Journal articles: 'Smart high-Performance building' – Grafiati (2024)

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PurposeThis paper examines the use of intelligent technologies in buildings and whether the use of smart technologies impacts the circular economy performance of buildings in terms of energy and water consumption, their marginal cost and the management decision time and quality, for building management companies.Design/methodology/approachThe study is initiated through the detailed build-up of the proposition that employs a systematic literature review and adopts the case study research design to make a cross-case analysis of the information extracted from data. The data are derived from the operating costs of two buildings in which most advanced smart technologies are used in Cape Town and interviews with their facility managers. These data provide two research case studies. The results of the investigation are then analysed and linked back to the literature.FindingsThe results of the research suggest that the implementation of smart technologies to create intelligent infrastructure is beneficial to the circular economy performance of buildings and the time taken for management decisions. The results of the study have proven that the impact of smart technologies on the circular economy performance of buildings is positive, as it lowers the cost of utilities and decreases the time required for management decisions.Research limitations/implicationsThe research reported in this paper is exploratory, and due to its limited sample size, its findings may not be statistically generalizable to the population of high-occupancy buildings in Cape Town, which incorporate smart infrastructure technologies within their building management systems (BMSs). Also, the empirical data collected were limited to the views and opinions of the interviewees, and the secondary data were obtained from the selected buildings.Practical implicationsThe findings suggest that investment in smart technologies within buildings is of significant value and will improve the circular economy performance of buildings in terms of low energy and water use, and effective management decisions.Social implicationsThe results imply that there would be more effective maintenance decisions taken by facilities managers, which will enable the maintenance of equipment to be properly monitored, problems with the building services and equipment to be identified in good time and in improved well-being and user satisfaction.Originality/valueThe study provides evidence to support the concept that advanced smart technologies boost performance, the time required for management decisions and that they enable circularity in buildings. It supports the proposition that investment in the more advanced smart technologies in buildings has more positive rewards.

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Climate change and technological development are pushing buildings to become more sophisticated. The installation of modern building automation systems, smart meters, and IoT devices is increasing the amount of available building operational data. The common term for this kind of building is a smart building but producing large amounts of raw data does not automatically offer intelligence that would offer new insights to the building’s operation. Smart meters are mainly used only for tracking the energy or water consumption in the building. On the other hand, building occupancy is usually not monitored in the building at all, even though it is one of the main influencing factors of consumption and indoor climate parameters. This paper is bringing the true smart building closer to practice by using machine learning methods with sub-metered electricity and water consumptions to predict the building occupancy. In the first approach, the number of occupants was predicted in an office floor using a supervised data mining method Random Forest. The model performed the best with the use of all predictors available, while from individual predictors, the sub-metered electricity used for office equipment showed the best performance. Since the supervised approach requires the continuous long-term collection of ground truth reference data (between one to three months, by this study), an unsupervised data mining method k-means clustering was tested in the second approach. With the unsupervised method, this study was able to predict the level of occupancy in a day as zero, medium, or high in a case study office floor using the equipment electricity consumption.

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In the EU’s revised Energy Performance of Buildings Directive (EPBD), a smart readiness indicator (SRI) was introduced as an energy efficiency activity to promote smart ready technologies (SRT) in the building sector. The proposed methodology is based on the evaluation of building services and how they contribute on SRT. The purpose of this paper is to explore the applicability of the SRI to cold climate countries in Northern Europe. The Northern European countries are an interesting test environment for the indicator because of their advanced information and communication technology and high building energy consumption profiles. The findings imply that regardless of the SRI’s conceptualization as a system oriented (smart grid) approach, in its current form, it was not able to recognize the specific features of cold climate buildings, specifically those employing advanced district heating (DH) systems. Another, more practical, implication of the study was that due to the subjective nature of the proposed process for selecting SRI relevant building services, the applicability of SRI as a fair rating system across the EU member states is problematic.

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The high energy consumption, attached to a high energy demand in buildings, has led the development of several research projects with the target of reducing the energy consumption in the buildings. As a result of this high consumption, the increased CO2 emissions that have been generated in recent years, have reached alarming levels, which is why it is necessary to reduce the environmental impact which we are contributing to our planet through the use of energy. The European Directive on Building Performance (EPBD 2018/844/EU), recently updated, requires new buildings to be close to the Zero Energy Buildings (nZEBs), increasing the use of renewable energies on-site, and also highlight how to get to improve the cost-effective renovation of existing buildings with the introduction of building control and automation systems ( smart systems), as well as the energy savings and increase the efficiency of energy systems, by reducing CO2 emissions. The use of new renewable energy technologies integrated in buildings, with the aim of reducing the consumption of the facilities that all nZEB buildings must have, such as the ventilation system used as an Indoor Air Quality (IAQ) control technique. In this study, the energy management of the enthalpy ventilation control system is analysed, where dynamic monitoring is going on in the building controlled through Supervisory Control And Data Acquisition (SCADA), in combination with different ventilation systems as free-cooling, heat recovery and geothermal energy of an Earth Air Heat eXchanger (EAHX), all of them as strategies implemented in a real nZEB building (LUCIA) located on the campus at the University of Valladolid, with the goal of improving energy efficiency in ventilation. In order to get this aims, monitoring data of several energy parameters (temperature, air velocity, air flow rate, enthalpy, etc.) are measurements, they allow us to perform a control of the combined ventilation systems to achieve a high IAQ and analyze an optimization of the energy efficiency of the all systems and to study of energy recovery and savings of carbon emissions that directly affect the reduction of the impact of climate change. The results achieved are the energy efficiency of the building in ventilation and optimum system operation in cooling and heating mode. In addition, by controlling the ventilation, the IAQ of the nZEB building is improved.

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In a global world, the human population invariably increases while resources gradually decrease as cities and towns constantly consume resources to satisfy their needs and requirements. At this point, it is very necessary to focus on making these urban areas more sustainable and greener. The need for some advanced and automated systems improves the situation, which leads to the innovation of smart cities. Smart city is the concept that helps in developing sustainable cities via optimized resource utilization methods. In smart city development, various sensing technologies can be used that can sense and utilize natural resources in better ways, like storing rainwater to use afterward, intelligent and smart control system, smart infrastructure monitoring system, smart healthcare system, smart transportation system, and smart system for energy consumption and generation by various facilities. To make the city smart and sustainable with efficient energy consumption, we propose renewable solar and wind energy-enabled hybrid heating and cooling HVAC-DHW (heating, ventilation, and air conditioning-Domestic Hot Water) system in which energy consumption is evaluated using optimized NARX-ANN and fuzzy controller based on user needs, dynamic behavior of the atmospheric environment, and spatial distribution of energy supply. To achieve the proposed goal, first, via sensor, heating and cooling effect of environment and building is sensed and these sensed inputs are then fed into deep-learning-based NARX-ANN that forecast internal building temperature. This forecasted temperature is fed into a fuzzy controller for optimizing output based on user demand. This processed information leads to energy distribution based on their requirement using a smart energy sensing system. Based on the experimentation result and performance analysis, it was found that the proposed system is more robust and has a high control response in comparison to the existing systems with minimum energy consumption. The analytical results support the feasibility of the proposed framework architecture to facilitate energy conserving in smart city buildings.

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The increased smartness of the built environment is expected to contribute positively to climate change mitigation through energy conservation, efficient renewable energy utilization, and greenhouse gas emission reduction. Accordingly, significant investments are required in smart technologies, which enable the distributed supply of renewables and increased demand-side energy flexibility. The present study set out to understand the cash flows and economic viability of a real-life smart system investment in a building. The data collection process was threefold: First, a case building’s level of (energy) smartness was estimated. Second, the semi-structured interviews were held to understand the building owner’s motives for a smart investment. Third, the investment’s profitability was analyzed. The study found that the progressive smartness investment was technically feasible, and surprisingly also economically profitable. The original EUR 6 million investment provided over 10% return-on-investment and, thus, increased the property value by more than EUR 10 million. Moreover, the commercial partners also emphasized the strategic value gained by renewable energy and environmental performance. The high level of smartness with a good return on investment was accomplished mainly through new income generated from the reserve power markets. However, the results implied that financial profitability alone was not enough to justify the economic viability of a smart building system investment.

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An educational building must integrate smart building strategies to ensure indoor environmental quality. Thermal, acoustic, visual comfort and indoor air quality are to be considered, otherwise they can develop the sick building syndrome. Smart buildings solve this potential problem by providing a highly efficient living ambience that includes safety, comfort and a good quality of living/learning/working experience, that helps the users achieve their best possible performance. These buildings should integrate advanced technologies such as automated systems and the implementation of architectural skins, well and functional designed spaces and architectural features that act as active bioclimatic solutions. The following is a case study of an architectural project for an elementary and junior high school academic campus in the state of Nuevo León, Mexico that has to deal with the extreme climate conditions of the location, while applying the best alternative and bioclimatic strategies through the implementation of inmotics, a responsive architectural skin, sustainable construction systems and native vegetation. In doing so, a comprehensive environmentally friendly building is created, taking advantage of the surrounding natural conditions, using the latest environmentally oriented systems and technologies. The result is a healthy, safe, and productive space for its users that greatly benefits the teaching-learning process.

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Smart cities allow cities to run more efficiently and have been approved by a lot of cities. During the process of building smart cities, a large amount of data is generated. Particularly, live sports events have been regarded as the inalienable part of smart cities. However, with the improvement in the quality of life, people tend to obtain better watching experience in terms of sports events. For such purpose, this paper proposes the live streaming transmission optimization method based on high-performance server, called HPTO, including two main modules, that is, high-performance server optimization and transmission optimization. Specifically, for the server optimization, this paper devises a distributed storage strategy to avoid producing the internal disk fragments and improve the writing efficiency of sports videos. For the transmission optimization, this paper devises a deep-learning-based video compression strategy to save the storage space of server and accelerate the transmission of sports videos. In addition, this paper makes simulation experiments based on PyCharm. The experimental results show that HPTO has higher storage efficiency, smaller transmission time, and lower packet loss rate than benchmarks, which indicates that the proposed two aspects of optimization strategies (server optimization and transmission optimization) are efficient.

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In recent years, the growing awareness of the possibilities of biomimetic and adaptive materials in architecture has assumed a fundamental importance in the scientific domain, for the high performance of the building façades from an environmental point of view. To meet the housing and comfort needs in climates, such as the ones in the Mediterranean zones, characterized by ever-increasing temperatures, architectural technology must collaborate with nature in an even more decisive way, through increasingly smart and sustainable solutions. The paper reviews a collection of good practice examples of advances in material science, and the method used is to analyze the current performance of and building envelopes with smart façade skins, in order to suggest some potential applications in the Mediterranean basin and regions of the world with similar climatic characteristics.The latter case studies, especially, show built examples of adaptive buildings that could be adopted for use in Mediterranean regions. When the climate characteristics are somehow different, the good practices from elsewhere can be implemented in an innovative way.

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In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the advantages of reliable analysis and high efficiency. However, the performances of existing machine learning–based damage identification methods are heavily dependent on the selected signatures from raw signals. This will cause the fact that the damage identification method, which is the optimal solution for a specific application, may fail to provide the similar performance on other cases. Besides, the feature extraction is a time-consuming task, which may affect the real-time performance in practical applications. To address these problems, this article proposes a novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices. The proposed deep convolutional neural network is capable of automatically extracting high-level features from raw signals or low-level features and optimally selecting the combination of extracted features via a multi-layer fusion to satisfy any damage identification objective. To evaluate the performance of the proposed deep convolutional neural network method, a five-level benchmark building equipped with adaptive smart isolators subjected to the seismic loading is investigated. The result shows that the proposed method has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods. Accordingly, it is deemed as an ideal and effective method for damage identification of smart structures.

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Risk prevention of historical buildings through sensor network monitoring represents a challenging and together promising task towards an optimized buildings’ protection plan. Due to the significant character that historical buildings have within the history and identity of local and consequently national communities, the monitoring approach above is nowadays considered a fundamental component of cultural preservation. Modern trends on sensor networks nowadays allow the installation of wireless monitoring equipment, able to collect a large volume and variety of data that can be further processed in order to support prevention modelling techniques and strategies. Despite the valuable post-processing outcome of the above modelling techniques, in several real-time monitoring cases, an aspect that may significant impact the data analysis accuracy from the monitoring engineer is to create an audiovisual representation of the gathered data that are easily perceived and directly associated to well-known environmental conditions. Towards this aim, in this work an audio-based representation of the collected data is introduced that achieves high monitoring performance in terms of the perceived nature of the building vibrations themselves. The proposed audio-based representation technique is based on a number of advanced sonification methods, combined with specific human-hearing simulation mechanisms that allow an effective increase of the represented information volume in real-time. A number of subjective tests that were performed demonstrate a significant improvement on the way that the monitoring personnel can efficiently perceive the origin and / or nature of the acquired vibrations, rendering the proposed technique a strong supplement towards efficient historical building risk prevention.

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As a typical composite structural type, concrete-Filled Steel Tubes (CFSTs) have been widely employed in civil engineering structures, especially in super high-rise buildings because of their enhanced load-carrying capacity and ductility. The uniformity and quality of the on-site cast concrete within the tube and the interface bonding performance between the steel tube and the concrete have received extensive attention. In this paper, Piezoelectric ceramic materials, such as Lead Zirconate Titanate (PZT) and a kind of functional smart aggregate (SA) based on PZT together are employed to evaluate the concrete quality and the bonding performance of CFST columns of a super high-rise building with a design height over 400 meters and with large cross-section CFST columns. Based on the wavelet packet analysis on the measurements of the PZT sensors, results show that no obvious damage and defect is detected and the concrete qualify and the interface of large cross-section CFST columns of this super high-rise building are in good condition. The proposed monitoring technology has great potential to be applied to practical engineering.

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A smart power integrated circuit to be fabricated with standard CMOS technologies was developed in view to obtain a versatile, high performance and low cost basic building block, suitable for a wide range of low power applications. This circuit merges together two transistors, connected in a low-side/high-side switch configuration, with specific control and protection circuitries. These transistors are NMOS medium-voltage lateral structures, which use the lightly doped drain concept and are targeted to handle currents up to 2 A and to support 25 V at OFF state. Experimental results on different applications and topologies show the applicability of the smart switching cell on portable systems power supplies and amplifiers (up to 20 W). Its performance also proves the ability of standard CMOS technologies to implement smart power circuits.

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<p><strong>Abstract.</strong> 3D building models are needed for urban planning and smart city. These models can be generated from stereo aerial images, satellite images or LiDAR point clouds. In this paper, we propose a geometric object-based building reconstruction method from satellite imagery derived point clouds. The goal is to achieve a geometrically correct, topologically consistent, and non-redundant 3D representation for buildings in urban areas. The paper first introduces our motivation, followed by a comprehensive review on related works. We then introduce the methodology and process developed in this paper. Primary results from the point clouds generated from WorldView high resolution satellite images are used to demonstrate the performance of the approach.</p>

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The object of this paper is the “Loccioni Leaf Lab”, an industrial nZEB connected to a thermal and electric smart grid. Having nZEB buildings connected to a smart grid offers the possibility of maximizing the benefits that can be obtained by optimal regulation of the grid itself, providing excellent economic and energy results. The case study, which hosts offices and workers operating on test benches, features high performance envelope, solar photovoltaic systems, groundwater heat pumps and a hightechnology control and monitoring system. In order to analyse HVAC-related energy consumptions, the building was modelled using DesignBuilder and EnergyPlus software. The annual dynamic simulations for the assessment of building thermal-energy performance were carried out using available monitored weather data (2019). The model was validated according to ASHRAE guidelines, comparing the outputs of the software with data collected and stored by Company internal database. In the validation process, mean indoor air temperatures of several zones and heating and cooling energy consumptions were considered as key outputs. The validated model has then been used to suggest optimization strategies and to analyse the results obtained with proposed interventions in terms of energy saving.

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Monitoring energy in buildings can ease us to have a better understanding of electricity consumption patterns to support efficiency and avoid potential damages. However, indoor installations are mostly unmonitored because their panel meters are usually difficult to access. Yet, indoor maintenance tends to be more difficult since the cables are inside the wall, ceiling, or concrete. Internet of Things and big data analytics can be used to track electricity usage either in residential, commercial, or industrial buildings. This paper presents how a simple real-time energy data analytics was built at a low cost and high accuracy to inspect energy fluctuations, anomaly, and its significant pattern. We proposed 3 layers of architecture namely acquisition, transportation, and application management. An electronic module named PZEM004T was used to sense voltage, current, and other electrical parameters. Through a microcontroller ESP8266, the data was processed and sent it to an application layer via an existing wireless network. The actual and historical data of electricity were visualized on high-resolution graphs. The experiment was conducted at our office building. The experimental results showed that data of electrical energy usage can be captured close to realtime and power anomaly and pattern can be figured. Performance and functionality testing showed acceptable use of this system with more than 99% accuracy. This system is intended to empower building managers in evaluating the electrical network balance as well as anticipating damage due to overload, overvoltage, and voltage drop. If this model is widely implemented it will produce big data that is useful for advanced analysis.

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At the most fundamental level, smart buildings deliver useful building services that make occupants productive. Smart asset management in hostipals starts with knowing the whereabouts of medical equipment. This paper investigates the subject of indoor localization of medical equipment in hospitals by defining functional spaces. In order to localize the assets indoors, a localization method is developed that takes into account several factors such as geometrical influences, characteristics of the Quuppa positioning system and obstructions in the indoor environment. For matching the position data to a real world location, several location types are developed by subdividing the floor plan into location clusters. The research has shown that a high-performance level can be achieved for locations that are within the high-resolution range of the <i>receiver</i>. The performance at the smallest subspaces can only be achieved when having a dense distribution of <i>receivers</i>. Test cases that were defined for specific situations in the test-area show successful localization in these subspaces for the majority of the test data.

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At the most fundamental level, smart buildings deliver useful building services that make occupants productive. Smart asset management in hostipals starts with knowing the whereabouts of medical equipment. This paper investigates the subject of indoor localization of medical equipment in hospitals by defining functional spaces. In order to localize the assets indoors, a localization method is developed that takes into account several factors such as geometrical influences, characteristics of the Quuppa positioning system and obstructions in the indoor environment. For matching the position data to a real world location, several location types are developed by subdividing the floor plan into location clusters. The research has shown that a high-performance level can be achieved for locations that are within the high-resolution range of the <i>receiver</i>. The performance at the smallest subspaces can only be achieved when having a dense distribution of <i>receivers</i>. Test cases that were defined for specific situations in the test-area show successful localization in these subspaces for the majority of the test data.

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Smart meters are currently being rolled out in European district heating (DH) systems at a large scale to enable time-varying district heating tariffs and improve consumer awareness about their own consumption. Smart-meter data can also be used in more advanced applications, e.g. for establishing model-based control schemes for demand response purposes and data-driven building energy performance labeling schemes. Many of these applications require separate measurements of the consumption for space heating (SH) and preparation of domestic hot water (DHW); however, smart meters often only provide the total DH energy consumption (SH+DHW) in truncated units (e.g. whole kWh on an hourly basis). Typical approaches for separating these two components of DH consumption require measurements with a high temporal and numerical resolution and are therefore not applicable to smart-meter data. New methods suitable for disaggregating the combined DH demand are therefore needed. This paper presents a validation of a model-based method for disaggregating DH consumption using ground truth data from 44 residential buildings.

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Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

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The considerable thicknesses of common insulation systems, applied to the internal or external building envelope, can be inappropriate in the Mediterranean climates for improving buildings? energy efficiency and their internal comfort at the same time; in fact, the high thicknesses of insulating material provided by legislation standards can be cause of environments? over-heating and formation of condensation. In this framework, the S-MUnSTa system is an innovative dynamic ventilated insulation system able to overcome condensation and overheating phenomena, also exploiting Internet of Things technologies; the main characteristic of the proposed smart insulation is that the ventilated external layer is equipped with dynamic valves of insulating material, for opening and closing the air channel, with the aim to optimize the thermal performance. In order to guarantee the expected performance of the system, as it has been patented, in this paper an innovative fixing system to install the insulating panels is presented. This new method allows a rapid and easy installation, without any specialization required and with low maintenance costs.

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Although wireless smart sensor platforms have been available over a decade, only a limited number of full-scale wireless smart sensor–based structural health monitoring implementations have been realized. Most wireless smart sensor platforms that are validated in full-scale implementations have now become obsolete and are no longer commercially available. While wireless sensing capabilities have grown, presenting significant opportunities, obstacles to wide application of wireless smart sensor for structural health monitoring exist both in terms of hardware and software. This article assesses the efficacy of the Xnode, a new wireless platform whose development has been driven by structural health monitoring requirements, as well as lessons learned from several full-scale wireless smart sensor deployments. The capabilities of the platform are evaluated in comparison with other commercial wireless smart sensors, in terms of hardware, software, and mechanical design. Extensive laboratory and field testing is employed to validate its performance on three aspects: fidelity of data acquisition, reliability of wireless communication, and efficiency of power management. Test results demonstrate the capabilities of the Xnode to support full-scale, high-fidelity data acquisition for civil infrastructure. In addition, the new sensor platform provides several significant benefits to extend the use of wireless smart sensors to a broader class of structural health monitoring applications, such as sudden event monitoring and real-time and control applications.

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Climate change (CC) is predominantly connected to greenhouse gas (GHG) emissions from the construction sector. It is clear how it is necessary to rethink construction materials in order to reduce GHG emissions. Among the various strategies proposed, recent research has investigated the potential of smart materials. This study in particular aims to develop an innovative building component that combines high energy performance with reduced thickness and weight. For this reason, the potential of Phase Change Materials (PCM) in cement-based mixes is investigated, comparing the performance of a traditional mix with two innovative mixes made with the addition of 3% and 7% PCM. This work characterizes the new material, analyzing its mechanical and thermal performance, highlighting how the mix strength decreases as the PCM ratio increases; however, both mixes may be considered suitable for masonry structures and may be classified as M5 and M15. Furthermore, from the analysis of the thermal performance, it emerges that the mix presents good behavior in terms of insulating properties.

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This article discusses research case studies that deploy physical computing with kinetic, pneumatic, and smart material technologies as vehicles to address the prospects of these technologies and their future impact on resilient and high-performance buildings. It looks into conceptual aspects of an integrated hybrid system that combines both computation approaches and unique opportunities inherent to these hybrid designs.

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As an important member of smart materials, magnetorheological elastomers (MREs) exhibit characteristics that their modulus can be controlled by an external magnetic field. Based on these experimental results, a viscoelastic solid model with four parameters was proposed to predict the performance of MRE. A building model, three stories high, was constructed using MATLAB SIMULINK to evaluate the performance of an MRE device in structural control. In addition, the performance of an MRF damper and an MRE device in structural control, where the resultant peak force was selected as a criterion in the evaluation process, was compared and discussed. Two controllers, passive on and passive off control strategy were used to compare the response of structure. The effectiveness of an MRE bearing in structural control was well justified.

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The complexity and variety of buildings and the defects of point cloud data are the main challenges faced by 3D urban reconstruction from point clouds, especially in metropolitan areas. In this paper, we developed a method that embeds multiple relations into a procedural modelling process for the automatic 3D reconstruction of buildings from photogrammetric point clouds. First, a hybrid tree of constructive solid geometry and boundary representation (CSG-BRep) was built to decompose the building bounding space into multiple polyhedral cells based on geometric-relation constraints. The cells that approximate the shapes of buildings were then selected based on topological-relation constraints and geometric building models were generated using a reconstructing CSG-BRep tree. Finally, different parts of buildings were retrieved from the CSG-BRep trees, and specific surface types were recognized to convert the building models into the City Geography Markup Language (CityGML) format. The point clouds of 105 buildings in a metropolitan area in Hong Kong were used to evaluate the performance of the proposed method. Compared with two existing methods, the proposed method performed the best in terms of robustness, regularity, and topological correctness. The CityGML building models enriched with semantic information were also compared with the manually digitized ground truth, and the high level of consistency between the results suggested that the produced models will be useful in smart city applications.

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Three-dimensional (3D) pipe network modeling plays an essential part in high performance-based smart city applications. Given that massive 3D pipe networks tend to be difficult to manage and to visualize, we propose in this study a hybrid framework for high-performance modeling of a 3D pipe network, including pipe network data model and high-performance modeling. The pipe network data model is devoted to three-dimensional pipe network construction based on network topology and building information models (BIMs). According to the topological relationships of the pipe point pipelines, the pipe network is decomposed into multiple pipe segment units. The high-performance modeling of 3D pipe network contains a spatial 3D model, the instantiation, adaptive rendering, and combination parallel computing. Spatial 3D model (S3M) is proposed for spatial data transmission, exchange, and visualization of massive and multi-source 3D spatial data. The combination parallel computing framework with GPU and OpenMP was developed to reduce the processing time for pipe networks. The results of the experiments showed that the hybrid framework achieves a high efficiency and the hardware resource occupation is reduced.

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After the Industry 4.0 discussion in Germany in 2011, much attention has been paid to smart factory in Korea. Since 2014, smart factories have been established and expanded in Korea. However, about 80% of them were established at a low level. In this paper, we analyze smart factory statuses in detail through an empirical research on 113 manufacturing companies that have established smart factories in Korea. We build a framework based on the resource-based view (RBV) and IT value creation process and analyze the results of five constructs—manufacturing strategy, organization, system, process, and performance—using basic statistical methodologies to derive the current statuses of manufacturing companies that have established smart factories. Our results show that implementing advanced technologies such as AI technology that can implement semi-finished and finished product quality inspection, manufacturing process optimization and product demand forecast is a challenge, particularly for SMEs. We also find that securing and managing facility data is a difficult problem. In addition, while output and material management ranked high, the utilization of integration systems, which is important when building a smart factory, was found to be extremely low. Lastly, the performance indicator results showed that yield management and defect rate were most important, while job creation through the introduction of smart factories was low. Based on the results of this study, the government may be able to determine effective smart factory policies and provide manufacturing companies with a guide on establishing a smart factory.

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The limited number of nurses is a problem that often occurs in the world of health which has an impact on the high workload of nurses resulting in less optimal patient care and decreased patient satisfaction. According to Institute for Healthcare Improvement, nurses spend only 20-30% of their time in direct patient care, the rest on documentation and administration processes. So, hospitals generally provide nurse calls so families don't have to leave patients to find nurses.The purpose of this study was to make it easier for patients to use Smart Nurse Call, especially for patients with acute diagnosis conditions and it's easier for nurses to monitor patient's condition. It's also examines the distance of data transmission in one building to determine the performance of wireless (access points).Based on data from test results, the quality of data transmission using wireless media is known that access point has data transmission limit. In testing distance at AH building stated that data transmission based on the range on the 1st floor is able to reach the entire corridor, for 2nd floor it can reach a distance of 13 meters while on 3rd floor it cannot be reached. The data from suitability test of smart nurse call tool showed that the suitability level of the device was quite high and almost same as manual test at 97.65%. The data from stress test results from push button get average voltage value low position at 0 V and average high voltage at 4.675 V.

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Shape Memory Alloys (SMA) is type of smart materials that have ability to undergo large deformation and return back to their undeformed shape through heating (shape memory effect) or removal of load (superelastic effect). This unique ability is useful to enhance behavior of structure and seismic resistance. In this paper, superelasticity (SE) effect of NiTi alloys is used to improve the structural characteristics of steel building. The finite element analysis of steel building is done using ABAQUS v.2017. In order to compare the structural behavior of the steel building equipped with Shape Memory Alloy bars at beam-column connection, three steel building was modeled with a different combination of high strength steel bars and SMA bars. The steel building was checked for time history analysis by using Vrancea 1977 earthquake data. In order to estimate the recentring ability, residual of roof displacement and energy dissipation. The steel building equipped with SMA bars shows 82.7%, 152.72% recovery in residual roof displacement for steel building equipped with 50% SMA bars and 50% HS steel bars and steel building equipped with 100% SMA bars respectively, and moderate energy dissipation. In general, the frame equipped with 50% superelastic SMA bars and 50% HS steel bars provided better seismic performance.

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This paper aims at presenting an innovative methodological approach to identify the heat storage and heat conductivity coefficients of a smart house. For this purpose, an experimental room is selected and heated to a high degree and then left for cooling, during which the reduction in the room temperature is measured with time. The time–temperature record provides the basis for determining the heat storage and heat conductivity coefficients through an objective standard curve matching procedure as the main context. These coefficients help to calculate the heat performance of a single room and, accordingly, the whole building, depending on its surrounding environment, construction material, volume and meteorological conditions. The proposed methodology depends on the one-dimensional heat flow event assuming that the construction material is hom*ogeneous and isotropic and that the heat scatters uniformly in the room. The novelty in the paper is the standard curve for time–temperature decrease record evaluation, which can be used in a smart building planning procedure for decisions on heat storage and transfer.

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The work is devoted to solving an urgent scientific problem - increasing the informativeness of methods for assessing the effectiveness of energy saving measures in the areas of heat supply and heat consumption. The pur-pose of the research was to create and implement a method for multicriteria evaluation of the effectiveness of en-ergy-saving measures for the thermal energy of buildings. The following indicators are used as criteria for as-sessing the effectiveness of the studied measures in this method: 1) energy effect - the absolute and relative value of the saved thermal energy; 2) environmental effect - the amount of fuel saved, reducing emissions of greenhouse gases and pollutants into the atmosphere; 3) economic effect - the cost of saved thermal energy and fuel. The prac-tical implementation of the created method was carried out on the basis of 2 full-scale objects: No. 1 - a 3-story fragment of the administrative building of Kharkov National University of Municipal Economy them. A.N. Beketo-va with a total heated area of 225 m2 and No. 2 - the building of the communal institution "Kharkov Palace of Children and Youth Creativity" with a total heated area of more than 13700 m2. For full-scale object No. 1, the effectiveness of using «smart home» technology to control the thermal conditions of the building’s premises using the HERZ Smart Comfort automated system has been evaluated. For full-scale object No. 2 was evaluated the ef-fectiveness of 2 energy-saving measures: controlling the thermal conditions of the building’s premises with the HERZ Smart Comfort system and using an alternative heat source - heliosystem, which consists of 180 solar collec-tors. The research results showed such properties of the proposed method for evaluating the effectiveness of ener-gy-saving measures, such as: high information content of the results, providing the possibility of multicriteria optimization of the parameters of heat supply systems and heat consumption of the research object, ability to im-prove by increasing the number of studied performance indicators. Keywords: heat supply, heated area, energy saving measures, efficiency, complex assessment, natural object, educational institution.

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In order to control the vibration of civil building structures, a smart extrusion-type isolator was developed based on magnetorheological gel (MRG) and magnetorheological elastomer (MRE). The key technology and performance tests of the isolator were investigated as well as the identification of parameters of the mechanical model. Test results showed that the MRG cylinder has a damping characteristic at high frequency while the MRE cylinder has an isolation characteristic at low frequency. The designed isolator is therefore superior over the traditional isolator since it will show small damping and low dynamic stiffness at a high frequency and small amplitude situation, which can overcome stiffness hardening that occurs on the traditional isolator. Meanwhile, the designed isolator will also have the behavior of large isolation and high dynamic stiffness under the low frequency and large amplitude condition, which has the advantage of realizable displacement control. The uniaxial mechanical model for the MRG/MRE smart isolator was built, and the parameters of the designed vibration isolator were identified. Theoretical results obtained from the mechanical model of the MRG/MRE smart isolator agree well with the experimental results indicating that the parameter identification method is feasible and effective.

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The recent drop in oil prices has lead Oman, a country with an oil dependent economy, to consider new sustainable policies and attitudes.The Sultanate’s five-year plan (2011-2015) began the development of renewable energy and environmental protection. One of the state’s approaches was hosting the National Eco-House Design contest in 2011 between higher educational institutions. However, disseminating the eco-house concept will take decades to be effective. This commentary argues for the potential of the Smart -Eco house to provide a quick and effective solution to support the present nationwide policy of promoting more sustainable practices. Based on relevant literature review, a questionnaire was written, and interviews were conducted with high ranking government policymakers and directors of smart home companies. The paper aims to draw the attention of the government, experts, decision-makers, and educational institutions to integrate the Smart – Eco house into their efforts towards efficient building energy performance, supporting the national economy, energy infrastructure, and the country’s environmentally sustainable policies.

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The challenges of the Internet of Things (IoT) in an urban environment are driven by smart vehicles which need to be able to efficiently sense and communicate with other nearby vehicles. The automotive market have strict circuit performances and reliability requirements for a temperature range of up to 175 ◦C. This proposal overviews an analysis of latched-comparators performance, considering process variability and temperature variation of previous works. This analysis is then extended to the metastability and performance metrics of successive approximation register (SAR) analog-to-digital converter (ADC) topology. Building blocks necessary for the SAR ADC are designed using an XH018 technology. Post-layout simulation results are drawn to validate the proposed temperature-aware analysis. Besides the known advantages of the Double-Tail comparator, this work demonstrates that such a comparator has a serious drawback under harsh environments. This proposal also shows that, once calibrated and operated at a frequency of around 100 MHz, the SAR ADC performance can be maintained in a wide temperature range. Both SA- and DT-SAR ADC achieve an ENOB of 9.8 bits, which is reduced to 9.6 bits in high-temperature operation. The results also show that background calibration is not required for the SAR ADC operation at the 100 MHz frequency range.

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Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.

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This paper presents a new smart monitoring system designed based on dc to dc converter for photovoltaic application. This system design according two parts to monitor input-output voltages and currents for dc/dc converter, (a) control system: the control system using Arduino NANO as microcontroller to read the measuring voltage and current values from sensor circuits of voltage and current. The measuring data send by Bluetooth HC-05 to end user (monitor system). Bluetooth as wireless communication between the control system and monitoring system (end users). (b) monitoring system: The monitoring system application program as a new application designed to monitor the received data from control system from safety distance (around 10m). the application program designed by the open source AppyBuilder software. The AppyBuilder is an open source software for easily building Android smartphone application. The advantages of the final circuit can be used to monitor step-up or step-down topologies, low-cost, and high-efficiency performance.

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Ultra-Precision Machining (UPM) is a kind of highly accurate processing technology developed to satisfy the manufacturing requirements of high-end cutting-edge products including nuclear energy producers, very large-scale integrated circuits, lasers, and aircraft. The information asymmetry phenomenon widely exists in the design and control of ultra-precision machining. It may lead to inconsistency between the designed performance and operational performance of the UPM equipment on stiffness, thermal stability, and motion accuracy, which result from its design, manufacturing, and control, and determine the form accuracy and surface roughness of machined parts. The performance of the UPM equipment should be improved continuously. It is still challenging to realize the real-time and self-adaptive control, in which building a high-fidelity and computationally efficient digital twin is a valuable solution. Nevertheless, the incorporation of the digital twin technology into the UPM design and control remains vague and sometimes contradictory. Based on a literature search in the Google Scholar database, the critical issues in the UPM design and control, and how to use the digital twin technologies to promote it, are reviewed. Firstly, the digital twins-based UPM design, including bearings module design, spindle-drive module design, stage system module design, servo module design, and clamping module design, are reviewed. Secondly, the digital twins-based UPM control studies, including voxel modeling, process planning, process monitoring, vibration control, and quality prediction, are reviewed. The key enabling technologies and research directions of digital twins-based design and control are discussed to deal with the information asymmetry phenomenon in UPM.

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Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.

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Supply chain is one of the main pillars of manufacturing and industrial companies whose smartness can help business to be intelligent. To this end, the use of innovative technologies to make it smart is always a concern. The smart supply chain utilizes innovative tools to enhance quality, improve performance and facilitate the decision-making process. Internet of things (IoT) is one of the key components of the IT infrastructure for the development of smart supply chains that have high potential for creating sustainability in systems. Furthermore, IoT is one of the most important sources of big data generation. Big data and strategies for data analysis as a deep and powerful solution for optimizing decisions and increasing productivity are growing rapidly. For this reason, this paper attempts to examine informative supply chain development strategies by investigating the supply chain in FMCG industries as a special case and to provide a complete analytical framework for building a sustainable smart supply chain using IoT-based big data analytics. The proposed framework is based on the IoT implementation methodology, with emphasis on the use of input big data and expert reviews. Given the nature of the FMCG industry, this can lead to better production decisions.

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Automatic building extraction from remote sensing imagery is important in many applications. The success of convolutional neural networks (CNNs) has also led to advances in using CNNs to extract man-made objects from high-resolution imagery. However, the large appearance and size variations of buildings make it difficult to extract both crowded small buildings and large buildings. High-resolution imagery must be segmented into patches for CNN models due to GPU memory limitations, and buildings are typically only partially contained in a single patch with little context information. To overcome the problems involved when using different levels of image features with common CNN models, this paper proposes a novel CNN architecture called a multiple-feature reuse network (MFRN) in which each layer is connected to all the subsequent layers of the same size, enabling the direct use of the hierarchical features in each layer. In addition, the model includes a smart decoder that enables precise localization with less GPU load. We tested our model on a large real-world remote sensing dataset and obtained an overall accuracy of 94.5% and an 85% F1 score, which outperformed the compared CNN models, including a 56-layer fully convolutional DenseNet with 93.8% overall accuracy and an F1 score of 83.5%. The experimental results indicate that the MFRN approach to connecting convolutional layers improves the performance of common CNN models for extracting buildings of different sizes and can achieve high accuracy with a consumer-level GPU.

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Most European cities are facing urban densification issues. In this context, a solution to create usable spaces without additional pressure on land consists in the vertical extension of existing buildings. Given their abundance in the building stock, tertiary buildings offer an important potential. The paper introduces the Working Space project, which aims to develop an innovative, modular and prefabricated timber construction system adapted to the vertical extension of existing office buildings. The dimensions of the system can be adjusted to a great variety of structural grids and allows for any new typological organisation. Based on the principles of bioclimatic architecture, the extension’s envelope provides high-performance insulation, a smart management of passive solar gains, natural ventilation and free cooling, but also offers large surfaces dedicated to photovoltaic energy production and urban biodiversity. The system is made up of eco-friendly, local materials with very low environmental impact. The project’s outcomes are presented at a variety of scales, from urban design to construction details, as well as the outputs of an extensive life cycle assessment including the induced mobility impacts. Finally, the paper introduces a first application of this innovative architectural concept, which is currently being completed in Lausanne, Switzerland.

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As the electronics in our daily lives proliferate, they continue to be largely limited to rigid form factors with bulky packaging dictated by traditional electronics manufacturing processes and fragile components. Yet for applications ranging from wireless, low-profile medical devices to smart food labels to aircraft with embedded stress sensors, there's a need for high-performance electronics that conform to the shape of our bodies, vehicles, and consumer goods. Flexible Hybrid Electronics (FHE), which combine additive manufacturing processes with flexible silicon will enable these capabilities. To move these concepts from the lab to the manufacturing floor in the United States, the Department of Defense established NextFlex, America's Flexible Hybrid Electronics Manufacturing Institute in 2015. Based in San Jose, CA, NextFlex is a $170M public-private partnership that is building a domestic FHE manufacturing ecosystem by developing manufacturing processes and tools with its member companies and universities, standing up an FHE manufacturing pilot line in Silicon Valley, and establishing education and workforce development programs to train tomorrow's workforce. This presentation will focus on the FHE opportunity, the NextFlex FHE manufacturing roadmaps, and NextFlex projects in areas such as device integration & packaging, modeling & design tools, and printed flexible components.

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Aerial scenes captured by UAVs have immense potential in IoT applications related to urban surveillance, road and building segmentation, land cover classification, and so on, which are necessary for the evolution of smart cities. The advancements in deep learning have greatly enhanced visual understanding, but the domain of aerial vision remains largely unexplored. Aerial images pose many unique challenges for performing proper scene parsing such as high-resolution data, small-scaled objects, a large number of objects in the camera view, dense clustering of objects, background clutter, and so on, which greatly hinder the performance of the existing deep learning methods. In this work, we propose ISDNet (Instance Segmentation and Detection Network), a novel network to perform instance segmentation and object detection on visual data captured by UAVs. This work enables aerial image analytics for various needs in a smart city. In particular, we use dilated convolutions to generate improved spatial context, leading to better discrimination between foreground and background features. The proposed network efficiently reuses the segment-mask features by propagating them from early stages using residual connections. Furthermore, ISDNet makes use of effective anchors to accommodate varying object scales and sizes. The proposed method obtains state-of-the-art results in the aerial context.

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The aim to reduce carbon dioxide emissions and to improve buildings energy efficiency has led to develop smart grids to manage electricity and heat. The work deals with the thermal and electric microgrid analysis of the Loccioni Company: the production facility consists of high-energy performance industrial and office buildings, partially powered by produced on-site renewable energy (solar PV, hydropower plants, water ground heat pumps). The challenge of continuous improvement in the use of energy led the company to develop a thermal microgrid for optimal management of the heat produced by a small-scale CHP system (50 kWel/110 kWth). The cogeneration system is based on biomass (pellet) chemical degradation process of gasification. The work discusses preliminary results relating to the first months of the system operation, to highlight the energy benefits and the critical issues. Data are collected by the Company monitoring system and a laboratory feedstock physico-chemical characterization is carried out. The main goal of this paper is to lay the foundations for the development of an energy management system that regulates energy flows between buildings. The development of the thermal microgrid will guarantee not only tangible benefits in terms of energy savings but also an increase in the resilience of the entire building/plant system.