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With the continuous increase in building energy consumption, the problem of low energy utilization efficiency, especially concerning windows accounting for approximately 40% of total energy loss, has become increasingly prominent and urgent. Electrochromic (EC) technology emerges as a promising solution capable of effectively regulating the transmittance and absorption of light. Notably, dual‐band EC smart windows (DESWs) offer a unique avenue to address this challenge by independently adjusting sunlight and heat entry, anticipated to significantly reduce energy consumption for internal lighting and temperature regulation in buildings. While the potential benefits of DESWs are substantial, research is still in its early stages, facing important obstacles that hinder progress. This review aims to enhance our comprehension of this technology by providing a detailed introduction to the fundamental features of DESWs, encompassing configuration, operating mechanisms, and parameters for performance evaluation. In particular, we explore potential methods and approaches to achieve independent modulation using single-component materials. Additionally, we analyze performance issues and challenges, pinpoint future development directions, and propose corresponding solutions. The purpose of this review is to expedite the research and development of DESWs, offering robust support for the creation of modern energy-efficient buildings and facilitating the transition of the construction industry towards more sustainable and efficient energy utilization. [Display omitted] • Overview of the emerging technology of dual-band electrochromic smart windows (DESWs) designed for the independent regulation of visible and near-infrared light. • Comprehensive exploration of recent advancements in typical single-component materials for DESWs, with a focus on their unique characteristics and potential applications in green building design. • Evaluation of the challenges encountered in DESWs research and proposed solutions, providing insights into future directions for advancing this technology. [ABSTRACT FROM AUTHOR]

Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this data. Earth observation data can be beneficial in this regard, because of their availability and requiring a reduced amount of fieldwork. In this work, we imported Google Street View (GSV), light detection and ranging-derived (LiDAR-derived) features, and orthophoto images to deep learning (DL) models. The DL models were trained on building land-use type data for the Greater Toronto Area (GTA). The data was created using building land-use type labels from OpenStreetMap (OSM) and web scraping. Then, we classified buildings into apartment, house, industrial, institutional, mixed residential/commercial, office building, retail, and other. Three DL-derived classification maps from GSV, LiDAR, and orthophoto images were combined at the decision level using the proposed ranking classes based on the F1 score method. For comparison, the classifiers were combined using fuzzy fusion as well. The results of two independent case studies, Vancouver and Fort Worth, showed that the proposed fusion method could achieve an overall accuracy of 75%, up to 8% higher than the previous study using CNNs and the same ground truth data. Also, the results showed that while mixed residential/commercial buildings were correctly detected using GSV images, the DL models confused many houses in the GTA with mixed residential/commercial because of their similar appearance in GSV images. [ABSTRACT FROM AUTHOR]

The construction industry consists of various sectors that have different needs and functions. Those sector-specific differences create different risks that construction professionals empirically know. However, there is no documented and unbiased assessment to validate the empirical knowledge. Identifying the varying spectrum of risks present in different construction sectors in a systematic manner can facilitate more effective and efficient sharing, learning, and adoption of proven construction project management methods across these sectors. Ultimately, this can lead to significant improvements in construction productivity as a whole. This research analyzed risks in different construction sectors, including the commercial building, industrial, and civil infrastructure sectors in a comparative and quantitative manner. Project risk registers from the three construction sectors were analyzed using fuzzy set qualitative comparative analysis and coincidence analysis. The study identified various risk configurations in each sector, as well as their similarities and dissimilarities. Based on the findings, an ideal path to the utilization and dissemination of advanced construction management strategies and methods across the construction sectors was developed. These results are expected to assist project participants and stakeholders in adopting and implementing advanced project planning, delivery, and management strategies derived from other construction sectors while taking into account different risks. The construction industry possesses a wealth of effective and proven construction project management knowledge and know-how that holds the potential to enhance productivity. Despite these resources, the industry still faces productivity challenges. One contributing factor is the industry's isolated nature, which makes it difficult to share and disseminate knowledge and know-how across construction sectors and the industry as a whole. Identifying similarities and differences across the construction sectors is one prerequisite for promoting seamless knowledge sharing and dissemination. These factors influence the level of modifications and adjustments for successful implementation in different construction sectors. In this background, this study identified similarities and dissimilarities in project risk spectrums across construction sectors in a comparative manner. Ideal pathways for sharing and disseminating knowledge and know-how were proposed based on the observed risk spectrums. The findings aim to streamline the sharing and dissemination of construction management expertise by offering direction for evaluating and selecting appropriate project planning, delivery, and management strategies. Such insights, which take into account different project risk spectrums in different construction sectors, will contribute significantly to improving productivity throughout the construction industry. [ABSTRACT FROM AUTHOR]

This paper explores the efficiency of exporting data from Building Information Modeling (BIM) to Building Energy Modeling (BEM) and proposes solutions to encountered problems. The study considers both detached and attached buildings, analyzing the impacts of resolution parameters, and evaluating energy performance based on BIM model. A case-study approach is employed using a residential apartment building, with data exported from Autodesk Revit and imported into DesignBuilder. The results demonstrate the significance of adjusting resolution parameters (the primary parameters that influence the energy analytical model's accuracy and processing time) for efficient data export. Exploring different values leads to improvements in resolving unclosed spaces and enhancing the geometrical model of building elements. Challenges persist in allocating material finishes to walls. By improving and integrating energy performance evaluation through enhanced accuracy and efficiency of data exchange, the findings have significant impacts on stakeholders involved in building design, construction, and operation. Additionally, this process can optimize energy consumption in buildings. Further research is needed to address remaining challenges and improve BIM to BEM systems compatibility including the development of smart mechanisms for energy model creation and resolution parameters refinement. Case studies on different building types and long-term performance monitoring are recommended. This research contributes to the efficiency of exporting data from BIM to BEM interfaces, offering insights for practitioners and guiding future research in BIM-BEM tools integration. [ABSTRACT FROM AUTHOR]

Computer modeling and simulation are essential for predicting architectural solutions performance. When integrated into the design process; they are known as performance-based design. During the design process, fast analytical cycles are required. However, daylighting simulations are challenging due to computational processing and time requirements. This paper presents a comparative study of fast daylight simulation methods, two-phase, three-phase and path tracing, in a hypothetical commercial building located in Miami, Florida. Five types of static glass and one electrochromic glass were tested with six parametric models of a translucent horizontal shading device. Daysim was used as reference. The two-phase method produced results similar to the reference results with faster processing times for static facades. For dynamic facades, the path tracing method with the tested interface enabled faster simulations and parametric variations. However, the three-phase method yielded a lower average error. [ABSTRACT FROM AUTHOR]

Lighting makes up 17% of electrical loads and the largest energy end use in commercial buildings. Linear fixtures are among the largest opportunities for energy efficiency improvement, given their long operating hours. With continued, rapid light-emitting diode (LED) development and more LED replacement products on the market, lighting replacement decisions become more complex and warrant re-examination. With a goal to inform building managers and other decision-makers with practical guidance on lighting replacement, this study is a life cycle cost (LCC) analysis that compares the cost–benefit of six replacement options (one fluorescent and five LED) for a 0.61 m by 1.22 m (2 ft by 4 ft) T8 recessed troffer based on data for 168 lighting products from an online vendor. Results show that direct wire (i.e. unballasted) LED retrofits are the least-cost option to replacing fluorescent lamps in terms of normalized LCC. Plug-and-play lamps suffer from a lock-in with ballasts, but their ease of installation can help spur LED adoption. In cases where an existing ballast is still usable, hybrid LED retrofits provide the least upfront cost option by deferring the cost of rewiring. LED luminaires can offer improved aesthetics and reliability; however, they have high upfront cost. Among them, luminaires with replaceable lamps offer lower cost than those without. [ABSTRACT FROM AUTHOR]

Air conditioning systems can play a positive or negative role in the spread of COVID-19 infection. The importance of sufficient outdoor air changes in buildings was highlighted by the World Health Organization, therefore these should be guaranteed by mechanical ventilation systems or adequate air conditioning systems. The proposed case study concerns the optimal number of outdoor air changes to limit COVID-19 contagion for a school building in Central Italy. The Wells–Riley model is used to assess the risk of airborne infection, while energy consumption is calculated by a dynamic energy simulation software. The scope of the paper offers an innovative method to define the optimal ventilation strategy for the building's HVAC system design to reduce the risk of infection with limited increases in energy consumption and greenhouse gas emissions. Results show that the desirable approach is the one in which the same low value of contagion risk is set in all rooms. This new approach results in significant energy savings, compared to the most common ones (setting the same high outdoor air rates for all rooms) to counteract the risk of infection. Finally, the zero-emission building target is verified by introducing a suitable photovoltaic system to offset pollutant emissions. [ABSTRACT FROM AUTHOR]

In the European Union, buildings account for 42% of the energy consumption and 36% of the direct and indirect energy-related greenhouse gas emissions. Reducing thermal power for heating purposes is crucial to achieve climate neutrality. The main purpose of this article is to identify the places in the building where it is possible to significantly improve energy efficiency through the use of appropriate construction and material solutions. This article contains a multi-aspect approach to the heat balance of a building. Solutions that have a direct impact on building energy consumption were analysed, taking into account architectural, technological, and material aspects. Particular attention was paid to energy-efficient design and material solutions for non-transparent and transparent external walls and thermal storage walls (Trombe walls). An analysis of heat transfer through building elements was carried out, along with the optimisation of energy-efficient solutions for non-transparent and transparent barriers. Two methods for determining the equivalent heat transfer coefficient Ue for solar active partitions are presented. The analysis presented in the work using the original method of the balanced heat transfer coefficient Ue is a testing ground for identifying unfavourable features of the building structure, as well as the most energy-efficient solutions that can be used in establishing standards for the construction and modernisation of buildings. The value of the Ue coefficient illustrates the actual heat transfer through the partition. Having Ue values for various structural solutions of building envelopes, the designer can easily select the most effective ones. The use of the presented methodology will allow for the optimisation of technical solutions for building elements to improve its energy efficiency. [ABSTRACT FROM AUTHOR]

Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees' decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings. [ABSTRACT FROM AUTHOR]

Energy consumption in buildings is a major contributor to India's greenhouse gas emissions, accounting for a significant portion of the country's environmental impact. Consequently, there is a crucial need to prioritize energy-efficient heating, ventilation, and air conditioning technologies supported by solar thermal collectors to minimize the environmental consequences associated with building energy consumption. In this investigation, a flat-plate collector thermal performance is enhanced by the adaptations of 0.1 volume percentage concentrations of aluminum oxide (Al2O3), nickel (Ni), and combinations of Al2O3–Ni nanoparticles dispersed in water as hybrid nanofluid at 0.028, 0.041, 0.055, and 0.068 kgs−1 flow rates. Influences of nanofluid and flow rate conditions on thermal and exergy efficiency, heat transfer coefficient, entropy generation, and coefficient of performance of FPC are experimentally analyzed and spot that the hybrid nanofluid (Al2O3/Ni/water) own higher thermal and exergy efficiency of 72.8% and 22.9%, better heat transfer coefficient of 133.2 Wm−2 K−1, and high COP of 7.9 under high flow rate. These output results are higher than those of the water-fluid-operated FPC. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

Buildings are energy- and resource-hungry: their construction and use account for around 39% of global carbon dioxide emissions; they consume around 40% of all the energy produced; they are responsible for over 35% of the EU's total waste generation; and account for about 50% of all extracted (fossil) materials. Therefore, they present a significant challenge to meeting national and international Net Zero targets of reducing greenhouse emissions and fossil resource use. The CircularB Project, is at the heart of this issue, which will underpin synergies of multi-scale circular perspectives (from materials, to components, to assets and built environments), digital transformation solutions, data-driven and complexity science, stakeholder behavioral science, and interdisciplinary capabilities towards achievable, affordable and marketable circular solutions for both new and existing buildings, for sustainable urban design, and for circular built environments across Europe. This paper contributes to the project by deriving new insights into the stakeholders' influences, inter-relationships, and obstacles in the implementation of circular economy concepts on existing building stocks in Europe, which represent over 90% of whole building assets. In order to identify and derive the insights, our study is rigorously based on (i) a robust critical literature review of key documentations such as articles, standards, policy reports, strategic roadmaps and white papers; and (ii) interviews with relevant stakeholders and decision makers. Uniquely, our work spans across all scales of CE implementation from materials, to products and components, to existing building stocks, and to living built environments. The findings point out the current challenges and obstacles required to be tackled. Inadequacies of financial incentives and governmental enforcement (via policy, legislation, or directive) are commonly found to be the most critical obstacles found throughout Europe. Circular economy is the global challenge and not just a single country can resolve the climate issue without the cooperation of other countries. The insights thus highlight the essential need for harmonized actions and tactical/pragmatic policies promoted and regulated by the European Commission, national and local governments who can dominate the influence, promote inter-relationship, and overcome the barriers towards circular economy much more effectively. [ABSTRACT FROM AUTHOR]

With the increasing need for energy, issues related to using energy efficiently in buildings and employing renewable energy technologies are gaining significance. The building production process is intricate, involving numerous stakeholders, multiple decisions, and a combination of qualitative and quantitative data. This process necessitates decision making based on specific requirements. The objective of this study is to identify effective criteria in decision making concerning the use of renewable energy technologies (RETs) in buildings in Turkey. It aims to highlight the importance of these criteria and compare them, and it also aims to define a recommendation decision-making model for the widespread adoption of renewable energy technologies. This study employed qualitative and quantitative research methods. Based on information gathered from the literature, the main criteria and sub-criteria for RET utilization were determined through in-depth interviews with an expert group, including individuals influencing the building design process (architects, engineers, consultants, employers, and users). A recommendation model was developed using the analytic hierarchy process method to highlight the significance of the identified criteria, compare the criteria and technologies, and facilitate the selection of the most-appropriate technology. This study demonstrates that the decision-making model can be utilized in determining RET-related criteria in the building production process, establishing their weights, and make informed decisions regarding the appropriate technology. [ABSTRACT FROM AUTHOR]

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. This study further demonstrates the HIL testbed's capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc. [ABSTRACT FROM AUTHOR]

In smart buildings, dynamically controlled lighting and shading devices have a direct impact on occupants' thermal and visual comfort, building energy use, and peak demand. This study aims to assess the impacts of adjusting vertical and work plane illuminance sensor location and rotation for different zone orientations across different climate zones, on illuminance levels, glare, energy consumption, and demand. Using the sensor locations of 1.6 m and facing the window (typical assumption), when implementing the integrated shading and lighting controls, the lighting and cooling energy savings range from 41% to 71% across both heating and cooling-dominated climate zones. The greatest cooling peak use demand reduction of 6.5 kW (23%) is achieved in Climate Zone 2 A, where the total cooling and lighting demand savings are 28-33% (9-10 kW). The percent change in lighting energy use is greater as compared to the percent change in cooling energy use when sensor location is varied, however, the total kWh and kW change is greater for cooling in most climate zones. Results suggest that adjusting the sensor distance from the window has a greater impact on illuminance levels, glare, and lighting and cooling energy use in all climate zones as compared to sensor rotation. [ABSTRACT FROM AUTHOR]

The present study aims to explore rowshans as essential vernacular architectural elements in designing houses in very hot-dry climates such as Jeddah, Saudi Arabia, to determine their most significant effects on air movement, ventilation, and mitigating cooling loads. A comprehensive combination of building performance simulation and computational fluid dynamics (CFD) analysis was used to model a room with six different sizes of rectangular openings and quantify rowshans' potential as passive elements in providing occupants with comfort and reducing energy use. Analysis of the passive element revealed the thermal performance and natural ventilation in single-family homes for the Jeddah climate, created by outdoor and indoor temperature, airspeed, and pressure differences in the room model, were improved, lowering sensation temperature for inhabitants' comfort. The results highlight the beneficial effects of rowshans in lowering a house's temperature during the daytime: from November to April, at noon, indoor air temperature (IAT) could reach a 15% to 22% reduction in the north orientation. The findings also show that rowshans with 5 × 5 cm opening grids can keep the air volume flow rate within an acceptable range and keep the room in the comfort zone range for 42.3% of hours annually, equal to 3704 h. An implication of these results is the possibility of establishing housing design criteria that can enhance efficiency and thermal comfort conditions, lower the cost of operations, provide occupants with satisfaction, and reduce emissions to regenerate the environment, leading to affordability and sustainability in the Jeddah region. [ABSTRACT FROM AUTHOR]

"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT's impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed. [ABSTRACT FROM AUTHOR]

Reducing energy consumption in residential and commercial buildings is an important research topic. Thermally activated building systems are a promising technology for significantly reducing energy consumption. The high thermal inertia, large surfaces, and radiative nature are advantages of these systems, but, on the other hand, this makes the system control and design complex. A transient simulation is also required to address the dynamic behavior of the system. The influence of 19 factors (material, design, and operating parameters) on the air temperature and mean radiant temperature inside the room as well as the required cooling equipment power were analyzed to better understand the system. The screening experiment was conducted using the random balance design method, and measurement data were used to validate the resistance–capacitance model. The analysis was performed using the Plackett–Burman design and a design with randomly selected points from a full factorial experiment. The results show that internal heat gains and the inlet water temperature have a significant influence on the system, and the influence of the screed's properties is insignificant compared to other parameters. It should be borne in mind that the obtained results and conclusions are valid for the assumed range of factors' variability. [ABSTRACT FROM AUTHOR]

Embracing sustainable strategies that consider Design for Manufacture and Assembly (DfMA) has become a rapidly growing trend in urban development. Continued uncertainty on the sustainability assessment of design could drive a series of indecisive decision-making among design alternatives, further disrupting the potential opportunities toward sustainable DfMA. However, there is a lack of research on sustainable design assessments for DfMA and establishing a sustainable index. This research establishes an integrated value model for the sustainability assessment framework and DfMA sustainability index to address this challenge. This model integrates Building Information Modelling (BIM) with MIVES, a customisable Multi-Criteria Decision Making (MCDM) tool. The pilot case of this framework is the retrofit of a commercial building's façade system, which demonstrated the capability of the proposed framework. Data collection and analysis include the comparisons between five design alternatives. This research furthers previous studies and has three-fold significance: 1) Establishing reasonable multi-criteria for the sustainable DfMA indices; 2) Adapting the MIVES approach for comparative analysis across three building phases to make it compatible with DfMA; 3) developing a quantitative analysis method for sustainable design assessment of DfMA in the construction industry. [ABSTRACT FROM AUTHOR]

Energy efficiency in buildings has a central role to play in reaching the climate neutrality goal set by the European Green Deal. With detailed knowledge of their building stock and the profile of their occupants, regional and local authorities can forge an important link between financiers, industry professionals and homeowners to ensure the successful deployment of financial schemes that support the uptake of energy efficiency upgrades. This paper provides a first assessment of the role of regional and local authorities by reviewing relevant initiatives and programmes on energy efficiency. Based on a questionnaire completed for more than 150 schemes, it explores how European municipalities and regions stimulate energy upgrades in residential, commercial and public buildings through public financial support. It is found that 60% of the examined schemes are offered in the form of grants and subsidies, and 45% of them targeted residential upgrades. The use of EU cohesion policy funds in supporting regional schemes, and promotion of energy efficiency investments at local level through the European Covenant of Mayors initiative are also explored. In spite of possible resource limitations at this level of governance, regional and local authorities are in a good position to utilise European structural or research funds to develop financial schemes, as standalone programmes or blended with national ones, thus providing additional support and funds for deep renovations. The results suggest that energy efficiency in buildings has become an important part of local and regional strategies in several European countries, but could be further enhanced. [ABSTRACT FROM AUTHOR]

The building sector contributes significantly to the greenhouse effect, generating significant carbon dioxide (CO2) emissions throughout the life cycle of buildings. Traditional methods for assessing emissions, such as software evaluation and site inspection, are time-consuming and do not adequately account for variability and uncertainty in emission data. This research aims to investigate and analyze the statistical characteristics of the operational carbon produced from different types of buildings in the context of the United Arab Emirates (UAE). The investigation focused on residential, commercial and educational buildings and their heating, ventilation, air conditioning (HVAC) systems, walls and window systems. All scenarios were statistically evaluated through linear-regression analysis, correlation analysis, Probability Mass Functions (PMFs) and Cumulative Distribution Functions (CDFs). The results of linear-regression analysis revealed an average accuracy (R2) of 0.958. The results of correlation analysis indicated that upgrading the HVAC system in residential and commercial buildings reduced the operational carbon, while in educational buildings, upgrading the window systems reduced the operational carbon. Finally, the PMF and CDF analyses indicated that upgrading the HVAC system in residential and commercial buildings was the optimal option, which reduced the carbon percentage by 28.56% and 28.48%, respectively. However, upgrading the window system was the optimal option for educational buildings, reducing the carbon percentage by 75.80%. [ABSTRACT FROM AUTHOR]

The present study aims to analyze the correlation between ground motion parameters and energy demands of low-rise RC buildings without shear walls. Two regular 4- and 7-story residential buildings were seismically designed to represent low-rise RC buildings. In order to establish the requirements of single degree of freedom (SDOF) systems as well as multi degree of freedom (MDOF) systems, the dynamic features of "equivalent" SDOF systems were defined by using MDOF systems. The correlation of 20 ground motion parameters (GMPs) of 44 records with the energy demands obtained from a total of 176 nonlinear time history analyses was examined for the SDOF and MDOF systems within the scope of this study. The ground motion parameters (GMPs) were taken as intensity measures (IMs). In contrast, maximum input energy was used as a demand measure (DM) and these energy values were normalized with the masses of the buildings for cases, where the buildings were evaluated together. Parameters related to acceleration and velocity are generally found to yield better results in comparison to the ones related to frequency and displacement. Velocity Spectrum Intensity (VSI) and Arias Intensity (Ia) were obtained to have the highest correlation values as a single parameter. This study suggested new equations by combining multiple ground motion parameters for SDOF and MDOF systems to better reflect damage potential than a single parameter. The use of multiple parameters in combination results in better correlation coefficients. [ABSTRACT FROM AUTHOR]

Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption. [ABSTRACT FROM AUTHOR]

The energy efficiency of existing buildings is a crucial factor in addressing energy consumption challenges in European countries, accounting for nearly 40% of the total energy usage. One such country is Cyprus, which faces significant challenges in transforming its existing building stock into energy-efficient and sustainable structures. To face this situation, extensive focus has been made by the government on the energy-efficient retrofit of non-residential public buildings erected before 2010, which lack any energy efficiency measures. This study examines the case of the Pano Polemidia Cultural Hall (PPCH), which represents the building stock of that period. Through the simulation of two scenarios, before and after the adoption of retrofit measures, the existing energy performance is initially evaluated and then the adoption of sustainable solutions, which improve substantially the energy efficiency and can be easily adopted from the relevant authorities, is explored. These retrofit measures include installation of HVAC system, covering of the shell of the building with external thermal insulation, lighting replacement with LED devices, installation of PV system and solar panels, and replacement of the external openings with aluminum windows. The results derived show that the energy consumption of the building was reduced from 468 to 218 kWh/m2·yr, with renewable energy sources (RESs) contributing 177 kWh/m2·yr, the CO2 emissions were reduced from 136.73 to 11.5 kg/m2·yr, while the reduction in energy consumption per sector ranged from 25% in lighting to 83% in hot water. Therefore, it is evident that a comprehensive retrofitting plan can transform the PPCH into a near-zero energy consumption building that also provides value to the local community and can act as a successful example for any other non-residential buildings with similar characteristics. [ABSTRACT FROM AUTHOR]

This paper investigates the Indoor Environmental Quality (IEQ) factors influencing occupant satisfaction in commercial buildings in Iran, contrasting the views of building experts (architects and engineers) with those of building occupants. Employing the fuzzy analytical hierarchy process (FAHP), this study focuses on the four primary IEQ factors: thermal comfort, indoor air quality, visual comfort, and acoustic comfort. The study aims to bridge the gap between expert evaluations and occupant perceptions of IEQ factors in commercial buildings in Iran. By examining the disparities in prioritising IEQ factors between these two groups, the study sheds light on the complexities of IEQ assessment and highlights the importance of considering diverse perspectives in optimising indoor environments. Our methodology includes a survey conducted among 30 building experts (15 architects and 15 building engineers) and 102 occupants, employing FAHP to derive the relative importance weights of each IEQ factor. The results highlight significant disparities between architects, engineers, and occupants in prioritising these factors. Architects emphasise visual comfort (42%), while engineers and occupants view thermal comfort (53% and 41%) as the most crucial factor for occupant satisfaction. The study underscores the complexity of IEQ in commercial buildings and the diverse perspectives influencing its assessment. It contributes to the broader discourse on optimising IEQ, emphasising the need for a comprehensive approach that encompasses both technical expertise and occupant experience. [ABSTRACT FROM AUTHOR]

Buildings are major consumers of energy, accounting for a significant proportion of total energy use worldwide. This substantial energy consumption not only leads to increased operational costs but also contributes to environmental concerns such as greenhouse gas emissions. In the United States, building energy consumption accounts for about 40% of total energy use, highlighting the importance of efficient energy management. Therefore, accurate prediction of energy usage in buildings is crucial. However, accurate prediction of building energy consumption remains a challenge due to the intricate interaction of indoor and outdoor variables. This study introduces the Partitioned Hierarchical Multitask Regression (PHMR), an innovative model integrating recursive partition regression (RPR) with multitask learning (hierML). PHMR adeptly predicts building energy consumption by integrating both indoor factors, such as building design and operational variables, and outdoor environmental influences. Rigorous simulation studies illustrate PHMR's efficacy. It outperforms traditional single-predictor regression models, achieving a 32.88% to 41.80% higher prediction accuracy, especially in scenarios with limited training data. This highlights PHMR's robustness and adaptability. The practical application of PHMR in managing a modular house's Heating, Ventilation, and Air Conditioning (HVAC) system in Spain resulted in a 37% improvement in prediction accuracy. This significant efficiency gain is evidenced by a high Pearson correlation coefficient (0.8) between PHMR's predictions and actual energy consumption. PHMR not only offers precise predictions for energy consumption but also facilitates operational cost reductions, thereby enhancing sustainability in building energy management. Its application in a real-world setting demonstrates the model's potential as a valuable tool for architects, engineers, and facility managers in designing and maintaining energy-efficient buildings. The model's integration of comprehensive data analysis with domain-specific knowledge positions it as a crucial asset in advancing sustainable energy practices in the building sector. [ABSTRACT FROM AUTHOR]

In buildings, air conditioning and mechanical ventilation (ACMV) systems are the major shareholders of overall energy consumption. Energy-efficient designs for ACMV systems in building applications are therefore needed. While designing an efficient ACMV system, consideration must be given to the growing concerns of enhanced thermal comfort and improved indoor air quality. The variable refrigerant flow (VRF) air-conditioning system is a widely adopted alternative to the existing building cooling systems due to the higher energy efficiency and individualized temperature control feature. However, it still suffers from shortcomings such as no outdoor air induction for ventilation and higher initial cost. Therefore, this paper reviewed the variable refrigerant flow and mechanical ventilation/air distribution systems, their integrated designs for non-residential buildings, performance evaluation and control optimization of the integrated systems, VRF systems' faults detection and diagnosis, current application of the VRF systems, and associated challenges. Together with these all, some advanced buildings' cooling techniques and improvements toward nearly/net-zero energy buildings are briefly discussed. Indoor thermal comfort models and criteria for different climates are also presented for an in-depth understanding of the VRF integrated mechanical ventilation designs. The literature survey shows that the supply air temperature and airflow rate are foremost in parameters that can be optimized in VRF integrated ventilation design as they greatly reduce the energy consumption. Further, policies on elevated indoor temperatures in air-conditioned buildings to mitigate their carbon footprint are strictly being implemented. Therefore, this review provides an insight to the researchers for further improvement in the integrated design and control optimization of the parameters involved. A paradigm shifts from the conventional compression-based electric-powered air conditioning systems to the renewable energy driven advanced air conditioning technologies which is also an emerging research area to be focused on achieving the target of nearly/net-zero energy buildings. [ABSTRACT FROM AUTHOR]

Despite the recognition of the built environment as a sector with high potential for reducing embodied emissions, there is a lack of national-level data on embodied carbon emissions attributed to the US commercial building stocks. Hence, there is a need to benchmark the current state of embodied emissions attributed to the commercial built environment and the impact of circular economy (CE) policies for their decarbonization to facilitate well-informed decision-making. This paper fills this knowledge gap through providing policy makers with a framework that estimates the current and future projections of embodied carbon emissions attributable to the US commercial building stocks and highlighting the decarbonization potential of multiple CE policies. To this end, the following multistep research methodology was adopted: (1) collecting and integrating relevant data; (2) conceptualizing and initializing a system dynamics (SD) model; (3) developing subsystems and coflow structures within the SD model using aggregated stock data from the National Energy Modeling System; (4) verifying and testing the developed SD model; and finally (5) suggesting policies and conducting scenario analysis using CE policy precedents. Findings reveal that in the baseline scenario, cumulative emissions are projected to reach 5,605 MtCO2e by 2050. As for the scenario analysis, the unit size reduction policy emerged as the most impactful single policy, achieving a remarkable 38.8% decarbonization. Whereas the comprehensive CE policy that optimizes floorspace demand and the carbon intensity of materials, exhibited a 52% decarbonization potential by midcentury. As such, this study contributes to the body of knowledge by benchmarking emissions projections, providing insights regarding CE decarbonization strategies, their associated primary stakeholders and implementation pathways. Ultimately, by embracing a holistic approach toward CE policies and harnessing accurate and standardized data on building embodied emissions, stakeholders can effectively attain decarbonization goals and promote a sustainable built environment. [ABSTRACT FROM AUTHOR]

In the past three decades, fire engineering has experienced fast development worldwide. Fire regulations for building industry were moved from prescriptive description to performance-based specification. This trend started in the UK in the 1980s and spread to other western countries. Hong Kong closely followed the UK development and adopted the approach of performance-based design (PBD) for the Hong Kong International Airport and MTR stations in the early 1990s. The Government of Hong Kong SAR formally recognised the PBD approach in the prescriptive codes of practice in the mid-1990s and developed the performance-based code (PBC) in the 2000s. This PBC was promulgated in 2011. The evolution in fire engineering impacted the fire community in Mainland China. The State Key Laboratory of Fire Science (SKLFS) was established in University of Science and Technology of China (USTC) in 1991. Then fire science and fire engineering stepped into the fast development track. The building industry in Mainland China started to adopt the PBD approach in the early 2000s for the significant developments including large airport terminals, railway and metro stations, super high-rise buildings, and mega commercial centres. However, a structured PBC system is not available within the regulatory framework to date. Adoption of PBD approach is slowing down in recent years. This paper presents a review of fire engineering development in Hong Kong and Mainland China. The review focuses on the development of the PBC in Hong Kong, and the PBD approach and fire research in Mainland China in the past 30 years. [ABSTRACT FROM AUTHOR]

The highly visible yet poorly studied phenomenon of roadside colossi—oversized commercial buildings and statues in the shape of everyday objects, referred to in this article as Big Things—has often been dismissed as a kitschy by-product of American post-war consumerism and car culture. There are no universal definitions or typologies for this form of material culture, nor is there a sufficiently global history that explains the origin, spread and contemporary popularity of these landmarks. In this article, I address these gaps in the discourse, drawing attention to the rich yet largely untapped theoretical underpinnings of Big Things. In doing so, I highlight the potential for further study of these landmarks as material evidence of broader socio-cultural impulses, particularly in communities across North America and Australia, where Big Things can be found in their greatest numbers. [ABSTRACT FROM AUTHOR]

Book publisher Claude Barbin was an important contributor to the development of the genre of epistolary novels in France in the late part of the seventeenth century. As a strategy to build commercial success for collections of private letters that he sold in his shop, Barbin instrumentalized tacit and evolving conventions about privacy for correspondence in the Ancién Régime. In this article, I discuss how Barbin instrumentalized such tacit conventions of privacy in the prefaces to collections of purportedly private letters, arguing that the label of "private letter" was part of Barbin's practical knowledge of marketing techniques in seventeenth-century France. [ABSTRACT FROM AUTHOR]

Traditional buildings have a central role to play in Europe's plans to be the first climate-neutral continent by 2050, with the building sector being the single largest energy consumer in the EU with 40 per cent of total energy use. Seventy-five per cent of the existing housing stock is energy inefficient with minute numbers renovated each year. Existing buildings offer huge potential for energy use reduction. Research has shown that a historic retrofit can emit less carbon compared to demolishment and rebuilding. Reductions in carbon emissions of up to 62 per cent were observed across a body of case studies by Historic England, including a 95 per cent reduction in the mews case study described in this paper. Moreover, carefully managed thermal retrofits, taking a 'whole house approach', have many benefits, including improved indoor comfort and air quality as well as reduced operational costs. These are factors which are fundamental in ensuring the continued use of historic constructions while improving their standards of preservation and durability. Thermally upgrading traditional building is still a much-debated topic. Inappropriate thermal interventions with unsuitable materials and finishes can have unforeseen consequences, resulting in building damage and a reduction in internal comfort, health and well-being. This paper aims to contribute to this discussion by providing an overview of key considerations when thermally upgrading the walls of traditional buildings while preserving unique characteristics and functionality. Advantages of adopting thermal mortars such as cork lime are put forward, and their potential efficacy is discussed, concluding with a case study focusing on the deep retrofit of a London mews within a conservation area. [ABSTRACT FROM AUTHOR]

This study investigates the impact of an active workstation on heating and cooling energy consumption in commercial buildings. The improved performance and health of office workers are well-known and supported by several surveys and studies. However, the preferred and revised indoor air temperature for different activities of the employees, such as standing and walking, directly impacts the energy consumption of the building. Therefore, this study systematically quantifies the impact of indoor environments on building energy consumption using a simulation tool. Based on literature reviews with multidisciplinary fields, revised setpoint temperature, metabolic rate, and required outdoor air rate due to the level of active workstation (standing and walking) are applied to EnergyPlus reference building models. The impact on heating and cooling energy consumption is analyzed by considering several representative cities in the United States. Their average sensitivity was found to be 7.28% lower and 14.28% higher for heating and cooling energy, respectively. The total increment of $13,887 represents approximately 0.16% of the total profit of the corporation. This increment can be justified given the benefits to workers' health, which include lower insurance costs and improved performance. This suggests that it is economically desirable to deploy active workstations widely. [ABSTRACT FROM AUTHOR]

Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP. [ABSTRACT FROM AUTHOR]

Automated fault detection and diagnostics analysis in commercial building systems using machine learning (ML) can improve the building's efficiency and conserve energy costs from inefficient equipment operation. However, ML can be challenging to implement in existing systems due to a lack of common data standards and because of a lack of building operators trained in ML techniques. Additionally, results from ML procedures can be complicated for untrained users to interpret. Boolean rule-based analysis is standard in current automated fault detection and diagnostics (AFDD) solutions but limits analysis to the rules defined and calibrated by energy engineers. Boolean rule-based analysis and ML can be combined to create an effective fault detection and diagnostics (FDD) tool. Three examples of ML's advantages over rule-based analysis are explored by analyzing functional building equipment. ML can detect long-term faults in the system caused by a lack of system maintenance. It can also detect faults in system components with incomplete sets of sensors by modeling expected system operations and by making comparisons to actual system operations. An example of ML detecting a failure in a building is shown along with a demonstration of the soft decision boundaries of ML-based FDD compared to Boolean rule-based FDD analysis. The results from the three examples are used to demonstrate the strengths and weaknesses of using ML for AFDD analysis. [ABSTRACT FROM AUTHOR]

Using off-site hydrogen can reduce CO 2 emissions from urban buildings with limited renewable energy generation. We demonstrated the supply and usage of off-site green hydrogen using a pilot-scale hydrogen energy utilization system, Hydro Q-BiC®, for a commercial building. The off-site location supplied green hydrogen to the on-site system using high-pressure gas. By developing rapid-filling-type metal hydride tanks, we could safely fill 100 Nm3 of off-site hydrogen at <1 MPaG in approximately 1 h. The use of off-site green hydrogen in a 24 h operation of Hydro Q-BiC® can extend the operation time of fuel cells and significantly contribute to a reduction in CO 2 emissions. Estimations indicate that gray hydrogen increases CO 2 emissions, and CO 2 emissions can be reduced to zero using a green-hydrogen supply of 300 Nm3. The results indicate that the type and quantity of supply of off-site hydrogen are important for reducing the CO 2 emissions of buildings. • Utilization of off-site hydrogen can help reduce CO 2 emissions from urban buildings. • Hydrogen energy utilization system Hydro Q-BiC® developed for a commercial building. • Hydro Q-BiC® tested to demonstrate efficacy in reducing CO 2 emissions of a building. • Type and quantity of off-site green hydrogen influence reduction in CO 2 emissions. [ABSTRACT FROM AUTHOR]

Climate change has emerged as a global focal point, notably affecting vulnerable countries like Pakistan. The country faces heightened risk, prompting concern among many due to its susceptibility to climate-related hazards. An imperative challenge arising from this phenomenon is the necessary shift towards sustainable energy, particularly concerning energy consumption within the building sector. Buildings significantly contribute to daily energy usage and subsequent GHG emissions. In Pakistan, transitioning to energy-efficient building stock holds pivotal importance in mitigating climate change impacts. This hinges on establishing a robust institutional framework and regulatory structure centered around energy efficiency. While Pakistan possesses policies and national guidelines advocating for energy-efficient buildings, their practical enforcement and application remain inadequate. The inception of the Building Energy Code of Pakistan in 1990, based on ASHRAE 90.1 standards, marked a step forward. However, obstacles rooted in institutional weakness and limited capacity building have impeded its effective adoption and adherence. Although the Energy Provisions of 2011 introduced modifications, their scope remained confined mainly to commercial structures. This paper conducts an extensive assessment of Pakistan's existing institutional and regulatory framework for building energy efficiency and transition. Drawing on literature insights and stakeholder perspectives, an in-depth understanding of prevailing policies and institutions uncovers underlying reasons for noncompliance and limited implementation. Additionally, input was gathered from stakeholders through interviews, aiming to identify barriers obstructing the reinforcement of building energy efficiency measures in Pakistan. Discourse and narrative analysis, informed by literature and stakeholder interviews, revealed the significance of awareness, participation, coordination, capacity building, technical expertise, finance, and resources as key factors contributing to deficient compliance and implementation of existing codes and policy guidelines. This paper also proposes actionable recommendations to expedite the transition towards a sustainable, energy-efficient building stock in Pakistan. [ABSTRACT FROM AUTHOR]

Closed-loop control of commercial building heating, ventilating, and air conditioning (HVAC) for demand response requires measurements used as feedback to the controllers. Demand response effectiveness is usually measured as a power deviation from baseline, but the building automation system (BAS) does not usually collect power measurements, and whole-building electric meters typically measure power at intervals of 15 min, which may be too slow for some types of demand response. Demand response strategies are sometimes focused on components of building HVAC systems, e.g., the response of supply/return fans to temperature set-point changes, but these components are usually not submetered. Fan power can be estimated from physics-based models leveraging BAS data, e.g., airflow measurements; but our ability to effectively close the loop on these estimates is not clear. In this paper, we introduce a massive dataset that contains both submetered fan power data and BAS data for several building HVAC systems during typical operation and demand response events. Through a case study we show that models leveraging BAS data alone do not provide accurate estimates of fan power during event transients, making it unlikely that closed-loop control of commercial building HVAC components for demand response would be effective using BAS data alone. This demonstrates the value of submetering HVAC components. More broadly, our dataset will enable future research bridging the gap between building control and power systems research. [ABSTRACT FROM AUTHOR]

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution. [ABSTRACT FROM AUTHOR]

Commercial building HVAC systems experience many sensing, mechanical, and control-related faults that increase energy consumption and impact occupant comfort. Fault detection & diagnostics (FDD) software has been demonstrated to identify and help diagnose these types of faults. Several studies have demonstrated FDD energy savings potential, but there is limited empirical data characterizing the quantity and type of faults reported by FDD tools. This paper presents results of an FDD fault reporting study, employing multi-year monitoring data for over 60,000 pieces of HVAC equipment, covering over 90 fault types, and using new metrics that we developed to characterize fault prevalence. Study results offer an unprecedented accounting of the quantity of faults reported, the most commonly occurring faults, and fault persistence. We find that 21 air handling unit (AHU) faults were reported on 20% or more AHUs in our dataset, and 18 AHU faults persisted for more than 20% of the time period covered by the data. On any given day, 40% of AHUs and 30% of air terminal units saw a reported fault of some kind. Based on in-depth analysis of these results we provide recommendations for building operators, FDD software developers, and researchers to enable more efficient commercial building operation. [ABSTRACT FROM AUTHOR]

Occupant behaviour is one of the key parameters that significantly impact the operation of heating, ventilation and air-conditioning (HVAC) systems and the energy performance of buildings. Detailed occupancy information can improve HVAC operation and utilisation of building spaces. Strategies such as vision-based occupancy detection and recognition have recently garnered much interest. This study investigated the performance of a vision-based deep learning detection technique for enhancing building system operations and energy performances. The model used was the Faster region-based convolutional neural network with Inception V2. Two occupancy detection model configurations were developed, tested and evaluated. Both models were analysed based on the application of the detector within a selected case study building, along with an evaluation based on different evaluation metrics. The results suggest the occupancy detector using model 1 provided an overall accuracy of 95.23%, while model 2 (the occupancy activity detector) provided an accuracy of 89.37%. In addition, the F1 score, which represents the harmonic mean between the precision and recall values, was 0.9756 for model 1 and 0.8298 for model 2. Building energy simulation was used to evaluate and compare the impact of such an approach on indoor occupancy heat gains. The study highlighted the potential of the detection approaches, but further development is necessary, including optimisation of the model, full integration with HVAC controls and further model training and field testing. [ABSTRACT FROM AUTHOR]

The relevance, relative importance and colinearity of input parameters to the results of building energy demand forecasts were investigated. Two calendar years of historical data including weather variables and days of the week were used. The study also aimed to assess the performance of multiple-linear-regression, support-vector-machine and artificial-neural-network models for predicting the daily heating, ventilation and air-conditioning energy consumption of a commercial building in France. Mean absolute error, root-mean-square error and coefficient of variation of root-mean-square error were selected as the performance criteria. The results showed that the best performance was achieved through the artificial-neural-network model according to all performance measures. Besides, the other two models were not able to meet the prediction requirements for energy consumption in a building since their coefficient-of-variation-of-root-mean-square-error values were not below 30%. The results also indicated that there was multiple colinearity between the number of degree days and outdoor temperature. Furthermore, the most significant parameter for daily energy consumption was found to be the number of degree days, followed by global radiation, sunshine rate and the day of the week. [ABSTRACT FROM AUTHOR]