useful insights to whoever is interested in the application of IoT technologies to make a building smart, enabling the comprehension of necessary investment and economic returns. [ABSTRACT FROM AUTHOR]

Summary As an emerging technology, blockchain (BC) has been playing a promising role in today's software‐defined networking (SDN)‐enabled Internet of Things (IoT) applications. Because of the salient feature of the network function virtualization (NFV) techniques, SDN can ensure an IoT system runs efficiently and smoothly in a cloud‐driven ecosystem. When cloud‐enabled systems encounter immense security and operational challenges caused mainly by third‐party dependency, large‐scale data communication, and maintenance, BC offers effective and robust data transfer solutions without incorporating intermediaries over the distributed network. With the increased SDN‐BC convergence in the IoT domain, the underlying challenges and perspectives deserve proper attention methodically and structurally. From the motivation of addressing such issues, this study provides necessary insights to combine those for successful plug‐and‐play. Therefore, the study includes purposefully investigating current state‐of‐the‐art to extract the research trends, future directions, and perspectives in this domain. This study provides a comprehensive survey of IoT, SDN, NFV, and BC‐enabled emerging technologies. More importantly, the authors intelligently integrated the four different technologies—IoT, SDN, BC, and NFV based on characteristics, scopes, challenges, taxonomies, and tables in numerous areas. Initially, the authors introduce the SDN‐IoT ecosystem in brief and address the features and applications. We took a close look at the SDN's overall taxonomy based on security, environment, scopes, and challenges. We also briefly describe the integration of SDN‐IoT with the NFV ecosystems. Moreover, we review the prospect of BC technology from security perspectives, its extent, challenges of practical implementation, and the possible integration of IoT regarding smart applications. Finally, this study highlights several future directions based on these technologies. [ABSTRACT FROM AUTHOR]

Living and working in comfort while a building's energy consumption is kept under control requires monitoring a system's consumption to optimize the energy performance. The way energy is generally used is often far from optimal, which requires the use of smart meters that can record the energy consumption and communicate the information to an energy manager who can analyze the consumption behavior, monitor, and optimize energy performance. Given that the heating, ventilation, and air conditioning (HVAC) systems are the largest electricity consumers in buildings, this paper discusses the importance of incorporating occupancy data in the energy efficiency analysis and unveils energy inefficiencies in the way the system operates. This paper uses 1-year data of a highly efficient certified office building located in the Houston area and shows the power of self-organizing maps and data analysis in identifying up to 4.6% possible savings in energy. The use of time series analysis and machine-learning techniques is conducive to helping energy managers discover more energy savings. [ABSTRACT FROM AUTHOR]

A smart controls system can control and optimize building services. This article aims to improve mosque energy efficiency. To ensure a comfortable environment, indoor temperatures, lighting performance, and illumination levels were monitored. DIAlux simulation software visualized the lighting for the indoor mosque environment. To assess the impact of smart occupancy sensors on total energy consumption, the DesignBuilder simulation tool was used. Mosque energy consumption can be significantly reduced. By replacing traditional lighting units inside mosques with LEDs, 9 % of the total energy could be saved. Utilizing a smart control plan with the mosque's energy index of 69 kWh/m2/yr could significantly reduce energy consumption. Since there are fewer prayers, except for Friday prayers and Ramadan, energy-regulating smart control systems are applicable during most prayer times. The study focuses on the mosque as a sustainable building and proposes a new concept for mosques to function as energy-efficient buildings in hot arid climate.

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.

This research seeks to improve the temperature control of AHU in building sub-levels using optimization algorithms. Specifically, the study applies the FA and PSO algorithms to optimize the PID control of AHU’s temperature. The study addresses the issue of temperature control in building sub-levels, which is a common challenge in HVAC systems. The study uses optimization algorithms and a nonlinear model to improve temperature control and reduce fluctuations in temperature from the desired setting. Additionally, a NL-ARX algorithm is utilized to create a nonlinear model based on the thermal dynamics and energy behavioral patterns of ACMV cooling systems. The study evaluates the performance of three controllers—PID, FA-PID, and PSO-PID—based on ITSE as a performance index. The study compares the performance of these controllers to achieve the desired temperature setting, and it analyses the influence of temperature regulation on occupant comfort levels. In this study, we compare different controllers using ITSE as a performance indicator. This shows how well different optimization algorithms work at setting the right temperature. The research gap is the lack of efficient temperature control solutions in building sub-levels that can optimize occupant comfort and energy efficiency. The experimental findings confirm that PSO-PID outperforms conventional PID and FA-PID optimization in terms of achieving the goal objective via computational complexity. Overall, this study’s focus is to explore and compare different optimization algorithms to improve temperature control and occupant comfort in building sub-levels.

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.

Bilgi ve iletişim teknolojilerinin ilerleyişi kullanıcı konforunu, sağlığını ve güvenliğini ön plana alan sistemlerin yaygınlaşmasına imkan sağlamaktadır. Günümüzde sürdürülebilir bir yaşam için akıllı binalar şehirlerde önemli örnekler teşkil etmeye başlamıştır. İnternetin ve bilgisayar teknolojilerinin gelişimi mimari tasarımda ve yapı sektöründe yeni sistemlerin kullanılmasına, akıllı binaların inşa edilebilmesine olanak sunmuştur. Dünya genelinde kaynakların verimli kullanımına, yaşam ve kullanım maliyetlerinin azaltılmasına, sağlıklı, güvenilir ve çevre dostu yapılara duyulan artan ihtiyaçlar bu gelişmeleri hızlandırmıştır. Bu çalışmada akıllı binaların gelişimine kadar olan süreçte toplumsal ve ekonomik unsurlar özetlenmektedir. Akıllı binaların tasarımında bilgi ve iletişim teknolojilerinin önemi ve desteği teknik olarak vurgulanmaktadır. Akıllı binaların değerlendirilmesinde başlıca kriterler ana başlıklar altında aktarılmaktadır. Araştırmada 2000 yılından sonra kullanıma açılan dünyanın farklı lokasyanlarından başarılı uygulama örnekleri olarak 5 farklı bina seçilerek incelenmektedir. Bu akıllı binaların, bilgi ve iletişim teknolojilerinden faydalanarak ortaya çıkan ortak özellikleri, tasarım kriterleri, sürdürülebilirlik uygulamaları ve yaşam kalitesini artırmaya yönelik katkıları belirlenerek çalışmada değerlendirilmektedir.

Smart building concepts are a relevant topic for the Ukrainian building sector. Although the current policy is mostly dedicated to the improvement of the current energy-saving situation in the building stock, the concept discussed could provide valuable inputs in the realization of the existing strategy. For an assessment of the expected positive impact of the implementation of smart indicators on building operation and renovation processes, as well as the predictions of scenarios of the range of the application of smart instruments, the research presented proposes a thorough analysis of the building stock in Ukraine and, in a real pilot case, the assessment of smart-readiness indicators (SRI) for a complex multi-family building. The case scenarios used, which are derived on the basis of SRI and a processed questionnaire among the habitants, are considered as the reference foundation for a practical application and disseminating the concept for similar cases.

Persistent poor acoustic conditions can imbalance humans’ psychophysical capabilities. A good acoustic project starts with either correct measurements of the existing acoustic parameters or with the correct hypothesis of new sound conditions. International standards define invasive measurement conditions and procedures that can disturb user activities. For this reason, alternative methodologies have been developed by mounting real-time sound-monitoring devices. Most of the research on these aims to decrease their dimensions in order to be placed in the tight service spaces of modern architecture and to reduce their aesthetic impact on interiors design. In this perspective, this article explores the features and potentialities of textile-based sound sensors (TSS) as they can not only fulfill these needs but can also be used as architectural ornaments by partially wrapping interiors. The ubiquitous of e-textiles for wearable applications has led to increasing the performance of TSS. Therefore, a comparison of the sensitivity values, signal-to-noise ratio and noise floor of sound TSS with sound sensors is presented, which is still missing in the literature. The paper demonstrates how these can be exploited for sound monitoring and can provide valid opportunities for new smart acoustic textiles.

The COVID-19 pandemic has generated new needs due to the associated health risks and, more specifically, its rapid infection rate. Prevention measures to avoid contagions in indoor spaces, especially in office and public buildings (e.g., hospitals, public administration, educational centres, etc.), have led to the need for adequate ventilation to dilute the possible concentration of the virus. This article presents our contribution to this new challenge, namely the Ventilation Early Warning System (VEWS) which has aims to adapt the operation of the current Heating, Ventilating and Air Conditioning (HVAC) systems to the ventilation needs of diaphanous workspaces, based on a Smart Campus Digital Twin (SCDT) framework approach, while maintaining sustainability. Different technologies such as the Internet of Things (IoT), Building Information Modelling (BIM) and Artificial Intelligence (AI) algorithms are combined to collect and integrate monitoring data (historical records, real-time information, and location-related patterns) to carry out forecasting simulations in this digital twin. The generated outputs serve to assist facility managers in their building governance, considering the appropriate application of health measures to reduce the risk of coronavirus contagion in combination with sustainability criteria. The article also provides the results of the implementation of the VEWS in a university workspace as a case study. Its application has made it possible to detect and warn of inadequate ventilation situations for the daily flow of people in the different controlled zones.

In recent years, renewable energies (RE) have gained more attention as they provide clean and sustainable energy. Affordable and clean energy is one of the sustainable development goals (SDG-7) of the UN. Solar energy is considered to be one of the world's most abundant renewable natural resources and can certainly contribute to meeting the SDGs. The photovoltaic (PV) panel converts solar energy into electrical energy without emitting greenhouse gases. PV panels generate power based on solar radiation received at a specific location over time. As a result, PV output power is difficult to forecast. Many public/private institutes generate green energy, and balancing demand and supply is vital. This research aims to forecast PV system output power based on weather and derived features using different machine learning (ML) models. A best-fitting model is developed based on the data to accurately predict output power. Moreover, different performance metrics are used to compare and evaluate the accuracy under different ML models such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP) and Support Vector Regression (SVR). Compared to the recent research, our results demonstrate that the obtained prediction accuracy is superior, with performance gains reaching up to 14% (R-squared) in the case of the RF model and 25% (MAPE) in the case of the SVR model. The comparison result inferred a similar performance when RF and XGBoost were applied. On the other hand, the ML models MLP, SVR, and KNN give a similar prediction pattern.

This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid. [ABSTRACT FROM AUTHOR]

Quality control is very important aspect in Building Information Modelling (BIM) workflows. Whatever stage of the lifecycle it is important to get and to follow building indicators. The BIM it is very data consuming field and analysis of these data require advance numerical tools from image processing to big data analysis. Artificial intelligent (AI) and machine learning (ML) had proven their efficiency to deal with automate processes and extract useful sources of data in different industries. In addition to the indicators tracking, AI and ML can make a good prediction about when and where to provide maintenance and/or quality control. In this article, a review of the AI and ML application in BIM will be presented. Further suggestions and challenges will be also discussed. The aim is to provide knowledge on the needs nowadays into building and landscaping domain, and to give a wide understanding on how those technics would impact industries and future studies. [ABSTRACT FROM AUTHOR]

Currently, large buildings are often equipped with building management systems (BMS), but either because of the type of control or the difficulty of interacting with them, they do not allow optimization policies to be implemented. As far as energy efficiency is concerned, optimization strategies often result from the analysis of monitored data by a specialist, which slows down both decision making and implementation. Aware of this problem, this project arises, in which a complementary platform to the BMS systems is developed to provide them with greater intelligence. For its design, the Puerta del Hierro Hospital in Madrid has been taken as a pilot case, from the data captured by its own BMS and using the 6 SIGMA methodology, algorithms have been developed capable of optimizing the production systems themselves, thus helping to make up for the weaknesses in the operation of the systems and reducing energy consumption, minimizing the environmental impact and allowing to obtain the maximum performance of its assets. The platform uses the information gathered by the BMS and weather forecasts to predict a building's energy demand and, based on this, optimize energy production at any given moment. [ABSTRACT FROM AUTHOR]

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Semantically rich depiction of the concepts for context-aware indoor routing brings appealing benefits for the safety of occupants of smart spaces in emergency evacuation. In this paper, we propose Smart Building Evacuation Ontology (SBEO3), a reusable ontology for indoor spaces, based on three different data models: user, building, and context. We provide a common representation of indoor routing and navigation, describe users' characteristics and preferences, grouping of individuals and their role in a specific context, hazards, and emergency evacuation. Among other characteristics, we consider abilities of individuals, safety and accessibility of spaces related to each person, intensity, impact, and severity of an emergency event or activity. SBEO is flexible and compatible with other ontologies of its domain, including SEAS, SSN/SOSA, SEMA4A, and empathi. We evaluate SBEO based on several metrics demonstrating that it addresses the information needs for the context-aware route recommendation system for emergency evacuation in indoor spaces. In the end, a simulation-based application example exploits SBEO using Context-Aware Emergency Evacuation Software (CAREE)4. [ABSTRACT FROM AUTHOR]

Smart building is a building development approach utilizing digital and communication technology to improve occupants' comfort inside the building and help increase energy usage efficiency in building operations. Despite its benefits, the smart building concept is still slowly adopted, particularly in developing countries. The advancement of computational techniques such as machine learning (ML) has helped building owners simulate and optimize various building performances in the building design process more accurately. Therefore, this study aims to assist energy efficiency design strategies in a building by identifying the features of the smart building characteristics that can potentially foster building energy efficiency. Furthermore, an ML model based on the features identified is then developed to predict the level of energy use. K-Nearest Neighbor (k-NN) algorithm is employed to develop the model with the openly accessible smart building energy usage datasets from Chulalongkorn University Building Energy Management System (CU-BEMS) as the training and testing datasets. The validation result shows that the predictive model has an average relative error value of 17.76%. The energy efficiency levels obtained from applying identified features range from 34.5% to 45.3%, depending on the reviewed floor. This paper also proposed the dashboard interface design for ML-based smart building energy management. [ABSTRACT FROM AUTHOR]

Data play an essential role in the optimal control of smart buildings' operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the monitoring system are likely to produce low-quality data, such as data loss and inconsistent data, which then seriously affect the control quality of the buildings. This paper proposes a new approach based on Gaussian process regression for data-quality monitoring and sensor network data compensation in smart buildings. The proposed method is proven to effectively detect and compensate for low-quality data thanks to the application of data analysis to the energy management monitoring system of a building model in Viet Nam. The research results provide a good opportunity to improve the efficiency of building energy-management systems and support the development of low-cost smart buildings. [ABSTRACT FROM AUTHOR]

Systems for determining the position of objects inside buildings have a wide range of applications, such as the surveillance of people's movements in hospitals, and of goods or mobile robots in warehouse spaces or production halls. Hence, there is a need for the development of methods that could be applied for those purposes. This paper presents the results of research on an experimental system for localizing people being evacuated from a building. The proposed solution was designed as a part of the building evacuation management system. The method used for finding location belongs to the class of proximity-type methods and is based on Received Signal Strength Indicator (RSSI) information of Bluetooth Low-Energy (BLE) devices. The devices used to build the system (BLE receivers) and the evacuee's wristband (BLE transmitters) are low-budget electronic modules. The paper presents preliminary research and the process of selecting data processing methods, as well as the results of tests of the experimental network created for the evacuation system. The results of measurements and statistical analyses of the properties of the RSSI parameter of the BLE signal transmission between the modules used in the designed system are presented. In addition, the results of RSSI measurements and the analyses of RSSI recorded under varying environmental conditions in the building are presented. The choice of the data processing method and its parameters was made with the use of the determined probabilities of the nearest locator node detection. Finally, the performance of the experimental installation of the evacuee tracking system was tested and the effectiveness of the proximity method was evaluated. The experimental tests aimed to analyze the detection range and the impact of shading. They also allowed for determining the mean error and for estimating the maximum position determination error. It should be emphasized that the proposed position estimation method has a very low computational load, allowing the implementation of an extensive real-time system on a typical personal computer. Although the proposed system should be classified as a coarse positioning system, its features such as low cost, simplicity, flexibility, the use of commonly available components and low requirements for computational load make it attractive. Such a system is directly transferable to other applications in, for example, Industry 4.0. [ABSTRACT FROM AUTHOR]