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The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method's development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

The Qinghai–Tibet plateau, with an average altitude of over 4000 m, has low annual average temperatures and a high demand for building heating. This region's abundant solar energy resources hold substantial practical significance for improving the indoor heat environment and reducing building energy consumption. This paper investigates the impact of orientation and skylight area ratio on building heat load and indoor temperature, using both actual measurement and simulation methods, with a case study of the comprehensive building at Beiluhe Observation and Research Station of Frozen Soil Engineering and Environment (Beiluhe Station), located in the Qinghai–Tibet Plateau region. Initially, a model was established using the EnergyPlus 9.4 software, with orientation variables set from east to west in 15° increments, to simulate the variations in building heat load resulting from orientation changes; simulations were then conducted for three different skylight area ratios under the optimal orientation to evaluate their influence on heat load and indoor temperature. The results show that for the architectural style examined in this paper, the optimal building orientation within the region is 30° south by east, with the optimal orientation range spanning from 45° south by east to due south. Heating load is negatively correlated with the skylight area ratio, and beyond a certain threshold, the rate of decrease in heat load diminishes or even stabilizes. The conclusions of this paper offer guidance for the orientation and skylight design of new buildings on the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

Demand response is an effective solution for balancing supply and demand in modern energy supply systems. For utility or load aggregators, it is important to accurately target potential consumers to participate in demand response programs to recruit a massive number of users. This is especially important for the invitation-based demand response mode, which is currently often used in China. In this paper, a portrait-based method is proposed to effectively identify potential consumers for different demand response tasks based on historical loads. Eight indicators are proposed to quantify the energy consumption characteristics from different aspects, and an evaluation method is introduced. Then, a selection method based on the K-means clustering algorithm and support vector machine classifiers is proposed. The method is tested under two scenarios, including load shifting and monthly peak shaving. The results show that the proposed method can identify potential users effectively, and the accuracy of the trained classification model exceeds 99.9%. The proposed portrait-based identification method provides an effective way to describe users' energy consumption characteristics and select potential users effectively, which is very useful for helping the utility or virtual plant with load management. [ABSTRACT FROM AUTHOR]

During emergencies, healthcare is generally provided by tents and temporary shelters, without considering the environmental and social impact of the structures as a priority, in favor of swift response. The resultant constructions intended as a temporary solution often persist for a long time. This paper aims to analyze an alternative and innovative modular structure designed as a transitory solution in emergencies and everyday life. The aim of this study is to assess and compare the energetic performance of a modular adaptive model for basic healthcare for people who are not subscribed to the Italian National Health System. The main goal is to challenge standard models by proposing a new model able to diminish the weaknesses of the current sanitary models, to improve the social conditions, flexibility and energy efficiency, and the thermal comfort of the occupants. In the first part of the paper, the conceptual framework and the preliminary design of the model are described by investigating the benefits of a safe space as a generator space for care services and the community. In the second part, the technological requirements of the system are defined by comparing the use of different panel structures and low-impact technologies. The energy efficiency and environmental impact of the model are assessed by comparing several panel structures in two different climatic areas in Italy (northern and Mediterranean areas) using SketchUp and EnergyPlus simulation. As a result, different configurations of the model are proposed according to the different climatic areas in order to optimize the model, from both an architectural and a technological point of view (box and panel composition). [ABSTRACT FROM AUTHOR]

Daylighting design is not only dimensioning glazed surfaces to provide sufficient natural light to an occupied space but also a method of analyzing how this can be achieved without unwanted effects, such as gains and losses of heat, glare, and variations in daylighting intensity at various indoor distances and levels. The case study presented in this paper highlights the energy consumed due to a group of windows with a large glazed area in an existing building located in a temperate continental climate area. The energy consumption results from supplementary artificial lighting required to maintain adequate illumination for indoor activities and to counterbalance heat loss during colder periods are evaluated. The analysis performed by modifying the glazed surface led to the identification of an optimum value of window dimensions for minimum energy consumption. The results of the case study highlight the fact that the evolution of the total energy consumption, evaluated as the sum of the energy consumption due to additional heating/cooling and the artificial lighting required to compensate for the reduction in natural light, is strongly influenced by the dimensions of the glazed surfaces, as well as the minimum level of lighting imposed by the regime of activities carried out in the building. Thus, the outcomes obtained in the research show that at lighting values below 500 lx, the total energy consumption is directly proportional to the glazed surface. From values of 500 lx for the illuminance level, the total energy consumption drops from 2730 kWh/year for a window height of 230 cm to 2399 kWh/year for a height of 110 cm, after which it starts to rise again, reaching a value of 2786 kWh/year for a height of 30 cm. This phenomenon is also found at values higher than 500 lx; accordingly, for an imposed lighting of 1000 lx, the minimum total consumption is identified at a window height of 150 cm. The case study presented in this paper clearly highlights a complex relationship between the height of the glazed surface and the energy consumption required to compensate for heating or cooling and the reduction in natural lighting. Lower window heights reduce heat loss or gain but also correspondingly increase the energy consumption of artificial lighting. [ABSTRACT FROM AUTHOR]

The building sector, which is responsible for a significant amount of total global energy consumption, provides substantial opportunities for energy efficiency studies. In the context of historic and traditional buildings, this matter becomes more crucial, as energy efficiency is more complex and challenging. The complexity partly derives from the multiple and diverse values with which the buildings are associated. These values are dynamic. In this paper, we chose historic houses in Gaziantep as our focal point. They provide an indicative example of houses with architectural features that help residents deal with the adverse effects of the hot climate. These specific features are significant for the users not only in terms of thermal comfort but also in terms of heritage values. The value that users attribute to the neighbourhood and their attitude towards buildings change over time. It is seen that thermal comfort plays a key role in energy efficiency and heritage conservation. Hence, understanding the role of thermal comfort perceptions and the ways in which they dictate certain energy efficiency and heritage conservation actions is critical. In this context, this paper addresses these dynamic, complex, and changing interrelationships over time. Drawing upon the dynamic analysis of in-depth, semi-structured interviews with three dwellings in Gaziantep's Bey neighbourhood, we will discuss how residents of historic houses perceive thermal comfort and how they negotiate and prioritise energy efficiency and heritage conservation. [ABSTRACT FROM AUTHOR]

The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments. [ABSTRACT FROM AUTHOR]

The paper discusses the energy efficiency of smart working (SW) as a solution to traditional work-approach issues, with a focus on evaluating benefits for both employers and employees. Remote working, while offering environmental advantages such as reduced commuting and office space use, poses challenges in assessing its true impact. The study presents results from a dynamic analysis on a real residential building, typical of an architectural style diffused in northern Italy, revealing a 15% average increase in energy consumption when all work tasks are performed from home. To address concerns about the environmental impact of SW, the research proposes a method and metrics for evaluation. Four novel indices (SWEET, SEE, SSEE, and 4E) are introduced, providing a structured approach to assess the energy efficiency of SW initiatives. The paper outlines a methodology for data gathering and metric application, aiming to acquire quantitative insights and mitigate disparities arising from a transfer of burdens to employees. This contribution not only signifies a ground-breaking methodology but also addresses an unresolved research question concerning the evaluation of the actual energy efficiency of smart working implementations for both employers and employees. The results underscore the importance of understanding the nuances of SW's impact on household energy usage and its broader implications for sustainability goals. [ABSTRACT FROM AUTHOR]

With the ever-increasing population and historic highest energy demand, the energy efficiency of buildings is becoming crucial. Architectural firms are moving from traditional Computer-Aided Design (CAD) to BIM. However, nearly 40% of the energy consumption is due to buildings. Therefore, there is a need to integrate BIM with Building Energy Modeling (BEM), which presents an innovative opportunity to demonstrate the potential of BIM to minimize energy consumption by integrating building information software with data from existing energy-efficient building automation systems (EBAS). BEM is a form of computational analysis that can be used in conjunction with BIM or Computer-Aided Engineering (CAE) systems. In this paper, an attempt has been made to explore the existing literature on BIM and BEM and identify the effect of the integration of BEM in BIM in the design phase of the project. A recent survey from the last ten years (2012 to 2023) was carried out on Google Scholar, Web of Science, Science Direct, and Scopus databases. Inclusion/exclusion criteria were applied, and papers were scrutinized. From the results, it can be observed that the convergence of BIM and BEM is found to be useful in practical applications; however, projects with short life cycles might not be suitable for this solution. Challenges exist in the interoperability tools which have restrictions on data exchange. Binary translation is found to be the most suitable candidate for data exchange. The analysis further showed that the most used program for integrating BIM/BEM is Green Building Studio developed by Autodesk to improve construction and operational efficiencies. [ABSTRACT FROM AUTHOR]

The energy consumption of residential buildings plays a crucial role in overall energy consumption and environmental sustainability. This paper aims to conduct an energy analysis of a residential house located in China, with a focus on comparing the accuracy of the model, identifying areas for improvement, and proposing energy-efficient solutions. Four sets of temperature sensors were placed to monitor the ambient temperature at which the building is located and the indoor temperature of the residential building during a heating season. The energy consumption of keeping the building running at a low temperature was recorded and compared with the simulation results to verify the accuracy of the model. The monitoring results give the weekly average temperature of each zone on each floor, and the door and window positions, room layouts, and orientations are discussed to analyze the thermal response of the building. In addition, the effect of the heat transfer coefficient of the exterior walls, the heat transfer coefficient of the roof, and the solar heat gain coefficient (SHGC) of the exterior windows on the heating energy consumption of the building are further analyzed through simulations. The results show that, after adding a certain thickness of insulation to the exterior walls and roofs of a building, increasing the thickness of the insulation layer produces little extra energy saving. The use of building windows with high SHGC can effectively reduce building heating energy consumption. [ABSTRACT FROM AUTHOR]

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies. [ABSTRACT FROM AUTHOR]

The adaptive façades serve as the interface between the indoor and outdoor energy of the building. Adaptive façade optimization design can improve daylighting performance, the thermal environment, view performance, and solar energy utilization efficiency, thus reducing building energy consumption. However, traditional design frameworks often neglect the influence of building envelope performance characteristics on adaptive façade optimization design. This paper aims to reveal the potential functional relationship between building façade performance characteristics and adaptive façade design. It proposes an adaptive façade optimization design framework based on building envelope performance characteristics. The method was then applied to a typical office building in northern China. This framework utilizes a K-means clustering algorithm to analyze building envelope performance characteristics, establish a link to adaptive façade design, and use the optimization algorithm and machine learning to make multi-objective optimization predictions. Finally, Pearson's correlation analysis and visual decision tools were employed to explore the optimization potential of adaptive façades concerning indoor daylighting performance, view performance, and solar energy utilization. The results showed that the optimized adaptive façade design enhances useful daylight illuminance (UDI) by 0.52%, quality of view (QV) by 5.36%, and beneficial solar radiation energy (BSR) by 14.93% compared to traditional blinds. In addition, each office unit can generate 309.94 KWh of photovoltaic power per year using photovoltaic shading systems. The framework provides new perspectives and methods for adaptive façade optimization design, which helps to achieve multiple performance objectives for buildings. [ABSTRACT FROM AUTHOR]

The rapid expansion of renewable energy in buildings has been expedited by technological advancements and government policies. However, including highly permeable intermittent renewables and energy storage presents significant challenges for traditional home energy management systems (HEMSs). Deep reinforcement learning (DRL) is regarded as the most efficient approach for tackling these problems because of its robust nonlinear fitting capacity and capability to operate without a predefined model. This paper presents a DRL control method intended to lower energy expenses and elevate renewable energy usage by optimizing the actions of the battery and heat pump in HEMS. We propose four DRL algorithms and thoroughly assess their performance. In pursuit of this objective, we also devise a new reward function for multi-objective optimization and an interactive environment grounded in expert experience. The results demonstrate that the TD3 algorithm excels in cost savings and PV self-consumption. Compared to the baseline model, the TD3 model achieved a 13.79% reduction in operating costs and a 5.07% increase in PV self-consumption. Additionally, we explored the impact of the feed-in tariff (FiT) on TD3's performance, revealing its resilience even when the FiT decreases. This comparison provides insights into algorithm selection for specific applications, promoting the development of DRL-driven energy management solutions. [ABSTRACT FROM AUTHOR]

This paper determines the best design parameters and uses conditions of lightweight aggregate concrete panels containing microencapsulated phase change materials (MPCM-LWAC panels). The main work of this paper includes the followings: (1) The fundamental properties (dry density, thermal conductivity, and specific heat capacity) of MPCM-LWAC were researched to reveal the effect of MPCM dosage on these properties. (2) A model test was carried out to quantify the effect of MPCM dosage on the thermal response of the MPCM-LWAC panel exposed to realistic climate conditions. (3) The numerical simulation was conducted to investigate the effect of MPCM dosage, panel thickness, and outdoor temperature conditions on the thermal response of the MPCM-LWAC panel, which helps to determine its optimum design parameters and use condition. The results showed that the incorporation of MPCM results in lower dry density and thermal conductivity of MPCM-LWAC but higher specific heat capacity. The more MPCM dosage in the MPCM-LWAC panel with a thickness of 35 mm, the lower the energy demand to keep a comfortable interior temperature. Most notably, when the panel thickness exceeds 105 mm, the MPCM-LWAC panel with 5% MPCM only delays the peak temperature. Moreover, the optimal use condition for MPCM-LWAC panels is an average outdoor temperature of 25 °C, which makes the energy demand attain a minimum. [ABSTRACT FROM AUTHOR]

Clothing adjustment by building occupants is a highly effective and prevalent thermal adaptation behavior aimed at achieving thermal comfort. This paper aims to quantify the impact of climate-responsive indoor clothing adaptation on heating/cooling energy consumption. A climate-responsive indoor temperature control strategy based on rural residents' indoor clothing adaptation was proposed and integrated into building energy simulations. Indoor clothing insulations were obtained using a predictive model from the author's prior research. These values were used to calculate indoor setpoint temperatures in terms of the PMV model, which were then input into the building energy simulations. The simulations were conducted using "Ladybug Tools" in Grasshopper. Four simulation scenarios were proposed for winter and summer, respectively, to compare heating/cooling energy use with different indoor clothing strategies (constant and dynamic) and thermal comfort requirements (neutral and 80% acceptable). The results indicated that indoor clothing adaptation significantly reduced indoor setpoint temperatures by 5.0–6.7 °C in winter. In contrast, the impacts on summer indoor setpoint temperatures were not significant. The impacts of indoor clothing adaptation on energy use were evident in both seasons and more pronounced in winter. With a neutral thermal comfort requirement (PMV = 0), total heating and cooling energy use decreased by 35.6% and 20.2%, respectively. The influence was further enhanced with lower indoor thermal comfort requirements. With an 80% acceptable thermal comfort requirement ( P M V = ± 0.85 ), total heating and cooling energy use decreased by 63.1% and 34.4%, respectively. The climate-responsive indoor temperature control strategy based on indoor clothing adaptation and its impact on heating/cooling energy consumption suggested a viable approach for improving building energy efficiency in China's rural area and similar cost-sensitive and fuel-poverty contexts. [ABSTRACT FROM AUTHOR]

This paper describes an empirical experiment of Internet of Things (IoT)'s integration in the Post-Occupancy Evaluation (POE) process. The experiment aimed to trial a novel IoT approach to enabling building user responsiveness to prevalent IEQ for individualised comfort. The purpose is to provide a system that mitigates a common issue of centralised air conditioning that limits occupants' control over their immediate environment. To achieve this, an IoT platform was developed with smart IEQ monitoring sensors and wearable devices and trialled with PhD researchers in a shared university workspace. The findings provided empirical evidence of IoT's enhanced benefits to improving user control over their individual comfort and enabling positive energy behaviour in buildings. Specifically, the IoT system provided real-time insight into CO2 concentration data while enabling responsive occupant interaction with their immediate environment and at-the-moment mitigation actions. Outputs of the experiment showed that the perceptions of participants about the stuffiness of the air, productivity, and healthy environment were significantly better after taking the mitigation action compared to before. Also, we found a significant relationship between measured CO2 concentration readings and perceived air stuffiness (p = 0.004) and productivity (p = 0.006) and a non-significant relationship between CO2 concentration readings and perceived healthy environment (p = 0.058). Interestingly, we observed that irrespective of the similarities in recorded CO2 concentration readings being within acceptable ranges (632–712 ppm), the perception of air stuffiness significantly differed (p = 0.018) before and after the mitigation actions. The effectiveness of the developed IoT platform was evidenced as most of the participants found the process very easy to participate in with little interruptions to their work as little time was consumed. The results are useful in modifying approaches to building occupant comfort and energy behaviour in commercial and residential settings. [ABSTRACT FROM AUTHOR]

Current research on rural houses in China mainly focuses on improving energy efficiency, with relatively few studies addressing energy-saving measures and enhancing thermal comfort for residents. Therefore, this paper focuses on existing rural houses in Nantong City, Jiangsu Province, as the research object. Through on-site measurements and questionnaire surveys, it was found that the average indoor temperature of rural houses is 28.5 °C in summer and below 10 °C in winter, failing to meet the comfort needs of the villagers. To further study human thermal comfort, a linear regression method was used to establish an indoor Mean Thermal Sensation (MTS) model for Nantong's rural houses. The neutral temperature in summer was found to be 26.46 °C, and an adaptive thermal comfort model for rural residents in the Nantong area was established. Through single-factor simulation and orthogonal experiments, the optimal comprehensive energy-saving renovation scheme was proposed. Finally, a typical rural house in Zhangzhuang Village was used as a case for building renovation practice. After the renovation, the number of thermal comfort hours increased by 145 h per year, the thermal comfort compliance rate reached 47.07%, and the overall energy-saving rate was 57.41%. [ABSTRACT FROM AUTHOR]

This paper proposes a temperature-adaptive radiative cooling (TARC) coating with simple preparation, cost effectiveness, and large-scale application based on a thermochromic powder. To determine the energy efficiency of the proposed TARC coating, the heat transfer on the surface of the TARC coating was analyzed. Then, a typical two-story residential building with a roof area of 258.43 m2 was modeled using EnergyPlus. Finally, the energy-saving potential and carbon emission reduction resulting from the application of the proposed TARC roof in buildings under different climates in China were discussed. The results showed that the average solar reflectivity under visible light wavelengths (0.38–0.78 μm) decreases from 0.71 to 0.37 when the TARC coating changes from cooling mode to heating mode. Furthermore, energy consumption can be reduced by approximately 17.8–43.0 MJ/m2 and 2.0–32.6 MJ/m2 for buildings with TARC roofs compared to those with asphalt shingle roofs and passive daytime radiative cooling (PDRC) roofs, respectively. This also leads to reductions in carbon emissions of 9.4–38.0 kgCO2/m2 and 1.0–28.9 kgCO2/m2 for the buildings located in the selected cities. To enhance building energy efficiency, TARC roofs and PDRC roofs are more suitable for use on buildings located in zones with high heating demands and high cooling demands, respectively. [ABSTRACT FROM AUTHOR]

Machine learning algorithms have proven to be practical in a wide range of applications. Many studies have been conducted on the operational energy consumption and thermal comfort of radiant floor systems. This paper conducts a case study in a self-designed experimental setup that combines radiant floor and fan coil cooling (RFCFC) and develops a data monitoring system as a source of historical operational data. Seven machine learning algorithms (extreme learning machine (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM)) were employed to predict the behavior of the RFCFC system. Corresponding prediction models were then developed to evaluate operative temperature (Top) and energy consumption (Eh). The performance of the model was evaluated using five error metrics. The obtained results showed that the RF model had very high performance in predicting Top and Eh, with high correlation coefficients (>0.9915) and low error metrics. Compared with other models, it also demonstrated high accuracy in Eh prediction, yielding maximum reductions of 68.1, 82.4, and 43.2% in the mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking algorithm analysis was also conducted. The obtained results demonstrated the importance of adjusting parameters, such as the radiant floor supply water temperature, to enhance the indoor comfort. This study provides a novel and effective method for evaluating the energy efficiency and thermal comfort of radiant cooling systems. It also provides insights for optimizing the efficiency and thermal comfort of RFCFC systems, and lays a theoretical foundation for future studies integrating machine learning algorithms in this field. [ABSTRACT FROM AUTHOR]

The global construction industry significantly contributes to energy consumption and greenhouse gas emissions, necessitating immediate action for sustainable development. Recognizing the impact of buildings on emissions, the United Nations has set ambitious energy-related goals for 2030. Retrofitting buildings emerges as a strategic method for reducing energy consumption, offering lower environmental impact and life cycle costs. However, retrofitting is a complex process influenced by diverse factors such as policies, available resources, techniques, building-specific data, and uncertainties. Thus, this paper reviews the existing literature on retrofitting strategies for tropical and humid climates to identify effective approaches for enhancing energy efficiency, thermal comfort, and overall building performance in these regions. Through comprehensive analyses, including bibliometric analysis using VOSviewer version 1.6.18 and systematic assessments, this study investigates various retrofitting strategies. This study categorizes tropical climates into Af (Tropical Rainforest Climate) and Aw (Tropical Savanna Climate) based on the Köppen climate classification. It reveals distinct emphases, with Af climates concentrating on office buildings and Aw climates prioritizing residential structures. Passive strategies were predominantly favored in office buildings, with glazing being the most commonly implemented approach. Residential structures, on the other hand, adopted a combination of passive strategies such as phase change materials along with active methods like appliance replacement. Educational buildings tended to rely on passive strategies, including roof covers, shading, and glazing. The absence of specific cost values underscores the importance of establishing baseline metrics, revealing significant challenges in retrofit techniques. This study further highlights an opportunity to explore passive methods in educational buildings, stressing the need for comprehensive guidelines, especially in institutional settings. Moreover, it emphasizes the urgency for ambitious regulations to address carbon emissions and optimize energy efficiency in tropical climates. [ABSTRACT FROM AUTHOR]

In the early stages of building design, decisions are made about the building's form and envelope, but designers rarely base their decisions on sophisticated energy simulations, even though these features are critical to a building's energy performance. This paper employs three methods—empirical, parametric, and uncertainty—to assess the interconnectedness of building form, envelope, orientation, and occupancy regarding thermal comfort and energy consumption for heating and cooling a residential building across three regions: Gdyel (mediterranean climate), Oum El Bouaghi, and Constantine (semi-arid climate). The study variables include indoor air temperature, relative humidity, and energy consumption. The initial findings stem from an experiment conducted in an apartment on the top floor of a building in Gdyel, which allowed us to record the evolution of the variables mentioned throughout the year and validate the parametric results of the multi-zone model created in TRNSYS16 software. This study showed that for the considered climates, a compact form is more suitable; it was found that the top floor with SF = 0.57 needs about 30% to 54% more energy than the inter-floor with SF = 0.21. In addition, the heating and cooling methods and habits adopted by Algerian households are responsible for 18% to 35% on the top floor and the inter-floor, respectively. [ABSTRACT FROM AUTHOR]

In this study, we critically examine the potential of recycled construction materials, focusing on how these materials can significantly reduce greenhouse gas (GHG) emissions and energy usage in the construction sector. By adopting an integrated approach that combines Life Cycle Assessment (LCA) and Material Flow Analysis (MFA) within the circular economy framework, we thoroughly examine the lifecycle environmental performance of these materials. Our findings reveal a promising future where incorporating recycled materials in construction can significantly lower GHG emissions and conserve energy. This underscores their crucial role in advancing sustainable construction practices. Moreover, our study emphasizes the need for robust regulatory frameworks and technological innovations to enhance the adoption of environmentally responsible practices. We encourage policymakers, industry stakeholders, and the academic community to collaborate and promote the adoption of a circular economy strategy in the building sector. Our research contributes to the ongoing discussion on sustainable construction, offering evidence-based insights that can inform future policies and initiatives to improve environmental stewardship in the construction industry. This study aligns with the European Union's objectives of achieving climate-neutral cities by 2030 and the United Nations' Sustainable Development Goals outlined for completion by 2030. Overall, this paper contributes to the ongoing dialogue on sustainable construction, providing a fact-driven basis for future policy and initiatives to enhance environmental stewardship in the industry. [ABSTRACT FROM AUTHOR]

With the trend of achieving both energy efficiency in buildings and occupants' comfort, gamification strategies have started to be developed and applied as incentive mechanisms to increase social interaction and facilitate human energy behavior transformation. In this article, 306 published papers are reviewed, and 21 studies are identified to determine the challenges and potential for the development of gamification strategies to improve building energy efficiency. Specifically, this work reviews the implementation techniques of gamification and methods to assess the impact of gamification mechanisms on human energy behavior changes. This analysis demonstrates that, firstly, the choice of an optimal gamification implementation method should be inherently attuned to the distinct characteristics of the building type and its occupants. Secondly, it is imperative to strike a judicious balance between extrinsic and intrinsic motivations, in which customization of gamification design elements are based on users' unique personality traits and preferences, to properly tailor gamification mechanisms. Thirdly, integrating a fusion of quantification of energy savings and qualitative interpretation of user behaviors to improve the energy efficiency in buildings is essential for a more holistic understanding of the impact of gamification on users' energy-related behavior change. The findings indicate that gamification techniques can enable the effective reduction of energy consumption in buildings. [ABSTRACT FROM AUTHOR]

With the development of Chinese society, there is an increasing demand for emissions reduction and the stable operation of the power grid. Regional comprehensive energy supply systems have entered the public's view owing to their advantages of reducing capacity, unified dispatch, improving efficiency, and reducing energy consumption. This paper focuses on a system under construction in Chongqing, which adopts a combined gas tri-supply (combined cooling, heat, and power, CCHP) and dynamic ice storage cooling system as the research object. By establishing a mathematical model for the simulation research, this study examines the start–stop priority sequence of the gas tri-supply subsystem and the heat pump subsystem under the ice storage priority strategy in winter and summer and proposes corresponding optimization solutions. By comparing the annual operating energy consumption of the system, we conclude that the gas tri-supply composite system has good economic efficiency and peak-shaving capability, indicating that regional gas tri-supply composite systems have great application potential in the future. The proposed optimized operation strategy and simulated energy consumption calculation provide theoretical guidance for the construction and operation of both this project and similar projects. [ABSTRACT FROM AUTHOR]

This paper presents a systematic analysis of energy savings and cost benefits associated with several options for integrating energy efficiency and renewable energy technologies. The primary goal of this study is to assess the cost-effectiveness of achieving optimal net-zero energy (NZE) designs for residential buildings in Australia. Specifically, the analysis combines a series of sensitivity analyses and multi-objective optimizations to account for a wide range of design strategies for detached homes in four cities representing different Australian climates. The results indicate that not only are NZE designs technically feasible for all the considered Australian cities, but they are also highly cost-effective. This cost-effectiveness is attributed to the lower installation costs of rooftop PV systems as well as the beneficial interactive effects of proven energy efficiency strategies. Indeed, it is found that the deployment costs of rooftop PV systems can be recovered in less than 4 years. Moreover, the addition of thermal insulation in walls and ceilings can reduce both HVAC capacities and annual energy end-use by up to 59%. Based on an optimization-based design, NZE homes in Australia can have lower construction costs and, ultimately, lower life cycle costs than dwellings built to meet current energy efficiency standards based primarily on stringent building envelope thermal performance. [ABSTRACT FROM AUTHOR]

Because they are in enclosed underground buildings, the generator layers of hydropower stations have limited ventilation. In order to reduce the influence of a hot and humid environment on equipment and staff health and create a good thermal environment with good air quality for underground buildings, in this paper, vertical wall-attached ventilation was combined with the generator layer of a hydropower station to replace traditional ventilation. The influence of air supply velocity, air supply outlet position, and the opening mode of the generator layer on indoor velocity and temperature field distribution were analyzed via numerical simulation, and the evaluation indices of different cases were also compared. In the single-sided vertical wall-attached ventilation mode, when the velocity was increased from 4 m/s to 8 m/s, the maximum increment in the energy utilization coefficient was 41%, and the maximum reduction in the velocity non-uniformity coefficient was 9.5%. The results show that the single-sided mode can offer a higher ventilation efficiency than the double-sided mode, with a higher energy efficiency and a more uniform air distribution. Based on the mean temperature and velocity, and the key evaluation indices (head-foot temperature difference, percentage of dissatisfaction, non-uniformity coefficient, energy utilization coefficient, and air diffusion performance index), it is suggested that the single-sided air supply mode should be adopted for this kind of tall building, with an air supply velocity of v = 6 m/s and two open air supply outlets at each interval. [ABSTRACT FROM AUTHOR]

Directive 2018/844/EU on energy efficiency claims that almost 80% of the final energy consumption in the EU is used within the buildings. The subsequent Directive (EU) 2023/1791 introduced in 2023 expected 68% of the world population to live in urban areas. Both directives enhanced the role of energy management (EM) applied to the city buildings. This paper introduces the development of energy management in Czech municipalities between the years 2009 and 2023. The main goal of this article is to present selected results of the unique national survey focused on the energy management in Czech municipalities conducted in 2023 and compare it with results of national surveys implemented in 2009 and 2018. The main method is the comparing of two surveys conducted among the municipal representatives or energy managers in the past with the new survey conducted in 2023. The results show that EM became a notably more important issue for most Czech municipalities. The number of cities collecting data on energy consumption grew by 288% and the number of city energy managers increased by 57%. On the other hand, the used potential of energy savings triggered by establishing EM was insufficient and only 4% of the respondents claimed high and exactly measured savings because of EM. This article should be applied to the stress power of municipal EM in performing energy efficiency and RES projects and encouraging the government to support municipal energy managers in their complex role. [ABSTRACT FROM AUTHOR]

There is an urgent need to promote the technical means of energy conservation in rural buildings. However, it is difficult to inspire enthusiasm among rural residents to implement the mandatory building energy conservation policy, resulting in invisible and avoidable waste. Based on the characteristics of energy-saving technology for buildings and the theory of planned behavior, structural equation modeling was adopted to explore the transformation process of the "cognition–willingness–behavior" of rural residents. The model results validate the hypothesis that behavioral attitudes, subjective norms and perceived behavioral control influence the adoption of energy-efficient building technologies by rural residents. The results show that in terms of the factors influencing rural residents' willingness to adopt building energy-saving technology, behavioral attitudes have the greatest impact, followed by subjective norms and perceived behavior control, and that rural residents' subjective norms can indirectly promote their willingness to adopt building energy-saving technology, which is mediated through their behavioral attitudes. The paper concludes with suggestions for countermeasures to enhance buildings' energy efficiency and energy management in rural areas. [ABSTRACT FROM AUTHOR]

Massive data can be collected from meters to analyze the energy use behavior and detect the operation problems of buildings. However, missing and abnormal data often occur for the raw data. Effective data filling and smoothing methods are required to improve data quality before conducting the analysis. This paper introduces a data filling method based on K-SVD. The complete dictionary is trained and then utilized to reconstruct incomplete samples to fill the missing or abnormal data. The impacts of the dictionary size, the data missing continuity, and the sample size on the performance of the proposed method are studied. The results show that a smaller dictionary size is recommended considering the computational complexity and accuracy. The K-SVD method outperforms traditional methods, showing a reduction in the MAPE and CVRMSE by 3.8–5.4% and 6.7–87.8%. The proposed K-SVD filling method performs better for non-consecutive missing data, with an improvement in the MAPE and CVRMSE by 0.1–4% and 5.1–6.7%. Smaller training samples are recommended. The method proposed in this study would provide an effective solution for data preprocessing in building and energy systems. [ABSTRACT FROM AUTHOR]

Control strategies for variable-air-volume (VAV) air conditioning significantly affect both the air quality within buildings and the consumption of building energy. Current control techniques effectively regulate room temperature using feedback on temperature discrepancies, yet they also elevate the wear on terminal devices and boost the energy usage of the supply fan. In this paper, the hysteresis and inertia parameters of end air valves and supply fans under two seasonal conditions are derived from experimental data. Aiming at the problems of frequent switching of the end air valve, long total switching stroke, and high energy consumption of the air supply fan, a fuzzy PI regulation method is proposed based on the original pressure-independent series PI regulation, which effectively solves the above problems. Initially, data on how room temperature reacts to changes in air supply fan speed and the position of end air valves during winter and summer were gathered. Following model identification, parameters for various seasonal conditions were determined. Secondly, the roles of different components in the variable-air-volume regulation process were investigated. Investigations revealed that within pressure-independent variable-air-volume control, the supply fan and end air valve emerged as the primary subjects of the study. A double-closed-loop control with the speed control of the supply fan as the outer loop and the opening control of the end air valve as the inner loop was adopted. Compared with the traditional serial PI regulation, the room temperature error of this method was increased, but it reduced the total stroke of the valve by more than 43%, which greatly reduced the valve's loss and noise and saved more than 2.7% of the energy consumption of the air supply fan. [ABSTRACT FROM AUTHOR]

Due to their higher energy efficiency and better economic performance, distributed energy resource (DER) systems are expected to be one of the main energy supply forms in the future and have gained increasing attention in recent years. Thus, there is a need to boost our understanding of how to apply DER systems in different types of actual cases. This paper investigates a techno-economic analysis of a DER system applied in a real case in a hot-summer and cold-winter zone in China, where the climate is considered to be Cfa according to the Köppen–Geiger climate classification system. An urban underground transportation infrastructure system located in Changsha (China) was chosen to analyze the techno-economic performance of a natural-gas-fired DER system in comparison with a centralized energy system (CES). First, a scientific and reasonable application program of the natural-gas-fired DER system was developed by an overall load analysis (electric load, air-conditioner load, and domestic hot water load during the operating period). Based on this load analysis, this research combined the energy consumption and the actual operating situation and then compared and analyzed different types and capacities of equipment in this case. Moreover, a comprehensive analysis of the economic benefits was estimated by comparing the natural-gas-fired DER system with conventional CESs. Overall, the total annual cost of the DER system was reduced by 18.73%, and its additional investment can be paid back within about 2.2 years. A better economic benefit was achieved by applying the natural-gas-fired DER system in an actual case, which will help encourage the widespread application of DER systems. [ABSTRACT FROM AUTHOR]

The dry Mediterranean climate (BShs) is the European region with the highest number of hours of sunshine per year. The high annual solar radiation makes sun shading devices necessary to comply with current energy efficiency standards. However, these standards do not sufficiently consider their effect on the indoor lighting comfort of buildings. The objective is to qualitatively and quantitatively determine how movable sun shading devices jointly influence the energy efficiency, thermal comfort and lighting comfort of buildings in BShs climate. The scientific novelty of the work consists of demonstrating the limitations of the sun shading systems commonly used in southeastern Spain and determining the optimal technical solution in this climate to simultaneously improve thermal and lighting comfort. This research comparatively studies the influence of various movable sun shading systems on the daylighting and thermal performance of an educational building. This study conducted on-site measurements, user surveys and computer simulations to study how to improve the thermal and lighting performances of the building. This work demonstrates that interior solar shading provides little improvement in thermal comfort and reduces the cooling demand by only 25%. External movable sun shading improves thermal comfort and reduces the cooling demand by more than 60%, but only adjustable blinds or awnings achieve adequate and homogeneous illuminance values as they diffuse daylight. The paper concludes that energy efficiency standards should be modified to ensure adequate lighting comfort in buildings. [ABSTRACT FROM AUTHOR]

The aim of this paper is to explore the rehabilitation strategies for multi-family dwellings on the level of function and techniques. The study employs its own methods of analysis using a sample of selected cases as a reference. Nearly 20% of EU buildings will have to be renovated by 2023, as almost 40% of the existing houses were built before 1980. The environmental impact of construction is among the highest among industrial activities due to the high consumption of resources and the generation of low-value waste at the construction and demolition stages. One way to reduce the environmental impact in this sector is to intervene in the building process, optimising the use of resources and waste generation. The principal objective of refurbishments is to reduce household energy consumption. However, the renovations in the housing sector should not be limited to energy considerations; the functional and technical aspects should also be considered. A new refurbishment model is necessary to focus on providing and improving the habitability of the housing stock and reducing the environmental impact by optimising the use of resources and waste generation. To achieve this, the renovations should be carefully monitored. [ABSTRACT FROM AUTHOR]

Daylighting has emerged as a prominent strategy for elevating indoor environments by harmonizing visual comfort and energy efficiency. This paper introduces a louver system crafted for energy simulations, specifically tailored to address lighting and cooling requirements in office spaces. Louvers, acknowledged for their exceptional efficiency in providing daylight, are integrated as a pivotal energy-saving technique. Adopting a quantitative research approach facilitated by building information simulation tools, DIALUX evo and Rhino were employed for modeling and simulating the building's daylighting performances. The simulation outcomes reveal substantial energy savings, particularly in the realms of lighting and cooling. Notably, a 50% louver opening in office spaces results in an impressive 27.0% reduction in energy consumption. The study explores various louver configurations, providing insights into both lighting and cooling energy savings. The overall system performance excels in sustaining consistent daylight, significantly contributing to enhanced energy efficiency. [ABSTRACT FROM AUTHOR]

Artificial lighting comprises nearly one-third of the total electrical load of buildings, resulting in significant carbon emissions. Reducing the carbon emissions caused by artificial lighting is one of the ways to achieve low-carbon buildings. To meet the demand for high-efficiency, energy-saving, and comfortable lighting, light pipes are increasingly used in buildings. This paper reviews the research and development of light pipes and integrated technology. Sky conditions as a dynamic factor always affect the performance of light pipes. The combination of light pipes and an artificial lighting system can effectively solve this problem. A light pipe can be integrated with a ventilation stack to achieve the ventilation and cooling or heating of a building. A lighting-heating coupled light guide can improve the energy efficiency and sustainability in buildings, such as where antimony tin oxide nanofluid is introduced to absorb additional heat and then provide domestic hot water. The application of a photocatalyst to light pipes can realize air purification and self-cleaning. The use of light pipes does not consume electricity and can reduce the time spent using artificial lighting, thus allowing for power savings. From a whole life cycle perspective, the use of light pipes can be a balance of cost and benefit. In conclusion, such information could be useful for engineers, researchers, and designers to assess the suitability of applying integrated light pipes in different building types and examine the potential of energy and cost savings. [ABSTRACT FROM AUTHOR]

In many countries, building airtightness is mandated by national regulations or energy efficiency programs, necessitating accurate measurements using the fan pressurization method. Given the significant influence of wind on measurement uncertainty and the need for reliable regulatory tests, experimental studies in a controlled environment are needed. This paper presents a novel experimental facility designed to replicate fan pressurization measurements on a model scale under controlled laboratory conditions. The key features of the facility include the ability to (1) conduct fan pressurization measurements, (2) generate steady wind conditions across varying wind speeds, and (3) accurately measure parameters like the pressure difference, wind speed, and airflow rate. The experimental facility includes a pressurization device, a wind tunnel, and a model representing a two-story house with nine distinct leakage distributions. A total of 96 fan pressurization measurements were executed using this setup, adhering to the similarity conditions specifically defined for assessing airflow errors due to wind. These tests followed the ISO 9972 standard, with the pressure differences ranging from 10 Pa to 100 Pa and steady wind speeds from 1 m·s−1 to 7.5 m·s−1. This experimental facility marks a significant advancement in understanding the effect of wind on building airtightness measurements. [ABSTRACT FROM AUTHOR]

While universities are expected to exemplify sustainable practices, they often encounter high energy demands. This dichotomy highlights the necessity for research into their energy consumption. Through a Systematic Literature Review, we examined international research trends in this field and explored factors influencing energy consumption. The importance of this article stems from its novel approach to energy consumption in universities, addressed from a global and comprehensive perspective, offering generalizable insights. Additionally, it pioneers in the use of a market concentration indicator (Herfindahl–Hirschman index) to measure the level of diversity in various bibliometric aspects. The extended perspective of our approach helps to close knowledge gaps about scientific trends and common energy consumption factors. Our results show that this topic has been investigated with limited involvement of social sciences. Building function, research intensity, and disciplinary orientation are distinctive factors in energy consumption in this field. Most influential authors, countries, and journals in this area were identified. This analysis contributes academically, by mapping research trends and providing guidance for future studies; practically, by offering insights for educational administrators on common factors affecting energy consumption; and in terms of policy, by advocating for the promotion of social sciences-based investigations on the topic. [ABSTRACT FROM AUTHOR]

Taking the high and large space of the University of Science and Technology Beijing Gymnasium as this research object, this paper analyzes the influence of different window positions, window-to-wall ratio (WWR), solar heat gain coefficient (SHGC), heat transfer coefficient (K), and visible light transmittance (VT) on the total indoor energy consumption in winter and summer and obtains the relationship between the daylight factor and VT formed when the window is opened per unit area. Through energy consumption simulation, the variation law and calculation formula for indoor total energy consumption are obtained. The results show that the SHGC and K of the exterior window have a significant influence on the total energy consumption. By using the energy consumption simulation of different types of exterior windows, it is concluded that the SHGC of the south-facing window is negatively correlated with the variation of air conditioning energy consumption per unit area Δe1,w, while the others are positively correlated. Moreover, the SHGC and K of the skylight have the most significant influence on the Δe1,w. The total energy consumption decreases and then increases with the increase in the window area, and there is a lowest point, so the right side of the lowest point is less than or equal to 105% of the lowest total energy consumption as a reasonable window area zone. Finally, a progressive optimization method for weighing daylighting and energy consumption in university gymnasiums in Beijing is proposed. [ABSTRACT FROM AUTHOR]

Utilizing integrated solar systems and renewable energy sources has the potential to not only decrease the CO2 emissions of buildings but also provide access to more affordable energy alternatives compared to fossil fuels, especially considering the recent rise in prices. Nevertheless, many designers and project decision makers are hesitant to embrace solar technologies due to the uncertainty surrounding the cost–benefit balance. This paper presents a case study of the design process, highlighting the energy-saving and cost–benefit aspects of a solar façade featuring solar thermophotovoltaics (STPVs) and wavelength-selective thermophotovoltaics (TPVs) in a high-rise residential building situated in the semi-arid climate of Tehran, Iran. The simulation methodology consists of EnergyPlus Engines in Rhino, along with the Ladybug and Honeybee plugins throughout the solar façade design process. The solar façade incorporating STPV yielded energy savings of 25 kWh per square meter, marking a 34% reduction compared to a standard façade. In contrast, the TPV demonstrated energy savings of 35 kWh per square meter, indicating a 48% decrease in energy consumption compared to a regular façade. This research indicates that, particularly in semi-arid climatic conditions, TPV systems exhibit a superior performance when integrated into the façades of high-rise structures; yet, due to the low electricity prices in the region, neither STPVs nor TPVs are financially viable. The study contributes to raising awareness, fostering technological innovations, influencing policy discussions, and promoting the adoption of sustainable practices in the field of energy and architecture. [ABSTRACT FROM AUTHOR]

Overheating in school buildings can negatively affect the cognitive learning performance of particularly young students whose thermoregulation systems are still developing. However, currently, in schools, issues related to overheating have been addressed by limiting the exposure time to thermal discomfort. In this paper, the development of a general procedure that combines building and bioheat simulations to evaluate overheating risk in schools and generate health-based overheating limit criteria that may be applied in Canadian schools is described. General school building models, having either old or new constructions, were created based on a primary school building and successfully calibrated using field measurements of indoor temperature and humidity and published building energy use intensity data. Three sets of two limit criteria (exposure duration and severity of overheating) that account for the personal exposure conditions of students in primary, middle, and secondary schools were developed by limiting the body dehydration of students during extreme overheating events. Comparing the proposed limit criteria with the hour of exceedance criterion revealed interesting relationships between them, suggesting the proposed limit criteria as a benchmark for the comfort-based criteria, particularly for the more vulnerable primary and middle schools. The proposed procedure with the obtained overheating limit criteria is intended to be applied in any field or simulation study to assess the risk of overheating in similar school buildings under any local prevailing climate. [ABSTRACT FROM AUTHOR]

In the frame of developing sustainable, reliable, and regenerative interventions on existing buildings, namely on large-scale public housing, the implementation of functional, technological, and effective strategies is devoted to thoroughly assessing the transformability of buildings using trustworthy performance indicators. With this aim, in the present paper, an assessment framework tool is presented and tested to evaluate the potential of buildings for transformation through the detection of regeneration strategies for the reduction of energy consumption, a definition of the new apartments' internal layout, and the implementation of sustainable systems to foster rainwater harvesting. The procedure is tested on a case study in Latina (Italy), showing its suitability to quantitatively assess the regenerative potentiality of public housing, thus resulting in an effective supporting tool for designers and policy makers. [ABSTRACT FROM AUTHOR]

The current design of commercial service spaces in many communities faces issues like incoherence, irrational resource allocation, and low utilization rates. These challenges contribute to increased energy consumption in communities, hindering the overall sustainable development of cities. As a representative community space in the urban environment, the commercial space within the campus requires continuous energy input. Its energy-efficient layout aligns with the principles of sustainable development. This paper uses the university campus as a case study to examine energy-efficient commercial space layout and community practices for environmental protection. Various factors influence the layout of inter-community commercial spaces, and the parameters for measuring the layout structure are diverse, considering the large sample size. Employing machine learning and big data processing to quantify development indicators across various industries and optimize their structure, resource allocation, and energy use has emerged as a viable tool for sustainable urban planning practices. This research seeks to utilize machine learning and data-driven optimization techniques to formulate a comprehensive framework for the sustainable allocation and design of business service spaces within communities. Firstly, we conduct a comprehensive investigation, which includes data collected by applying questionnaire surveys and field research, to assess and model the factors influencing the spatial layout of commercial services on university campuses. Secondly, the AEL machine learning model is constructed by combining the analytic hierarchy process to determine subjective weights, the entropy weight method to calculate objective weights, and the Lagrange algorithm to determine comprehensive weights. Thirdly, we assess and improve the layout of commercial service spaces. Then, by training and testing the Neural Network Model, we apply cases to ensure the accuracy of the machine learning calculation results. Qualitative analysis elucidates the varying factors influencing the sustainable layout of different commercial spaces. Quantitative analysis indicates that, within university campuses, the distance between commercial service spaces and residence halls is a crucial factor in fostering a more sustainable layout. Other significant factors include their location along major student routes and proximity to teaching areas. This study makes contributions not only to the specific field of optimizing commercial service space in communities but also to the broader discourse on sustainable urban development. It advances our understanding of the complex dynamics involved in crafting urban environments that are both efficient and environmentally friendly. Beyond theoretical considerations, the study provides practical solutions and recommendations applicable to implementing tangible improvements in resource allocation. These contributions aim to foster urban environments that are not only environmentally conscious but also economically viable. [ABSTRACT FROM AUTHOR]

Building design optimization is a highly complex problem, requiring long computational running processes because of the many options that exist when a building is being designed. This paper introduces an integrated approach through which to perform this optimization within an acceptable time frame. The approach includes the methods of variable selection, model simplification, and a sequential optimization process. Using singular value decomposition, a large number of design variables is reduced to a smaller subset that can be solved more quickly through the optimization algorithm. To expedite the variable selection process, a modeling approach that quickly simulates annual energy consumption was developed to replace full annual energy simulations. The developed methodology was applied to two residential buildings in the US, and the results are discussed herein. To assess the accuracy of the integrated optimization methodology, the optimized life cycle costs are compaa variables demonstrating the strongest contributions in the optimization study were identified. The proposed methodology significantly shortened the time requirements for the optimization processes of the two case studies by 74% and 84%; the optimized life cycle costs were within 0.05% and 0.06%, respectively, of the optimum point. [ABSTRACT FROM AUTHOR]

Productive facades, consisting of photovoltaic shading and vertical farming systems, have been proposed as a means to improve the thermal and visual status of residential buildings while also maintaining energy performance and providing vegetables. However, how to quickly and accurately predict electricity and vegetable output during the numerous influencing architectural and environmental factors is one of the key issues in the early stages of design, and few studies have investigated the impact of such structures on both indoor environmental qualities and production performance. In this paper, we present a novel prediction method that uses experimental data to train and test an artificial neural network (ANN). The results indicated that using the Bipolar Sigmoid activation function to process the experimental data input to the artificial neuron network gives more accurate predicted results both in the yield of photovoltaic shading and vertical farming systems. In addition, this prediction method was applied to a typical high-rise residential building in Singapore to assess the self-sufficiency potential of high-rise residential buildings integrated with productive facades. The results indicated that the upper part of the building can meet 20.0–23.1% of the annual household electricity demand of a family of four in a four-room residential unit in Singapore and almost the entire year's vegetable demand, while the middle part can meet 18.4–21.2% and 89.1%, respectively. The results demonstrated the importance of a productive facade in reducing energy demand, enhancing food security, and improving indoor visual and thermal comfort. [ABSTRACT FROM AUTHOR]

The climate change–built environment nexus is complex and intertwined. Recognizing the rising air temperatures and solar radiations owing to climate-induced global warming, it is critical to manage the increased building energy and cooling loads in the Middle East Gulf states' hot desert climates (Bwh). One of the top climate priorities is to promote climate resilience by reducing risks and enhancing adaptation options. This study aims to systematically review the existing literature to document building energy performances in and the associated adaptation measures of the Middle East Gulf states, regarding the implications of climate change. It is accomplished by answering the following questions: 'How well do we understand the effects of climate change on building energy use in hot urban deserts?' and 'What are the most appropriate adaptation strategies to reduce energy use in hot urban deserts?'. Using the Preferred Reporting Items for Systematic review and Meta-Analysis protocols (PRISMA), 17 studies on the influence of present and future weather scenarios on building performance are examined, considering variations in typology, methods, and input variables. Finally, the paper identifies the preferred methods and input variables for modelling building energy performance under predicted climatic changes. Passive design considerations are considered highly effective in mitigating and adapting to climate change implications. Thermal insulation and efficient window glazing are identified as the best-performing strategies, while the use of solar Photovoltaic (PV) is considered efficient in meeting the primary energy demands. The study's findings can assist planners and designers in projecting future climatic influences on the energy usage of existing buildings. [ABSTRACT FROM AUTHOR]

An analytical hierarchy model of the impact of solar reflectance, thermal emittance, heat transfer coefficient, and heat storage coefficient on building energy consumption was established through the implementation of orthogonal design experiments. The EnergyPlus software (v9.0.1) was utilized to simulate building energy consumption across diverse climatic regions in China, providing essential benchmarks for the orthogonal design. The results of the range analysis consistently indicate that, barring regions characterized by extremely cold climates, solar reflectance emerges as the predominant factor exerting an influence on building energy consumption. As geographical latitude increases, the impact of the heat transfer coefficient becomes progressively larger, while the weight of thermal reflectance concurrently diminishes. Drawing upon the principles rooted in the gradient refractive rate theory and the concept of atmospheric window radiation, a range of high-reflectance and high-emittance cool roof coatings in various colors were meticulously developed. A spectrophotometer was employed to precisely quantify their reflectance properties, and simulations were subsequently conducted to scrutinize their energy-saving characteristics. The results demonstrate that the cool roof coatings that were developed using the methodology described in this paper exhibit substantial enhancements in reflectance, with increases of 0.24, 0.25, 0.37, and 0.35 for the yellow, red, blue, and green cool roofing materials, respectively, in comparison to conventional colored coatings. Under typical summer conditions, these enhancements translate to significant reductions in roof temperatures, ranging from 9.4 °C to 14.0 °C. Moreover, the simulations exploring the cooling loads for the roofs of differing colors consistently revealed remarkable energy savings. These savings were quantified to be 4.1%, 3.9%, 5.5%, and 5.4%, respectively, when compared to conventional coatings of the corresponding colors. These findings offer valuable insights into strategies for optimizing the energy efficiency of buildings through the application of high-reflectance cool roofing materials. [ABSTRACT FROM AUTHOR]

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

Buildings account for a significant portion of energy consumption and greenhouse gas emissions, underscoring the urgent need for energy refurbishment and green procurement strategies. This study explores the potential of Living Labs, a collaborative and user-centered approach, to bridge knowledge gaps and foster innovation in these areas. The research employed a comprehensive methodology, including stakeholder surveys and statistical analyses, to evaluate knowledge enhancement in various domains such as green policies, mentoring, funding possibilities, and green public procurement. The results demonstrated statistically significant improvements in knowledge across different fields and stakeholder groups. This study contributes to the objectives of the European Green Deal by highlighting the role of Living Labs in advancing knowledge critical to achieving energy efficiency and sustainability targets. Based on these findings, several recommendations are proposed to further leverage the effectiveness of Living Labs in promoting the energy refurbishment of buildings and green public procurement. [ABSTRACT FROM AUTHOR]

The construction industry is a key driver of environmental change due to its extensive use of resources and high emissions, thus significantly burdening global efforts towards sustainable development targets. A large portion of the environmental footprint of buildings results from the energy required to sustain indoor comfort levels. Thus, enhancing the energy efficiency of existing buildings becomes critical in reducing their environmental impact. This study explores the impact of thermal performance improvements on the heating demand, employing numerical modeling and two energy performance methodologies, PHPP and Mc001-2022, across various climatic datasets and case studies in Romania. The results show substantial variability in heating demand predictions: Mc001-2022 predicts up to 27.2% higher continuous heating demands and 21.0% higher intermittent demands compared to PHPP in one case study. In the second case study, the differences range from 8.1% higher to 6.9% lower for continuous heating and from 3.3% higher to 9.9% lower for intermittent heating, depending on the scenario. These findings underscore the importance of the methodological choice and localized climatic data in heating demand assessments, highlighting the need for a tailored, context-specific approach to energy performance assessment, integrating multiple energy efficiency measures suited to the unique characteristics of each building. [ABSTRACT FROM AUTHOR]

Enhancing the energy efficiency of building envelopes is one of the key strategies for energy conservation and reducing consumption in buildings. This study employs numerical research methods to explore the impact of crucial factors such as solar cell coverage, air channel height, indoor relative humidity, and indoor wind speed on the power generation performance and thermal comfort of a photovoltaic (PV)—Trombe wall. The dynamic changes in optical and thermal performance and energy efficiency matching mechanisms of this system are also discussed in hot summer and warm winter regions. The research findings indicate that the periods of good thermal comfort and power generation efficiency for humans are from 9:00 to 17:00 in winter. In summer, these periods are from 5:00 to 8:00 as well as from 17:00 to 20:00. When the system height is 2 m, the electricity price for power supplied by the PV—Trombe wall system is 25% lower than the residential price, with an annual energy generation of 322.5 kWh/m2 of solar panel, which can save USD 6.35 in costs. Moreover, an experiment is conducted to investigate the thermoelectric correlation by constructing a traditional Trombe wall and an external PV—Trombe wall. When the coverage reached 52.08%, the overall system efficiency was maximized. At a coverage of 78.12%, the system's thermal efficiency was at its lowest, while the maximum power generation was 510.3 W. It can be seen that the PV—Trombe wall possesses good economic benefits and energy saving as well as emission reduction potential in hot summers and warm winters regions, and the smooth implementation of related works will effectively promote its applications and promotions. [ABSTRACT FROM AUTHOR]