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The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach of testing the GNSS signal receiver on the ground is mainly the mountaintop-based testing approach, which has problems such as high cost and large simulation error. In order to overcome the limitations of the mountaintop-based test approach, this paper proposes an accurate, repeatable, and controllable GNSS atmospheric occultation simulation system and builds a load performance evaluation approach based on the ground-based GNSS atmospheric occultation simulation system on the basis of it. The GNSS atmospheric occultation simulation system consists of the visualization and interaction module, the GNSS-RO simulation signal generation module, the GNSS-RO simulator module, the GNSS-RO signal receiver module, and the GNSS-RO inversion and evaluation module, combined with the preset atmospheric model to generate GNSS-RO simulation signals with a high degree of simulation, and comparing the atmospheric parameters of the inversion performance of the GNSS-RO signal receiver with the parameters of the preset atmospheric model to obtain the error data. The overall performance of the GNSS-RO signal receiver can be evaluated based on the error information. The novel approach to evaluate the GNSS-RO signal receiver performance proposed in this paper is validated by using the FY-3E (FengYun-3E) receiver qualification parts that have been verified in orbit, and the results confirm that the approach can meet the requirements of the GNSS-RO receiver performance test. This study shows that the novel approach to evaluate the GNSS-RO signal receiver performance in terms of the ground-based atmospheric occultation simulation system can efficiently and accurately be used to carry out the receiver test and provides an effective solution for the ground-based test of GNSS-RO signal receivers. [ABSTRACT FROM AUTHOR]

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents' well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis. [ABSTRACT FROM AUTHOR]

Technological improvements are crucial for achieving decarbonisation targets and addressing the impacts of climate change in the built environment via mitigation and adaptation measures. Data-driven methods for building performance prediction are particularly important in this regard. Nevertheless, the deployment of these technologies faces challenges, particularly in the domains of artificial intelligence (AI) ethics, interpretability and explainability of machine learning (ML) algorithms. The challenges encountered in applications for the built environment are amplified, particularly when data-driven solutions need to be applied throughout all the stages of the building life cycle and to address problems from a socio-technical perspective, where human behaviour needs to be considered. This requires a consistent use of analytics to assess the performance of a building, ideally by employing a digital twin (DT) approach, which involves the creation of a digital counterpart of the building for continuous analysis and improvement. This paper presents an in-depth review of the critical connections between data-driven methods, AI ethics, interpretability and their implementation in the built environment, acknowledging the complex and interconnected nature of these topics. The review is organised into three distinct analytical levels: The first level explores key issues of the current research on the interpretability of machine learning methods. The second level considers the adoption of interpretable data-driven methods for building energy modelling and the problem of establishing a link with the third level, which examines physics-driven grey-box modelling techniques, in order to provide integrated modelling solutions. The review's findings highlight how the interpretability concept is relevant in multiple contexts pertaining to energy and the built environment and how some of the current knowledge gaps can be addressed by further research in the broad area of data-driven methods. [ABSTRACT FROM AUTHOR]

The current goal of the European Commission, which aims to reduce CO2 by 90% compared to values estimated in 1980, and the ever-increasing sensitivity to environmental sustainability, fully involve the construction sector, which, according to the OECD (Organization for Economic Co-Operation and Development) is responsible for over one-third of the world's energy requirement. In this frame, numerous researchers and companies are focusing on ecologically sustainable building materials, to be used in new and existing buildings, that are able to simultaneously fulfill the constructive function and improve the energy behavior of the building envelope. The goal of the present paper is the analysis of the energy performance of some innovative locally produced natural building materials (timber, sheep wool, rammed earth, lime-based plaster, natural fibers) used in multilayer vertical closures, compared to that of more common building materials (bricks, concrete, synthetic insulation). First, the physical-mechanical characterization of the local natural materials was carried out, then the model of a building was implemented, whose energetic performance was simulated by varying the type of stratigraphy of the walls, including the use of both innovative and common materials. The building chosen for the simulation consists of one of the BESTEST ANSI/ASHRAE reported in the 140-2017 standard using the climatic data of the Mediterranean area. The results of the simulation have been presented and discussed. [ABSTRACT FROM AUTHOR]

As buildings are one of the major contributors to greenhouse gas emissions and energy consumption, they have a key potential for energy efficiency and indoor environmental quality improvement. Therefore, the development of nearly Zero-Energy Buildings (nZEBs) is strategic to respond to these challenges and to design and retrofit sustainable highly performing buildings. Actually, the nZEB target can also be reached with highly insulated wooden technologies. However, they must be critically revised and adapted when taking into account the warm climate peculiarities. The paper contributes to this attempt by dealing with the implementation of a methodology specifically focused on the long-term assessment of the real building envelope performance. The methodology is applied to a recently built wooden nZEB detached single-story dwelling constructed in 2017 in central Italy. One year monitoring data were collected about the envelope in-field dynamic performance and the indoor microclimate and well-being conditions. The theoretical design-stage data and the monitored data were compared. The positive aspects as well as the critical issues of nZEB target in the Mediterranean climate context and the performance gap were underlined. Accordingly, the main aspects to be considered in the design of nZEBs envelope were highlighted. [ABSTRACT FROM AUTHOR]

According to the latest energy efficiency European directive (EED 2023/1791/EU), the expected energy renovation rate of at least 3% of the buildings' floor area each year towards nearly zero-energy buildings (nZEBs) is extended to include public buildings not only of the central government (as per the first EED 2012/27/EU) but also of regional and local authorities. This poses a great challenge, especially for Municipalities that often manage large building stocks with high energy demands. In response to this challenge, this paper presents the application of the so-called "typology approach" for conducting public buildings' energy renovation plans at the local level. A computational survey is initially introduced to decide the optimal set of building-stock clustering criteria among all possible combinations, involving the minimization of the RMSE index regarding the primary energy consumption of each building. For a representative building from each identified typology, the key performance indicators (KPIs) are computed for alternative energy-upgrading scenarios. Exploiting the IMPULSE Interreg-MED project tools, the KPIs from each representative building are at first extrapolated to all buildings of the examined stock and, finally, a gradual energy renovation plan is automatically produced based on user-defined decision parameters including the required annual renovation rate. The methodology is applied for the case of the Municipality of Hersonissos in Greece. For the specific 44-buildings' stock it was found that the optimal clustering set included four criteria, building use, construction year, heating, and a cooling system, leading to 15 building typologies. Finally, assuming a 7% renovation rate per year, a 12-year gradual renovation (nZEB transformation) plan is obtained foreseeing an 85% CO2 emissions' reduction. [ABSTRACT FROM AUTHOR]

The current practice of building thermal retrofitting is based on the outcomes of energy audits that make use of standardized tabulated information mapped on a structure or structural elements under inherently very specific conditions. Therefore, it provides very limited outcomes for further analysis, especially when decision making procedures are required to particularize the retrofitting strategy. This paper introduces a novel fuzzy logic approach for predicting the thermal performance of building walls that can be used in practice to partially substitute time-consuming and costly energy audits or complex computational analyses. The objective of this concept is to forecast the annual heating energy demands of buildings and to identify the potential energy savings that could be achieved by applying thermal retrofitting measures based on limited resources and information obtained from maps, blueprints, and/or a simple site inspection. For this purpose, a sample knowledge base was created using a validated computational model of the heat and moisture transport in the multilayered wall assemblies. Then, the fuzzy logic model was introduced to predict the thermal performance of selected walls. Our comparison of the fuzzy model outputs with simulated data proved the potential of using the proposed concept as an efficient and straightforward tool for predicting the seasonal thermal performance of building envelopes and to partially replace the current practice, which requires the utilization of building energy audits. [ABSTRACT FROM AUTHOR]

People tend to spend considerable amounts of time in buildings; thus the issue of providing proper indoor environmental quality is of significant importance. This paper experimentally analyses the subjective sensations of the occupants of intelligent and traditional buildings with the focus on possible differences between these two types of buildings. The study is based on a large database of 1302 questionnaires collected in 92 rooms where simultaneous measurements of the indoor environment physical parameters (air and globe temperature, relative humidity, carbon dioxide concentration, and illuminance) were carried out. Their impact on the subjective assessment of the indoor environment has been presented and analysed. The results show that the occupants seemed to be more favourable towards the indoor conditions in the intelligent building; however, the differences in comparison to the traditional buildings were not considerable. Similarly, self-reported productivity proved to be higher in the intelligent building, while the optimal range of air temperature, which ensured highest productivity, was 22–25 °C. Moreover, a strong correlation between the occupants' overall comfort and their perception of the air quality has been found. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the potential benefits and feasibility of integrating Building Energy Management Systems (BEMSs) with Building Information Modeling (BIM) in, but not limited to, the construction and building management sectors. By examining advantages, challenges, and real-world case studies, this study offers valuable insights into the impact of BEMS-BIM integration on building operations. The research methodology includes a literature review and bibliometric analysis to understand the subject domain and identify prevalent keywords. Additionally, case studies demonstrate the effectiveness of BEMS-BIM integration in real-world scenarios. This study investigates the possibilities and challenges of BIM to the BEMS methodology for energy-efficient industrial buildings, emphasizing the importance of addressing uncertainties and enhancing software interoperability. This research highlights the potential of BEMS-BIM integration to revolutionize building performance, enhance sustainability, and contribute to a greener and more efficient future for the construction and building management industries. [ABSTRACT FROM AUTHOR]

Reducing energy consumption while providing a high-quality environment for building occupants has become an important target worthy of consideration in the pre-design stage. A reasonable design can achieve both better performance and energy conservation. Parametric design tools show potential to integrate performance simulation and control elements into the early design stage. The large number of design scheme iterations, however, increases the computational load and simulation time, hampering the search for optimized solutions. This paper proposes an integration of parametric design and optimization methods with performance simulation, machine learning, and algorithmic generation. Architectural schemes were modeled parametrically, and numerous iterations were generated systematically and imported into neural networks. Generative Adversarial Networks (GANs) were used to predict environmental performance based on the simulation results. Then, multi-object optimization can be achieved through the fast evolution of the genetic algorithm binding with the database. The test case used in this paper demonstrates that this approach can solve the optimization problem with less time and computational cost, and it provides architects with a fast and easily implemented tool to optimize design strategies based on specific environmental objectives. [ABSTRACT FROM AUTHOR]

The cold-formed steel shear wall with steel sheathing has gained increasing popularity due to its excellent shear capacity. To extend the applicability of this system to multi-story residences, aside from experimental investigations on the shear walls, it is essential to conduct a comprehensive study on the seismic performance of buildings. In this paper, numerical simulations were conducted on specimens subjected to monotonic and cyclic loading. Subsequently, seismic analysis of mid-rise building models was also carried out to investigate the influence of the proposed shear wall on building seismic performance. The research findings indicate that this study's modeling method can effectively simulate the shear performance of the proposed shear wall under monotonic and cyclic loading. In addition, the proposed shear wall significantly enhances the structural stiffness and improves the seismic performance of the structure under seismic action. [ABSTRACT FROM AUTHOR]

High-performance glazing technologies are essential for achieving the occupant comfort and building energy efficiency required in contemporary and future buildings. In real-world applications, glazing façades are selected from a steadily increasing number of glazing technologies. However, the authors could not identify a systematic and comprehensive review and classification of glazing technologies in the literature. This creates a barrier when comparing typologically different glazing technologies and combining multiple technologies in a glazing unit. This paper provides a systematic review and classification of established and emerging glazing technologies based on publications from 2001–2022 which were interpreted following the PRISMA methodology. This study reveals that the majority of high-performance glazing systems used in practice are in multi-layer glazing configurations and that the glazing system performance can focus on including additional and multiple functionalities, which aim at improving overall building performance. It was also found that there is a large potential for improvement of multilayer, evacuated, aerogels, electrochromic, and solar cell glazing by incorporating other technologies or innovative materials in multi-layer glazing units for either improving existing technologies or for the development of new ones. However, their longevity, robustness, and cost affordability should be ensured. [ABSTRACT FROM AUTHOR]

Within the context of climate change and the environmental impact of the building industry, insulation materials contribute to improving the thermal performance of buildings, thus reducing energy demand and carbon emissions during the operation phase. Although most of them are responsible for significant carbon emissions during their production, bio-based insulation materials can provide good performance with low carbon emissions. This paper aims to investigate natural insulation materials' properties and environmental impacts through a literature review. Due to the growing importance of Environmental Product Declarations (EPDs) on specification requirements, many manufacturers already disclose environmental data related to their products, allowing for a comparison between thermal insulation solutions. In academic research, embodied environmental impacts are not as explored as physical properties. In addition, from the analysis of results, it is possible to conclude that the characterization of the physical properties of this type of material is normally focused on thermal conductivity. Nevertheless, most studies overlook other important parameters of these materials, such as the thermal capacity, lifetime, and environmental impacts. This is something that is necessary to overcome in future developments to allow for a comprehensive comparison between the properties of different (conventional and bio-based) insulation materials. [ABSTRACT FROM AUTHOR]

Digitalization offers new, unprecedented possibilities to increase the energy efficiency and improve the indoor conditions in buildings in a cost-efficient way. Smart buildings are seen by many stakeholders as the way forward. Smart buildings feature advanced monitoring and control systems that allow a better control of the buildings' indoor spaces, but it is becoming evident that the massive amount of data produced in smart buildings is rarely used. This work presents a long-term evaluation of a smart building testbed for one year; the building features state-of-the-art monitoring capability and local energy generation (PV). The analysis shows room for improving energy efficiency and indoor comfort due to non-optimal control settings; for instance, average indoor temperatures in all winter months were above 24 °C. The analysis of electricity and domestic hot water use has shown a relevant spread in average use, with single users consuming approximately four times more than the average users. The combination of CO2 and temperature sensor was sufficient to pinpoint the anomalous operation of windows in wintertime, which has an impact on energy use for space heating. Although the quantification of the impact of users on the overall energy performance of the building was beyond the scope of this paper, this study showcases that modern commercial monitoring systems for buildings have the potential to identify anomalies. The evidence collected in the paper suggests that this data could be used to promote energy-efficient behaviors among building occupants and shows that cost-effective actions could be carried out if data generated by the monitoring and control systems were used more extensively. [ABSTRACT FROM AUTHOR]

One of the important factors influencing the thermal performance of buildings is the leakage of the envelope. When it comes to Montenegro, although there is a formal airtightness requirement, air permeability tests are not mandatory and therefore there is a lack of data in this regard. This paper reports the results of fan pressurization tests on a single apartment in a multi-family residential building before and after replacing the windows. The replacement of old wooden windows with new UPVC ones, provided that the installation is carefully supervised, proved to be an effective air tightening measure, as it resulted in a reduction of air change rate at the reference building pressure from 6.25 h−1 to 0.77 h−1, or by nearly 90%. The energy impact of air leakage was evaluated using the national software for calculating the energy performance of buildings based on the DIN V 18599 methodology. Calculations showed that by reducing infiltration, significant energy savings for heating can be achieved, while savings for cooling are practically negligible. Savings in relative terms were greater when the façade walls were thermally insulated and when the building was located in a colder climate zone. Savings in delivered energy ranged from 13 to 25 kWh/m2·year, depending on the climate zone. [ABSTRACT FROM AUTHOR]

Climate change and natural disasters have brought threats to humanity greater than ever. The built environment is a critical part of the climate action plan in terms of its resilience and sustainability. The growing awareness of sustainability in the built environment has led to the increased uptake of sustainable buildings and infrastructure projects. However, a growing number of studies reported some forms of performance gaps in achieving sustainability, in which the actual outcomes and performance of buildings were far from the expected goals. Researchers started to question the alleged benefits of the sustainable built environment. Apart from being overoptimistic in setting the environmental goals, some studies suggested that the gaps could arise from a negligence of the interplay between humans and the environment for delivering the sustainability goals. Hence, this paper intends to highlight opportunities in optimizing sustainability performance in the built environment via human-centric approaches. By placing human needs at the focal point, human-centric solutions would empower stakeholders to harness sustainable goals in the built environment effectively. A conceptual framework has been proposed to consolidate the findings and highlight human-centric solutions in between research gaps and opportunities in sustainable built environment research. [ABSTRACT FROM AUTHOR]

In assessing the energy performance of buildings, the thermal performance of the structural components and building materials is crucial. Although reference catalogs are used to determine the thermal properties of construction materials, the use of novel materials or non-homogeneous mixtures, particularly with biomaterials, demands the development of new instruments that are capable of performing rapid, accurate and cost-effective thermal characterization. This study introduces the ambient hot-box, a new tool for measuring the thermal properties of construction components and heterogeneous materials. The paper provides a methodology for measuring a sample's benchmark and fresh materials using a streamlined hot-box-based instrument. Utilizing samples as a benchmark material, the new instrument is assessed, yielding transmittance values with errors below 4%. The electronic circuits, measurements techniques and instrument implementation are all described. [ABSTRACT FROM AUTHOR]

Architectural construction is responsible for the consumption of large amounts of resources, so the optimization of architectural design and evaluation is significant for sustainable global development. Most architectural assessments focus on energy conservation, novel materials and eco-friendly strategies, but without agreed indicators and criteria. Since the consideration of natural aspects is somewhat fuzzy and vague, this study utilized data mining technology to explore the major factors related to relationships between buildings and nature. By employing the popular technique of web crawling, this study collected 38,320 architectural descriptions from the "Archdaily", including descriptions of 11 types of buildings, four of which were taken as typical research representatives. The 100 most frequent words were used to create a word cloud. Using Python script, all of the text was refined and processed with the word2vec model, thereby allowing to conduct Agglomerative Hierarchical Clustering (AHC). The frequency of words related to natural aspects were analyzed within 15 architectural design elements. Different building types in different areas have obvious similarities in terms of design elements, so it is feasible to adopt the same evaluation factors for the building evaluation systems of different regions. This paper mainly focuses on improving the accuracy and validity of assessment by providing basic evaluation indicators that could enhance connections between design and evaluation progress, stimulating the improvement of building environmental performance. [ABSTRACT FROM AUTHOR]

Automatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. In this workflow, LiDAR points that belong to the building category are first segmented into multiple clusters by applying the grid-based DBSCAN clustering algorithm. Each cluster contains the points of an individual building. Then, the outermost points of each building are extracted, on which the recursive convex hull algorithm is applied to generate the initial outline of each building. Since LiDAR points are irregularly distributed, the initial building outline contains irregular zig-zag shapes. In order to achieve a regularized building footprint that is close to the true building boundary, a signal-based regularization algorithm is developed. The initial outline is first transformed into a signal, which can reveal the wholistic geometric structure of the building outline after applying a denoising procedure. By analyzing the denoised signal, the locations of corners are identified, and the regularized building footprint is generated. The performance of the proposed workflow is tested and evaluated using two datasets that have different point densities and building types. The qualitative assessment reveals that the proposed workflow has a satisfying performance in generating building footprints even for building with complex structures. The quantitative assessment compares the performance of signal-based regularization with existing regularization methods using the 149 buildings contained in the test dataset. The experimental result shows the proposed method has achieved superior results based on a number of commonly used accuracy metrics. [ABSTRACT FROM AUTHOR]

Building extraction using deep learning techniques has advantages but relies on a large number of clean labeled samples to train the model. Complex appearance and tilt shots often cause many offsets between building labels and true locations, and these noises have a considerable impact on building extraction. This paper proposes a new knowledge distillation-based building extraction method to reduce the impact of noise on the model and maintain the generalization of the model. The method can maximize the generalizable knowledge of large-scale noisy samples and the accurate supervision of small-scale clean samples. The proposed method comprises two similar teacher and student networks, where the teacher network is trained by large-scale noisy samples and the student network is trained by small-scale clean samples and guided by the knowledge of the teacher network. Experimental results show that the student network can not only alleviate the influence of noise labels but also obtain the capability of building extraction without incorrect labels in the teacher network and improve the performance of building extraction. [ABSTRACT FROM AUTHOR]

This paper aims to develop multi-objective optimized control methods to improve the performance of retrofitting building heating systems in reducing consumed energy as well as providing comfortable temperature in a multi-zone building. While researchers evaluate various controllers in specific systems, providing a comprehensive controller for retrofitting the existing heating systems of multi-zone buildings is less investigated. A case study approach with a four-story residential building is simulated. The building energy consumption is modeled by EnergyPlus. The model is validated with energy data. Then, the building steam system model is upgraded, and in the other case, renewed by a hydronic system instead of a steam one. Three optimized controller groups are developed, including Model Predictive Controller (MPC), fuzzy controllers (Fuzzy Logic Controller (FLC) and an Optimized Fuzzy Sliding Mode Controller (OFSMC)), and optimized traditional ones. These controllers were applied to the upgraded steam and hydronic heating systems. The control methods affected the tuning of the boiler feed flow by regulating the condensing cycle and circulating the pump flow of the hydronic system. Accordingly, renewing the heating system improves energy efficiency by up to 29% by implementing a hydronic system instead of the steam one. The fuzzy controllers increased renewing effectiveness by providing comfortable temperatures and reducing building environmental footprints by up to 95% and 12%, respectively, compared with an on/off controller baseline. [ABSTRACT FROM AUTHOR]

Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method. [ABSTRACT FROM AUTHOR]

In this study, we assessed a lifestyle in which occupants adjust the fittings based on climate, weather, and time, in terms of energy efficiency and thermal conditions. The proposed solution is a Zero Energy House (ZEH) with high thermal performance. The thermal performance of the building envelope can be adjusted by changing the operation of fittings based on the indoor and outdoor environments, as well as air conditioning usage. Many studies have achieved zero energy by increasing the thermal performance of an envelope and using highly efficient energy-saving facilities; however, uniquely, here we focus on occupant behavior to change the building envelope condition. In this paper, numerical analysis was used to investigate the effect of adjusting the fittings on buildings with different thermal performances of the envelope. The analysis demonstrates that, while more research into measures is needed in the summer, the adjustment of fittings and thermal storage properties in the winter season can reduce the heating load by 48–59% compared to the normal ZEH and improve the indoor environment. In terms of the heating and cooling load throughout the year, the results also showed that applying fittings adjustment and heat storage to an ordinary house can provide nearly the same energy-saving effect as a highly insulated house. [ABSTRACT FROM AUTHOR]

The carbon neutrality of the built environment plays a key role in fighting the climate crisis and fully realising ecological transition. In this field, the aim of the paper is to promote a new production paradigm that enables an integrated process from the design phase to the production of high-performance building components, to their "tailor-made installation" for the efficiency enhancement of the national residential building stock, reducing resource consumptions, costs, construction time and the associated environmental impact. The methodological approach is based on three key elements: digitalisation, production and performance. Recent technologies make it possible to achieve important goals, through the creation of the digital cataloguing of existing buildings, advanced dynamic simulations, improved energy performance for building stock and industrial production chain optimisation of the construction sector according to off-site construction criteria. This strategy was tested on a demonstrator building. The methodology application has made it possible to validate a catalogue of standardised insulation solutions that can be applied to 90% of the existing national building stock and that can reduce installation times by 60% and costs by around 30% compared to traditional techniques, while complying with the national requirements on thermal building insulation. The study is able to organise production according to advanced standardisation in order to meet the real demand of the market with advantages in term of production costs and economies of scale. [ABSTRACT FROM AUTHOR]

In 2010, the Energy Performance of Buildings Directive announced that all new buildings are to be nearly zero-energy as of January 2021. Having reached year 2022, it can be said that the transition has proven to be slower than anticipated. Transition research has long acknowledged the potential impact of the human factor in the process of change. While there is a relative wealth of literature on end-users and their perceptions as recipients of change within the demand end of the market, research on professionals and their perceptions as actors in the process of change is limited. Thus, this study looks at the human factor in the supply end of the market by bringing professionals' perceptions to the forefront in its investigation of barriers to the implementation and uptake of nearly zero-energy housing in practice. As part of the project entitled Housing 4.0 Energy: Affordable and Sustainable Housing through Digitization, data were collected through a focus group and semi-structured interviews with housing professionals in Kilkenny, Ireland. Descriptive coding, inferential coding, and fact tracing revealed several identified barriers to be perceptions and not actual barriers to nearly zero-energy housing. Additionally, information dissemination and assimilation between policy and industry was identified as an overarching barrier. Therefore, the paper ends with recommendations to reduce delay factors at the supply end of the market, thus contributing to closing the gap between the development of policies and their implementation. [ABSTRACT FROM AUTHOR]

A variety of mathematical models are available to estimate the thermal performance of buildings. Nevertheless, mathematical models predict the thermal performance of buildings that might differ from the actual performance. The hot box is a widely-used test apparatus to assess the actual thermal performance of various building envelope components (walls, roofs, windows) in the laboratory. This paper presents the process of designing, constructing, and calibrating a unique small-scale hot box apparatus. Despite its smaller metering area (1.0 m × 1.0 m), this apparatus met the key requirements (below ±0.25 °C fluctuations in chambers' air temperature, and below 2.0% variation from the point-to-point temperature in reference to the temperature difference across the specimen) as prescribed in the ASTM C1363 and ISO 8990 standards. The walls of this apparatus are uniquely constructed using vacuum insulation panels or VIPs. The efficient and novel use of VIPs and workmanship during the construction of the apparatus are demonstrated through the temperature stability within the chambers. The achieved range of temperature steadiness below ±0.05 °C and point-to-point temperature variation below 1.0% of the temperature difference across the specimen allow for this apparatus to be considered unique among the calibrated hot box categories reported in the literature. In addition, having an affordable, simple-to-operate, and high-accuracy facility offers a great opportunity for researchers and practitioners to investigate new ideas and solutions. The apparatus was calibrated using two extruded polystyrene foam (XPS) specimens with thicknesses of 2″ and 4″. The calibration exercise indicates small differences between results obtained numerically, theoretically, and experimentally (below 3.0%). Ultimately, the apparatus was employed to measure the thermal properties of a specimen representing a lightweight steel framing (LSF) wall system, which is commonly used in cold climates. The results obtained experimentally were then compared to the ones estimated numerically using a 3D finite element modelling tool. The difference between the results obtained by both methods was below 9.0%. [ABSTRACT FROM AUTHOR]

This paper focuses on the modelling and analysis of electromechanical coupling in a magnetic levitation energy harvester. A prototype harvester is built and its performance is tested with a shaker under resonance conditions. In order to modify the electromechanical coupling, a specially designed coil stack consisting of four independent coils is proposed. The configuration of the coil and the gap between them change the shape of the electromechanical coupling function. The results obtained show that the proper configuration of the modular coil allows one to modify the shape of the electromechanical coupling, increasing the recovered energy, and widens the resonance operating bandwidth. [ABSTRACT FROM AUTHOR]

Encouraged by the European Union, all European countries need to enforce solutions to reduce non-renewable energy consumption in buildings. The reduction of energy (heating, domestic hot water, and appliances consumption) aims for the vision of near-zero energy consumption as a requirement goal for constructing buildings. In this paper, we review the available standards, tools and frameworks on the energy performance of buildings. Additionally, this work investigates if energy performance ratings can be obtained with energy consumption data from IoT devices and if the floor size and energy consumption values are enough to determine a dwellings' energy performance rating. The essential outcome of this work is a data-driven prediction tool for energy performance labels that can run automatically. The tool is based on the cutting edge kNN classification algorithm and trained on open datasets with actual building data such as those coming from the IoT paradigm. Additionally, it assesses the results of the prediction by analysing its accuracy values. Furthermore, an approach to semantic annotations for energy performance certification data with currently available ontologies is presented. Use cases for an extension of this work are also discussed in the end. [ABSTRACT FROM AUTHOR]

Energy communities, also known as renewable or citizen energy communities, can be a source of innovative aggregation solutions at the local level. The unleashed flexibility potential of households could provide self-balancing services for local energy communities or create new revenue streams for local flexibility aggregators. This paper proposes a methodology for the assessment of the energy savings potential of residential customers, factoring in local climatological conditions, energy consumption patterns, and building energy performance when the available input data are scarce. For baseline consumption modelling, the correlation between historical energy consumption data collected from a survey, building energy performance parameters, and the availability of flexibility assets was determined, taking into account the inconsistency between the quantity and quality of collected data from various consumers. For this purpose, a modelling approach using calculations for "Agent" buildings was used. In this way, each building user was assigned to a specific "Agent" with dedicated consumption characteristics for a flexibility asset. The capacities engaged in a flexibility programme were modelled according to the available flexibility assets, whilst the duration of a flexibility demand response (DR) event was considered a function of building energy performance characteristics, and consequently, activation strategies were applied. Additionally, several energy savings activation scenarios were modelled to interlink technical and behavioural constraints of household consumers. These constraints restrict the available flexibility, thus influencing the possibility of daily repetitions of a DR event and increasing savings with flexibility event activation. This model is intended to optimise flexibility assets provided by the end-users and, in this manner, deliver permanent energy savings, offering new business opportunities for aggregators or local energy communities. The novelty of this research is the recognition of an aggregator as a permanent energy savings provider, even if the obtained savings are very conservative per individual flexibility asset. Nevertheless, if properly aggregated and identified, the obtained savings could create novel business opportunities for a local aggregator. [ABSTRACT FROM AUTHOR]

Building contour extraction from high-resolution remote sensing images is a basic task for the reasonable planning of regional construction. Recently, building segmentation methods based on the U-Net network have become popular as they largely improve the segmentation accuracy by applying 'skip connection' to combine high-level and low-level feature information more effectively. Meanwhile, researchers have demonstrated that introducing an attention mechanism into U-Net can enhance local feature expression and improve the performance of building extraction in remote sensing images. In this paper, we intend to explore the effectiveness of the primeval attention gate module and propose the novel Attention Gate Module (AG) based on adjusting the position of 'Resampler' in an attention gate to Sigmoid function for a building extraction task, and a novel Attention Gates U network (AGs-Unet) is further proposed based on AG, which can automatically learn different forms of building structures in high-resolution remote sensing images and realize efficient extraction of building contour. AGs-Unet integrates attention gates with a single U-Net network, in which a series of attention gate modules are added into the 'skip connection' for suppressing the irrelevant and noisy feature responses in the input image to highlight the dominant features of the buildings in the image. AGs-Unet improves the feature selection of the attention map to enhance the ability of feature learning, as well as paying attention to the feature information of small-scale buildings. We conducted the experiments on the WHU building dataset and the INRIA Aerial Image Labeling dataset, in which the proposed AGs-Unet model is compared with several classic models (such as FCN8s, SegNet, U-Net, and DANet) and two state-of-the-art models (such as PISANet, and ARC-Net). The extraction accuracy of each model is evaluated by using three evaluation indexes, namely, overall accuracy, precision, and intersection over union. Experimental results show that the proposed AGs-Unet model can improve the quality of building extraction from high-resolution remote sensing images effectively in terms of prediction performance and result accuracy. [ABSTRACT FROM AUTHOR]

Buildings are the focus of European (EU) policies aimed at a sustainable and competitive low-carbon economy by 2020. Reducing energy consumption of existing buildings and achieving nearly zero energy buildings (NZEBs) are the core of the Energy Efficiency Directive (EED) and the recast of the Energy Performance of Building Directive (EPBD). To comply with these requirements, Member States have to adopt actions to exploit energy savings from the building sector. This paper describes the differences between deep, major and NZEB renovation and then it provides an overview of best practice policies and measures to target retrofit and investment related to non-residential buildings. Energy requirements defined by Member States for NZEB levels are reported comparing both new and existing residential and non-residential buildings. The paper shows how the attention given to refurbishment of NZEBs increased over the last decade, but the achievement of a comprehensive implementation of retrofit remains one of main challenges that Europe is facing. [ABSTRACT FROM AUTHOR]

A focus on indoor comfort and tightening targets for energy savings in buildings presents new opportunities for heating, ventilation, and air-conditioning products (HVAC). This paper presents a novel comfort solution that integrates a suspended radiant ceiling with diffuse ventilation, dubbed HVACeiling. In combination with the concrete slab, the HVACeiling has the potential to provide thermal comfort with minimal temperature offset, which supports operation of the heating and cooling system at temperatures very close to the room comfort temperature. The paper presents a parametric numerical study of the concept in a simplified two-pipe layout with fixed flow and fixed temperatures. First, the analysis was focused on different internal and solar loads, heat losses, and climatic locations with the aim of assessing the potential of self-regulation, i.e., no active controls, thermal comfort, ability to reduce peak loads and the consequential building design considerations. Secondly, the purpose was to analyse the concept in a generic office building with five offices and one meeting room and compare it to other HVAC solutions. The whole-year analyses of heating, cooling, energy performance, and thermal comfort were done using the building performance simulation software IDA ICE. It was found that it was possible to create thermal comfort in Paris, Munich, and Copenhagen with water circulating constantly with fixed temperatures of 20–24 °C without controls and with window sizes from 15 to 30% of the floor area. The studies showed that the HVACeiling reduced the operative peak temperatures on the warmest days in comparison with a standard radiant ceiling with mixing ventilation by 1 K. Compared to all-air solutions, the HVACeiling reduced the yearly energy consumption by 20–30% and the peak power in summer up to 69%. This study indicates that thermal comfort is achievable in a European context even at very small temperature offsets, which supports the use of more renewable energy sources. [ABSTRACT FROM AUTHOR]

This paper presents the methodology for conducting a cost-optimal energy performance calculation of a solar hot water system, used for space heating and domestic hot water needs. The calculation is based on dynamic hourly methods, according to the new Energy Performance of Buildings' (EPB) set of standards EN 15316:2017, and a revision of the standard EN 15316-5:2017 from the year 2021, dealing with storage-tank water temperature calculations. The paper provides proposals for modifications to these newly introduced standards, in order to overcome the observed ambiguities and shortcomings. The calculation of annual energy performance of a building was performed on an hourly basis over a year for the reference of an nZEB multi-apartment building, for a climate area of the city of Zagreb, taking into account water temperature change in the layers of the storage tank connected to solar collectors and hot water boilers. The cost-optimal solution was then determined by varying individual parameters of the building technical system. The influence of these parameters on the energy efficiency of the building was analyzed in detail. Furthermore, the results were compared against those obtained by the Croatian calculation algorithm based on the previous set of EPB standards, EN 15316:2008, currently used EU-wide for the energy performance certification of buildings. The results indicated that the calculation methods of the present algorithm underestimated the consumption of building primary energy by 12%. The energy delivered by solar collectors was underestimated by 18%. [ABSTRACT FROM AUTHOR]

Typification is a well-established operator of map generalization. Although it is widely used in many existing research fields, less discussion has been devoted to the quality of typification. This paper presents a user survey for the evaluation of different typification results of façade structures under different constraints. The survey shows that preservation of the shape of the features is the most important constraint for a reasonable typification process, which has also been quantitatively verified by calculating the similarities between the typified façades and the original façade using attributed relational graph (ARG) and nested earth mover's distance (NEMD) algorithms. Based on that, an algorithm is developed to generate perceivably reasonable representation from the original facade with decreasing map scale. The algorithm is implemented and tested on a number of façades. Experiments reveal that the typification can be automatically conducted and can create results which are well associated with the original façades. [ABSTRACT FROM AUTHOR]

The transition to a sustainable society and a carbon-neutral economy by 2050 requires extensive deployment of renewable energy sources that, due to the aleatority and non-programmability of most of them, may seriously affect the stability of existing power grids. In this context, buildings are increasingly being seen as a potential source of energy flexibility for the power grid. In literature, key performance indicators, allowing different aspects of the load management, are used to investigate buildings' energy flexibility. The paper reviews existing indicators developed in the context of theoretical, experimental and numerical studies on flexible buildings, outlining the current status and the potential future perspective. Moreover, the paper briefly reviews the range of grid services that flexible buildings can provide to support the reliability of the electric power system which is potentially challenged by the increasing interconnection of distributed variable renewable generation. [ABSTRACT FROM AUTHOR]

The building sector continues to register a significant rise in energy demand and environmental impact, notably in developing countries. A considerable proportion of this energy is required during the operational phase of buildings for interior heating and cooling, leading to a necessity of building performance improvement. A holistic approach in building design and construction represents a step to moderate construction costs in conjunction with reduced long-term operating costs and a low impact on the environment. The present paper presents an experimental evaluation of the energy efficiency of a building under real climate conditions; the building, which represents a holistically designed modular laboratory, is located in a moderate continental temperate climate, characteristic of the south-eastern part of the Pannonian Depression, with some sub-Mediterranean influences. Considerations for the holistic design of the building, including multi-object optimization and integrated design with a high regard for technology and operational life are described. The paper provides a genuine overview of the energy efficiency response of the building during six months of operational use through a monitored energy management system. The energetic analysis presented in the paper represents an intermediary stage as not all the energetic users were installed nor all the energetic suppliers. However, the results showed a reliable thermal response in the behaviour of recycled-PET thermal wadding used as insulation material in the building and for the intermediary stage in which the building has only secondary energy users, the energetic balance proves its efficiency, keeping the buffer stock of energy high values over 90%. [ABSTRACT FROM AUTHOR]

Buildings, energy, and the environment are key issues facing construction around the world. The energy efficiency of buildings is a key topic when it comes to reducing the world's energy consumption, releasing harmful gases, and global climate change, as they consume about 40% of the world's energy supplies. Heat losses in buildings reduce the energy performance of buildings and are basically important to them. In the paper, the authors focus on the main problems related to heat losses generated by chimney systems, which are inseparable equipment of building structures, resulting in lower energy efficiency and, at the same time, technical efficiency and durability of the building partitions themselves. Authors present thermal imaging with its contribution to the detection of heat losses, thermal bridges, insulation problems, and other performance disturbances, and then verifications using appropriate simulation models. The mathematical apparatus of artificial neural networks was implemented to predict the temperature distributions on the surfaces of prefabricated chimney solutions. In Europe, we can often find a large building substance equipped with traditional chimneys, which disrupts the current trend of striving to reduce energy consumption, especially that derived from fossil fuels. Speaking of energy-efficient buildings, one should not ignore those that, without additional security and modern installations, are constantly used in a very wide range. Therefore, the article deals with an essential problem that is not perceived in design studies and during the operation period as having a basis in incorrect architectural solutions and which can be easily eliminated. It concerns the cooling of internal partitions of buildings on their last storeys, in places where chimneys are located, regardless of their function. The authors of the paper decided to take a closer look at this phenomenon, which may allow the limiting of its effects and at the same time reduce its impact on the energy performance of technologically older buildings. [ABSTRACT FROM AUTHOR]

Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade the prediction accuracy, which may affect the efficiency and performance of the building energy management systems. In this paper, we investigate the impact of bi-level poisoning attacks on regression models of energy usage obtained from household appliances. Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper. Attacks and defenses are evaluated on a benchmark dataset. Experimental results show that an intelligent cyber-attacker can poison the prediction model to manipulate the decision. However, our proposed solution successfully ensures defense against such poisoning attacks effectively compared to other benchmark techniques. [ABSTRACT FROM AUTHOR]

The stress-strain behavior of concrete can be improved by providing a lateral passive confining pressure, such as fiber-reinforced polymer (FRP) wrapping. Many axial stress-strain models have been proposed for FRP-confined concrete columns. However, few models can predict the stress-strain behavior of confined concrete columns with more than two specified cross-sections. A stress-strain model of FRP-confined concrete columns with cross-sectional unification was developed in this paper based on a database from the existing literature that includes circular, square, rectangular and elliptical concrete columns that are highly confined by FRP jackets. Using the database, the existing theoretical models were evaluated. In addition, the ultimate stress and strain models with cross-sectional unification were proposed using two parameters: the cross-sectional aspect ratio and corner radius ratio. The elliptical cross-section can be considered as a rectangular one with a special corner radius for the model calculations. A simple and accurate model of the equivalent corner radius ratio for elliptical columns was proposed. Compared to the other existing models and experimental data, the proposed models show good performance. [ABSTRACT FROM AUTHOR]

Buildings do not usually perform during operation as well as predicted during the design stage. Disagreement between simulated and metered energy consumption represents a common issue in building simulation. For this reason, the calibration of building simulation models is of growing interest. Sensitivity and uncertainty analyses play an important role in building model accuracy. They can be used to identify the building model parameters most influent on the energy consumption. Given this, these analyses should be integrated within calibration methodologies and applications for tuning the parameters. This paper aims at providing a picture of the state of the art of calibration methodologies in the domain of building energy performance assessment. First, the most common methodologies for calibration are presented, emphasizing criticalities and gaps that can be faced. In particular the main issues to be addressed, when carrying out calibrated simulation, are discussed. The standard statistical criteria for considering the building models calibrated and for evaluating their goodness-of-fit are also presented. Second, the commonly used techniques for investigating uncertainties in building models are reviewed. Third, a review of the latest main studies in the calibrated simulation domain is presented. Criticalities and recommendations for new studies are finally provided. [ABSTRACT FROM AUTHOR]

Occupant behavior and business processes in a building environment constitute an inseparable set of important factors that drives energy consumption. Existing methodologies for building energy management lag behind in addressing these core parameters by focusing explicitly on the building's structural components. Additional layers of information regarding indoor and outdoor environmental conditions and occupant behavior patterns, mostly driven by everyday business processes (schedules, loads, and specific business activities related to occupancy patterns and building operations), are necessary for the effective and efficient modeling of building energy performance in order to establish a holistic energy efficiency management framework. The aim of this paper was to develop a context-driven framework in which multiple levels of information regarding occupant behavior patterns resulting from everyday business processes were incorporated for efficient energy management in buildings. A preliminary framework evaluation was performed in a multifaceted university building involving a number of spaces, employees, business processes, and data from sensors and metering devices. The results derived by linking operational aspects and environmental conditions (temperature, humidity, and luminance) to occupant behavior underlying business processes and organizational structures indicated the potential energy savings: a max of 7.08% for Heating, ventilation, and air conditioning (HVAC), 19.46% for lighting and a maximum of 6.34% saving related to office appliances. [ABSTRACT FROM AUTHOR]

Despite the multiple advantages of prefabricated compared to conventional buildings, such as significant reductions in cost and time, improved quality and accuracy in manufacture, easy dismantling and reuse of components, reduction in environmental degradation, increase of productivity gains, etc., they still share a small part of the European building stock, mainly in the Mediterranean. This paper attempts to highlight the potential of prefabricated buildings to achieve advanced levels of performance, particularly as regards their thermal and energy behavior. More specifically, in this paper the energy needs of a single-family building constructed with prefabricated elements is analyzed, considering different climate contexts. The prefabricated elements comprising the building envelope were developed in order to address specific requirements with respect to their structural, hygrothermal, energy, fire, acoustical, and environmental performance, within the research project SUPRIM (sustainable preconstructed innovative module). The new multifunctional building element, also incorporating phase change materials for increased latent thermal heat storage, has been proven to be beneficial in all the examined climate zones. The results of the relevant studies will highlight the contribution of the new prefabricated element to the sustainability of the overall construction, as well as its advantages when compared with conventional constructions. [ABSTRACT FROM AUTHOR]

Building extraction from high-resolution remote sensing images plays a vital part in urban planning, safety supervision, geographic databases updates, and some other applications. Several researches are devoted to using convolutional neural network (CNN) to extract buildings from high-resolution satellite/aerial images. There are two major methods, one is the CNN-based semantic segmentation methods, which can not distinguish different objects of the same category and may lead to edge connection. The other one is CNN-based instance segmentation methods, which rely heavily on pre-defined anchors, and result in the highly sensitive, high computation/storage cost and imbalance between positive and negative samples. Therefore, in this paper, we propose an improved anchor-free instance segmentation method based on CenterMask with spatial and channel attention-guided mechanisms and improved effective backbone network for accurate extraction of buildings in high-resolution remote sensing images. Then we analyze the influence of different parameters and network structure on the performance of the model, and compare the performance for building extraction of Mask R-CNN, Mask Scoring R-CNN, CenterMask, and the improved CenterMask in this paper. Experimental results show that our improved CenterMask method can successfully well-balanced performance in terms of speed and accuracy, which achieves state-of-the-art performance at real-time speed. [ABSTRACT FROM AUTHOR]

A large amount of the Iranian energy demand is related to the building sector, mainly due to its obsolescence. In this paper, a second-skin system has been implemented as a retrofit action for an office building, evaluating the effect of a tensile material as second-skin in terms of primary energy saving, carbon dioxide equivalent emissions, and simple payback period. The analysis was carried out through numerical simulations across a whole year and for four Iranian cities (Tabriz, Teheran, Yazd, and Bandar Abbas) in four different climates (cold, temperate, hot-dry, and hot-wet), and with the building aligned at either north-south or east-west. Moreover, an economic analysis was carried out suggesting different incentive policies to promote building energy refurbishment. The simulation results highlighted a favorable orientation for buildings in Iran, suggesting a guideline for new constructions. Indeed, the best results were achieved for an east-west orientation of the building (up to a primary energy saving of 13.6% and reduction of carbon dioxide equivalent emissions of 45.5 MgCO2,eq, in Yazd), with a decrease of the annual specific total (cooling and thermal) energy demand of 37.9 kWh/m2/year. The simple payback period values were also lower in the east-west orientation than the north-south one. [ABSTRACT FROM AUTHOR]

This paper deals with the impact of the use of daylight on the overall energy demand for heating, cooling, and lighting in educational buildings. The energy performance of buildings is currently of the utmost importance as current European regulations, starting from 31st December 2020 impose that all new buildings must meet nearly zero-energy building requirements. This paper presents a study of the illuminance distribution in an educational room obtained from measurements and simulation results using two different models. One of the models, integrated with a thermal simulation software, was used to estimate the impact of daylight on the energy demand. The analysis included the use of various window types, lighting control system, reference point location, and daylight calculation model for a sample room in an educational building. Results of the analysis indicate that, due to the high share of lighting demand (reaching up to 78% of the primary energy balance), there is a need to take into account the efficiency of lighting systems during the design process to correctly determine the actual energy balance of a building, increase the quality of the design of lighting systems, as well as to select the optimal parameters of windows. [ABSTRACT FROM AUTHOR]

The green wall is an engineered technology for stormwater management and climate change mitigation at the urban level. At the building scale, these energy efficiency measures are suitable for improving indoor comfort conditions and for reducing energy needs. Several guidelines are available about vertical greening systems, but these propose design parameters and performance evaluation criteria, often incomparable. In order to facilitate the implementation of proper technical standards, this paper proposes a critical review of more recent scientific investigations. All parameters for the design optimization are discussed as well as the achievable social and private benefits by taking into consideration the type of study (numerical or experimental), the climate conditions, the analysis period, all technical requirements of the green layer as well as of the back wall. The review underlines that a multi-criteria design approach is needed for green vertical systems. Thus, the paper is concluded with a SWOT analysis, evidencing "strengths", "weaknesses", "opportunities" and "threats". The analysis shows that the highlighted benefits will acquire greater relevance considering the increase in global temperatures and the growing need to redevelop densely built urban centers, while some negative aspects may be filled in the future with a deeper preparation of designers and careful choice of materials. The review paper shows, therefore, drivers and barriers occurring designing and implementing green walls. [ABSTRACT FROM AUTHOR]

Selecting the best materials that ensure maximum performance is crucial in the construction engineering design of any construction project. However, this is challenging and usually not properly considered because of the lack of systematic and scientific evaluation methods for the performance of materials. This paper proposes a new approach of selecting material to satisfy the performance goal of material designers in building constructions based on the analytic hierarchy process method. To validate the suggested model, a case study was conducted for a concrete system form, the performance of which is susceptible to its materials and has a strong effect on overall project productivity. The newly developed form comprising polymers and alloys showed that the proposed material selection model provided a better combination of materials, and the solution was technically more advanced and ensured better performance. This paper contributes to the body of knowledge by expanding the understanding of how construction material properties affect project performance and provides a guideline for material engineers to select the best-performing building materials while considering a performance goal. [ABSTRACT FROM AUTHOR]

Considering the huge power consumption, rapid response and the short-term heat reserving capacity of the air conditioning load in the building's energy system, the air conditioning load and its system can be equivalent to the virtual energy storage device for the power grid. Therefore, to obtain a high matching building renewable energy system, a virtual energy storage system of the air conditioning load, accompanied by a storage battery, was built in the paper. Then, operating strategies for the virtual energy storage of the air conditioning load and storage battery were designed. Further, to quantize the contribution of the virtual energy storage to the improvement of matching characteristics, two indicators of the demand side and supply side in the energy system were adopted, including on-site energy fraction (OEFr) and on-site energy matching (OEMr). Lastly, matching characteristic research of the building's renewable energy system based on virtual energy storage of the air conditioning load was established and analyzed by TRNSYS and MATLAB in Tianjin, China. The results revealed that a better matching characteristic performance of the building's renewable energy systems driven by virtual energy storage was obtained. In the condition set out in the paper, compared with that without virtual energy storage, the average values of OEFr and OEMr after virtual energy storage were 0.66 and 0.77, which increased by 8.19% and 8.45% respectively. Simultaneously, the battery operation performance in the building renewable system was improved when the virtual energy storage was working. The times of charge and discharge cycles decreased after virtual energy storage, and the depth of discharge of the battery reduced by 23.37% on a specific day. [ABSTRACT FROM AUTHOR]

Released green building evaluation standards for operation stage include a huge number of indicators, which are very comprehensive and systematic. However, the indicators of these standards are very complicated and a large amount of time and manpower are consumed for their evaluation. To evaluate the operational performance of green buildings more practically and efficiently, some studies collect the operational data for part of the indicators (mainly focusing on building energy performance, indoor environmental quality or occupant satisfaction), which are too rough to evaluate the performance of green building. This paper proposed a total of 27 key performance indicators (KPIs) for green building operations monitoring. The number of proposed indicators is much fewer than the evaluation standards, as well as suitable for long-term monitoring, which can dramatically reduce evaluation time and cost. On the other hand, the indicators involving Outdoor environmental quality, Indoor environmental quality, HVAC system, P&D system, Renewable energy system, Total resource consumption and User behavior, which are more comprehensive and systematic than the conventional monitoring studies for operational performance of green building. Firstly, an indicators library for operations monitoring of green building was established based on relevant standards and literature review in this field. Secondly, "SMART" principle and Delphi method were adopted to select the key performance indicators for green building operations monitoring. Different background experts regarding green building industry were chosen to screen the most relevant, accessible and measurable indicators. Subsequently, two projects in China were selected for case study of key performance indicators proposed in this paper for green building operations monitoring to validate the feasibility and advancement. [ABSTRACT FROM AUTHOR]

Performing numerous simulations of a building component, for example to assess its hygrothermal performance with consideration of multiple uncertain input parameters, can easily become computationally inhibitive. To solve this issue, the hygrothermal model can be replaced by a metamodel, a much simpler mathematical model which mimics the original model with a strongly reduced calculation time. In this paper, convolutional neural networks predicting the hygrothermal time series (e.g., temperature, relative humidity, moisture content) are used to that aim. A strategy is presented to optimise the networks' hyper-parameters, using the Grey-Wolf Optimiser algorithm. Based on this optimisation, some hyper-parameters were found to have a significant impact on the prediction performance, whereas others were less important. In this paper, this approach is applied to the hygrothermal response of a massive masonry wall, for which the prediction performance and the training time were evaluated. The outcomes show that, with well-tuned hyper-parameter settings, convolutional neural networks are able to capture the complex patterns of the hygrothermal response accurately and are thus well-suited to replace time-consuming standard hygrothermal models. [ABSTRACT FROM AUTHOR]