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By employing historical case study methodology, this paper examines the transition towards renewable energy and increased energy efficiency in the Swedish pulp and paper industry (PPI) during the 1970s and 1980s. Between 1973 and 1990, CO emissions were cut by 80 % in this sector, and this was mainly achieved by substituting away from oil to biofuels in the form of by-products from the pulp manufacturing process. The CO reduction was also a result of energy efficiency improvements and increased internal production of electricity through back-pressure turbine power generation. Sweden was highly dependent on oil at the advent of the first Oil Crisis in 1973, and the increased oil prices put pressure on the Swedish government and the energy-intensive PPI to reduce this oil dependency. Of central importance for the energy transition was the highly collaborative strategy of the PPI, both internally among pulp mills as well as between the sector as a whole and the corporatist Swedish state administration. The Swedish government chose a proactive strategy by emphasizing knowledge management and collaboration with the industry along with the substitution of internal biofuels for oil. The transition was also characterized by a strong focus on unutilized potentials in the PPI; a previous waste problem now could be transformed into energy savings and improved energy efficiency. Energy taxes and fees also played an important role in Swedish energy policy during the 1970s and the 1980s. All in all, the study illustrates the central role of governments and their ability to push industrial sectors into new technological pathways through a wide palette of mutually reinforcing policy instruments. The results further point at the importance of a more holistic understanding of the interplay between different policies and their impacts in the longer run. [ABSTRACT FROM AUTHOR]

Highlights • Definition of passive envelope indexes and caracterization of libraries in La Plata. • Environmental monitoring: achievement of preventive conservation indexes. • Correlation between passive envelope indexes and preventive conservation indexes. • Determination of the most influential passive envelope indexes for the achievement of preventive conservation indexes. Abstract The common approach for libraries to preserve their paper-based collections from irreversible deterioration is by controlling quantitative parameters, such as temperature and relative humidity. Variations of these parameters for preventive conservation can be expressed by the "performance index" (PI) and the "amplitude index" (AI). Building envelope is the first filter of the external climate, buffering the external weather fluctuations and maintaining the building's indoor climate stable. Its shape, window areas and envelope materials are the most influential parameters. As they capture the relationship of a few geometric and physical variables with the building's performance, the envelope characteristics can be expressed as certain passive envelope indexes. This paper studies the relationship of certain passive envelope indexes and the preventive conservation indexes for books for several libraries and archives located in La Plata, Argentina. The results show the impact of every passive envelope index upon the preventive conservation indexes. The compactness, heaviness, adjacency to spaces and adjacency to ground have a positive linear correlation meanwhile transparency and global transmittance are parameters with a negative correlation. In general, the most influential parameter is the relative area adjacent to other spaces, followed by global transmittance, transparency, heaviness, relative area adjacent to ground and compactness. [ABSTRACT FROM AUTHOR]

Much research has been focused on maintaining stable humidity conditions in buildings housing heritage collections while reducing energy use. Moisture buffering by collections themselves can have a marked effect on the stabilisation of relative humidity (RH), the key parameter for preservation. Modelling of moisture transport using COMSOL Multiphysics was applied to transform three-dimensional paper and wooden objects into their one-dimensional representations, without changing the moisture uptake and release characteristics. The results were coupled to the modelling of indoor microclimate and energy consumption in collection storage spaces with the use of WUFI ® Plus software. The study revealed the crucial impact of air exchange rate of the building on the stability of indoor RH and the humidification and dehumidification loads required to maintain it. In the adequately air-tight library store, a sizeable paper collection was found to reduce the RH fluctuations from ±9% to ±6% around the yearly average and the energy consumption due to the humidification and dehumidification load by 38% when compared with the empty space, for a high-quality climate control scenario. In turn, a wooden collection, occupying a realistic fraction of a museum store was not large enough to significantly narrow down the RH variations and reduce the energy consumption. [ABSTRACT FROM AUTHOR]

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines. [ABSTRACT FROM AUTHOR]

Daytime subambient radiative cooling is a most promising alternative to electricity-free building cooling. However, optical performance degradation arising from surface contamination and inevitable parasitic heat gain still pose unprecedented challenges to cool building at subambient temperatures. This paper proposed a superhydrophobic cellulose aerogel cooler (SHB-CAC) as building envelope by integrating self-cleaning capacity, passive daytime radiative cooling and thermal insulation to reduce environmental heat gain. The SHB-CAC demonstrates high solar reflectance (93%) and long-wave infrared emittance (91%), accomplishing a temperature drop of 8.5 °C lower than the ambient under sunlight of 800 W/m2 in the outdoor experiment. Notably, the SHB-CAC possesses low thermal conductivity (28 mW/(m∙K)) that suppresses parasitic heat gain from warmer surrounding and reduces cooling energy consumption. The self-cleaning property resulting from superhydrophobicity protects SHB-CAC from water wetting and dust contamination but also maintains its good surface radiation capacities under different humidity environment. A building energy simulation was conducted and results showed that 43.4% of cooling energy on average could be saved compared to the building baseline consumption, if SHB-CAC was widely used in China, indicating that the strategy of optical and thermal conductivity management of cooler has the potential to reduce the energy consumption of buildings. [Display omitted] • Waste paper is converted into aerogel cooler for building cooling. • High R solar and ε IR , thermal insulation, and self-clean are achieved in one design. • The aerogel achieves a high sub-ambient temperature drop of ∼8.5 °C on daytime. • The aerogel shows that 43.4% of cooling energy could be saved. [ABSTRACT FROM AUTHOR]

In recent years, the integration of digital technologies has grown rapidly in the field of thermal comfort and energy efficiency for buildings. The concept of a digital twin, incorporating multiple digital technologies, has gained increasing attention. The literature lacks a review of the digital twin concept in thermal comfort and energy consumption for existing buildings. This paper conducts a review of the current state-of-the-art in digital twin (DT) technology for thermal comfort/ energy consumption in buildings. The review employs a scientometric approach and examines various technologies used in creating DTs and a systematic analysis of the methods, technologies, algorithms, and approaches used in digital twin experiments. The results show a growing number of studies in this area, with a focus on thermal comfort monitoring, visualization, tracking, energy management, prediction, and optimization for existing buildings. Furthermore, the prediction of energy consumption using algorithms such as Artificial Neural Networks (ANN), Artificial Intelligence (AI), deep neural networks, and YOLOv4 have been used in buildings. However, the wider adoption of a DT that can facilitate occupants, and thermal sensations, enhance human-centered solutions, and improve energy prediction levels are necessary. There is a need for further international collaboration to expand the studies on digital twins for thermal comfort and energy efficiency. The review highlights the limitations and areas of improvement, such as the limited adoption of sensors for environmental measures, the need for more focus on the subjective perception of occupants, and the need for more comparative studies of algorithms for predicting energy consumption. Further studies can be conducted in areas such as understanding occupant psychological responses/behaviors to comfort in the digital world. This will enhance a more consolidated and robust validation for building performance. [ABSTRACT FROM AUTHOR]

Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively). [ABSTRACT FROM AUTHOR]

Abstract: Buildings consume a vast amount of energy during the life cycle stages of construction, use and demolition. Total life cycle energy use in a building consists of two components: embodied and operational energy. Embodied energy is expended in the processes of building material production, on-site delivery, construction, maintenance, renovation and final demolition. Operational energy is consumed in operating the buildings. Studies have revealed the growing significance of embodied energy inherent in buildings and have demonstrated its relationship to carbon emissions. Current interpretations of embodied energy are quite unclear and vary greatly, and embodied energy databases suffer from the problems of variation and incomparability. Parameters differ and cause significant variation in reported embodied energy figures. Studies either followed the international Life Cycle Assessment (LCA) standards or did not mention compliance with any standard. Literature states that the current LCA standards fail to provide complete guidance and do not address some important issues. It also recommends developing a set of standards to streamline the embodied energy calculation process. This paper discusses parameters causing problems in embodied energy data and identifies unresolved issues in current LCA standards. We also recommend an approach to derive guidelines that could be developed into a globally accepted protocol. [Copyright &y& Elsevier]

As socioeconomic development continues, the issue of building energy consumption has attracted significant attention, and improving the thermal insulation performance of buildings has become a crucial strategic measure. Simultaneously, the application of solid waste in insulation materials has also become a hot topic. This paper reviews the sources and classifications of solid waste, focusing on research progress in its application as insulation materials in the domains of daily life, agriculture, and industry. The research shows that incorporating household solid waste materials, such as waste glass, paper, and clothing scraps into cementitious thermal insulation can significantly reduce the thermal conductivity of the materials, leading to excellent thermal insulation properties. Insulation materials prepared from agricultural solid waste, such as barley straw, corn stalk, chicken feather, and date palm fibers, possess characteristics of lightweight and strong thermal insulation. Industrial solid waste, including waste tires, iron tailings, and coal bottom ash, can also be utilized in the preparation of insulation materials. These innovative applications not only have positive environmental significance by reducing waste emissions and resource consumption, but also provide efficient and sustainable insulation solutions for the construction industry. However, to further optimize the mix design and enhance the durability of insulation materials, continuous research is required to investigate the mechanisms through which solid waste impacts the performance of insulation materials. [ABSTRACT FROM AUTHOR]

Building water dissipation (BWD) refers to the change of water into vapour during water use activities in the building. The intention of the work described in this paper was to explore the BWD mechanism and establish a calculation model for BWD. The main research methods included experimental monitoring, a questionnaire survey and data mining. The results of this work showed that the capacity of buildings and the building area determine the type of water dissipation whereas the building population determines the BWD volume. The BWD calculation model was thus built based on two key parameters – building area and building population. Multi-variate regression fitting was used to analyse and study the other factors that influence BWD, such as water consumption, number of floors and so on. Using cluster analysis, 100 sample buildings were divided into different categories. Ordinary least squares regression was used to fit the multi-variate regression for the BWD of sample buildings. The results showed that the classification of buildings by cluster analysis can enhance the fitting effect of multi-variate regression of BWD. [ABSTRACT FROM AUTHOR]

Cooling system is in charge of a high percent of total energy consumption in buildings. Different cooling systems and devices have different effects on the cooling load and energy consumption. The main objective of this paper is to study the effects of floor and ceiling temperature in comparison with other parameters including location of air diffusers and application of insulation and sealant. On the other hand, hospitals are huge buildings with high energy requirements and systems compared to other buildings such as universities, schools and offices. In this paper the impacts of these different cooling parameters on the temperature and cooling load in a room of a hospital are studied using Design of Experiments (DOE) and the Taguchi method. The experiments are simulated in Energy Plus to achieve the comfort temperature in one hospital’s room in Iran during six months of spring and summer. Using different cooling systems such as chilled floor (during night to decrease the peak load during day) and ceiling in combination with air handling unit can provide people satisfaction while optimizing energy consumption. The results show that the location of inlet and outlet diffusers has the most effect while the chilled floor (when the other systems are off) has the less impact on reaching comfort temperature. The ranking of parameters and their interactions has been discussed through the paper. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the optimization capabilities of the salp swarm algorithm (SSA), a metaheuristic algorithm capable of addressing contemporary global challenges. The paper focuses on assessing SSA as an optimizer and observing its impact as a predictor in an example energy problem to gauge its predictive power. Salp swarm algorithm (SSA) distinguishes itself with its optimization capabilities, providing effective solutions to optimization problems. The quality, competitiveness, and efficiency of the algorithm were initially assessed using the CEC 2019 and CEC 2020 function sets. The results demonstrated that SSA is a competitive, effective, and up-to-date algorithm. This competitive nature suggests that SSA can be effectively employed across a wide range of problems. Therefore, the paper aims to evaluate its success in providing solutions to an energy prediction problem. In addressing the challenge of effective energy utilization, the accurate prediction of heat loading (HL) and cool loading (CL) factors, critical in building design, contributes significantly to the solution. In solving this problem, machine learning algorithms, specifically the multi-layer perceptron (MLP) as an artificial neural network architecture, were chosen. SSA was approached in a supervised manner, and a comparison with alternative metaheuristic algorithms was conducted. The obtained results indicate that the SSA-based MLP architecture (SSA-MLP) exhibits effective predictive capabilities in energy problems. By combining the optimization power of SSA and the learning capabilities of MLP, a robust solution with a competitive advantage in energy efficiency is presented. [ABSTRACT FROM AUTHOR]

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energy-saving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving schemes, which are used as the input of the model and the building energy consumption as the output of the model, so as to establish the building energy consumption analysis model based on BP neural network. However, in practical application, BP neural network has the defects of slow convergence and easy to fall into local minima. In view of this, this paper uses genetic algorithm to optimize the weight and threshold of BP neural network, completes the improvement of various building energy consumption analysis models, and realizes the qualitative analysis of building energy consumption. The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm (GABP) in this paper is relatively high, which is more accurate than the results of the traditional BP neural network model, and the relative error of the analysis model is reduced from 11.56% to 8.13%, which proves that the GABP can be better suitable for the study of school building energy consumption analysis model, It is applied to the prediction of building energy consumption, which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University. [ABSTRACT FROM AUTHOR]

Spatial resources and environmental problems caused by population growth are increasingly becoming the focus of global concern. The environmental sustainability of building products has become the research frontier of the industry. Previous research has proved that 25% of energy consumption comes from daily use, and realizing low-energy design based on the whole life cycle is of great significance to promoting the transformation of the building industry. This paper focuses on the wasteful energy consumption problem caused by the changes in residents' behavioral requirements due to the changes in family structure. Based on the family life cycle analysis, this paper explores the mechanism of residents' behavioral requirements and spatial function changes at each stage to clarify the relationship between residents' behavior and building energy consumption. Then, by controlling factors, including Light Correction Coefficient, Effective Daylight Area, window-to-ground ratio, and window-to-wall ratio, and applying the passive design methodology, this paper establishes the zero-energy home opening design guidelines based on the family life cycle. This research guides designers in the design of zero-energy house openings and, at the same time, provides new perspectives for related research in the field of building energy consumption, which helps to promote the sustainable development of the field of architectural design. [ABSTRACT FROM AUTHOR]

The demand for low-carbon and energy-efficient building designs is urgent, especially considering that building energy consumption constitutes a significant part of global energy usage. Unlike small to medium-sized buildings such as residential and office spaces, large public buildings, like sports facilities, have unique usage patterns and architectural forms, offering more significant potential for energy-saving strategies. This review focuses on sports buildings, selecting 62 high-quality papers published in building science over the past 30 years that investigate low-carbon and energy-efficient research. Summarizing and synthesizing these papers reveals that current studies predominantly concentrate on four main areas: indoor air quality, ventilation, thermal environment, and energy consumption. Notably, many studies emphasize improving indoor thermal comfort and reducing energy consumption in sports buildings through measurements and evaluations of indoor thermal environments, temperature distributions, heat transfer phenomena, and energy consumption analyses. Key outcomes indicate that green technology innovations, such as energy substitution technologies, significantly enhance energy efficiency and reduce CO2 emissions. However, present research emphasizes singular energy-saving approaches, suggesting future directions could integrate comprehensive green technologies, life-cycle assessments, and applications of intelligent technologies and the Internet of Things (IoT). These enhancements aim to provide more effective and sustainable solutions for implementing green, low-carbon energy practices in sports buildings. The review emphasizes that in order to accomplish sustainable urban growth and achieve global carbon neutrality targets, a comprehensive approach involving technical innovation, legislative assistance, and extensive preparation is crucial. [ABSTRACT FROM AUTHOR]

Abstract: Construction of sustainable, energy efficient buildings, infrastructure and transportation systems is a worthwhile and necessary goal to address critical worldwide environmental and energy concerns. However, as societies move toward this goal, it is the position of the International Forum of Fire Research Directors that the safety of the public and the protection of property against losses due to fire should not, and need not, be compromised in its pursuit. Fire safety consideration should form an unrenouncable part of any new product or technology developed to promote sustainable construction, and acceptable solutions to acute fire safety concerns must not pose a threat to the long term health of workers, fire fighters or the general public. This paper concentrates primarily on fire safety performance of new products, materials and technologies associated with the move toward sustainable communities, while recognizing that there are also issues confronting the fire safe design of buildings and infrastructure with features such as natural daylighting, natural ventilation, “smart facade” technologies, intelligent building controls, and water reduction strategies. [Copyright &y& Elsevier]

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]

This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in residential buildings. The proposed FRBPSs are characterized by a genetically optimized accuracy–interpretability trade-off. The trade-off optimization is carried out by means of multi-objective evolutionary optimization algorithms—in particular, by our generalization of the well-known strength Pareto evolutionary algorithm 2 (SPEA2). The proposed FRBPSs' designing procedure is our original extension and generalization (for regression problems operating on continuous outputs) of an approach to designing fuzzy rule-based classifiers (FRBCs) we developed earlier and published in 2020 in this journal. FRBCs operate on discrete outputs, i.e., class labels. The experimental contribution of this work includes designing the collection of FRBPSs for residential building energy consumption prediction using the data set published in 2024 and available from Kaggle Database Repository. Moreover, the comparison with 20 available alternative approaches is carried out, demonstrating that our approach significantly outperforms alternative methods in terms of interpretability and transparency of the energy consumption predictions made while remaining comparable or slightly superior in terms of the accuracy of those predictions. [ABSTRACT FROM AUTHOR]

To effectively reduce building energy consumption, a novel full fresh air system with a heat source tower (HST) and a borehole heat exchanger (BHE) was proposed for space cooling and dehumidification in this paper. The cooling system only adopts geothermal energy to produce dry and cold fresh air for space cooling and dehumidification through the BHE and HST, which has the advantage of non-condensate water compared to BHE systems integrated with a fan coil or chilled beam. Based on the established mathematical model of the cooling system, this paper analyzed the system characteristics, feasibility, operation strategy, energy performance, and cost-effectiveness of the proposed model in detail. The results show that the mathematical model has less than 10% error in estimating the system performance compared to the practical HST–BHE experimental set up. Under the specific boundary conditions, the cooling and dehumidification capacity of this system increases with the decrease in the air temperature, air moisture content, and inlet water temperature of the HST. The optimal cooling capacity and the system COP can be achieved when the air–water flow ratio is at 4:3. A case study was conducted in a residential building in Shenyang with an area of about 1800 m2. It was found that this system can fully meet the cooling and dehumidification demand in such a residential building. The operation strategy of the cooling system can be optimized by adjusting the air–water flow ratio from 4:3 to 3:2 during the early cooling season (7 June–1 July) and end cooling season (3 August–1 September). As a result, the average COP of the cooling system during the whole cooling season can be improved from 6.1 to 8.7. Compared with the air source heat pump (ASHP) and the ground source heat pump (GSHP) for space cooling, the proposed cooling system can achieve an energy saving rate of 123% and 26%, respectively. Considering that the BHE of the GSHP can be part of the proposed HST–BHE cooling system, the integration of the HST and GHSP for space cooling (and heating) is strongly recommended in actual applications. [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]

A critical issue in intelligent building control is detecting energy consumption anomalies based on intelligent device status data. The building field is plagued by energy consumption anomalies caused by a number of factors, many of which are associated with one another in apparent temporal relationships. For the detection of abnormalities, most traditional detection methods rely solely on a single variable of energy consumption data and its time series changes. Therefore, they are unable to examine the correlation between the multiple characteristic factors that affect energy consumption anomalies and their relationship in time. The outcomes of anomaly detection are one-sided. To address the above problems, this paper proposes an anomaly detection method based on multivariate time series. Firstly, in order to extract the correlation between different feature variables affecting energy consumption, this paper introduces a graph convolutional network to build an anomaly detection framework. Secondly, as different feature variables have different influences on each other, the framework is enhanced by a graph attention mechanism so that time series features with higher influence on energy consumption are given more attention weights, resulting in better anomaly detection of building energy consumption. Finally, the effectiveness of this paper's method and existing methods for detecting energy consumption anomalies in smart buildings are compared using standard data sets. The experimental results show that the model has better detection accuracy. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including energy consumption of buildings, built environment and heating and ventilation industry.

The purpose of this study is to provide a holistic review of two decades of research advancement in the indoor environmental quality modelling and indexing field (IEQMI) using bibliometric analysis methods. The explicit objectives of the present study are: (1) identifying researchers, institutions, countries (territories), and journals with the most influence in the IEQMI topic; (2) investigating the hot topics in the IEQMI field; and (3) thematically analysing the keyword evolution in the IEQMI field. A scientometric review was conducted using the bibliometric data of 456 IEQMI research articles published in the past two decades. VOSviewer software was employed for bibliometric analysis, and the SciMAT tool was used to investigate the keywords' thematic evolution in three sub-periods (2004–2009; 2010–2015; 2016–2021). Results show that there is a continuous increment in the number of published papers in the field of IEQMI, and 60 out of 193 countries in the world have been involved in IEQMI studies. The IEQMI research mainly focuses on: (a) thermal comfort and energy efficiency; (b) occupant satisfaction and comfort; (c) IAQ and health issues; (d) methods and procedures. This field has undergone significant evolution. While 'indoor environmental quality was initially the only theme in the first period', 'occupant satisfaction', 'buildings', 'impact', 'building information modelling', and 'health' were added as the main thematic areas in the second period; 'occupant behaviour' and 'energy' were novel themes in IEQMI studies receiving much attention in the third period. [ABSTRACT FROM AUTHOR]

Countless years have been spent researching the strategies necessary for improving the energy consumption of buildings globally. There have been numerous attempts at achieving both passive solutions and enhancing and optimising the existing active systems. This paper seeks to review, analyse and summarise the possibilities of using thermoelectricity in two different contexts to the integration with buildings, integrated thermoelectric systems, and non-integrated thermoelectric systems. The utilisation of thermoelectricity in cohorts with existing renewable technologies and the utilisation of thermoelectric systems that operate individually, both have the potential to provide the occupants of a building with conditions pertinent to thermal and visual comfort. The results in this paper are classified according to the integration types of thermoelectric systems within different parts of the fabric of a building while maintaining an active role in enhancing the building envelope and self-contained thermoelectric systems that sustain a passive role for the same. The introduction to this paper also gives a very broad and surface-level insight into categorisation of different kinds of thermoelectric systems that are being studied and researched across the world. [ABSTRACT FROM AUTHOR]

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Smart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings. [ABSTRACT FROM AUTHOR]

Approximately 40 % of the final energy is used for heating and cooling of buildings. Of this, as much as 75 % is used in residential buildings. The proportion of energy needed in buildings is relatively high, so it is necessary to start using technologies that have low energy consumption, use it efficiently or provide it from renewable sources. One such technology is thermal energy storage, which allows us to store excess energy for later when we need extra energy. The paper presents two experiments in the field of adsorption space heating. In the first experiment, measurements were carried out on the adsorbents - zeolite 13X and zeolite NaYBFK, which were placed into a duct, through which humid air was transported by means of a centrifugal fan. In the second experiment, water vapour as the working medium was used. With the first experiment we achieved better water uptake on NaYBF, while the second experiment shows the increase of water uptake of zeolite 13X. [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]

To accurately describe the thermal comfort of indoor occupant used to regulate heating, ventilation and air conditioning (HVAC) system is a key goal to reduce energy consumption in intelligent buildings. In this paper, we propose a noncontact measurement of occupant thermal discomfort behavior as an index of thermal comfort. The method takes the existing monitoring image data in the building as the input to infer the occupant thermal discomfort directly, which saves the cost because there is no need to install new sensors in the building and the occupant does not need to wear additional equipment. The framework combined three channels of body posture, motion and performance information to infer occupant thermal discomfort behavior, it consists of a human detection and crop module, a posture analysis module, an optical flow extraction module and a 3D convolutional neural network module. The three channels describe the actions from different perspectives, and the proposed method makes full use of the complementarity of the three modalities to identify the occupant's thermal discomfort behaviors. Sixteen postures related to thermal discomfort were identified through a questionnaire, and 14,800 video clips containing these postures were collected for experimental evaluation. The results demonstrate the superior performances of our approach to the state-of-the-art techniques. The framework achieves noninvasive, cost-effective thermal comfort evaluation, and has potential value in improving energy efficiency of HVAC systems. [ABSTRACT FROM AUTHOR]

The pursuit of climate neutrality requires global systemic actions involving the use of solutions aimed at reducing emissions. Changes must be introduced in all sectors affecting climate change, namely power engineering and district heating, construction, transport, and industry, as well as agriculture and forestry. Analyzing the structure of final energy consumption in the EU by sector, it can be stated that households account for 27% of the total energy consumption. Comprehensive actions are needed to increase the energy efficiency of buildings. The aim of this paper was to indicate aspects of improving energy efficiency in buildings and their equipment, taking into account the striving for climate neutrality. Analyzed possibilities and conditions of using various solutions of energy-efficient systems aimed at increasing energy resilience and security and preventing environmental degradation. Particular attention was paid to construction and material solutions, as well as installation solutions, which increased the accumulation and energy efficiency of the building. These activities are closely related to the conditions and dynamics of the heat exchange process in the applied solutions and are also related to the factors influencing thermal comfort and energy consumption in buildings. Due to the growing popularity of modern information technologies and artificial intelligence in energy management in recent years, this article reviews the latest research in this area. One of the directions of future research indicated by scientists is autonomous building control in real time, adapting to the momentary needs of users. The analysis of the possibilities of using modern energy efficiency solutions in buildings conducted in this work may be useful for optimizing heat and energy management models and models of society's consumption as an element of energy transformation towards climate neutrality and counteracting the deepening of energy poverty. [ABSTRACT FROM AUTHOR]

Educational buildings have a large share and impact on urban development. While research shows a significant portion of non-industrial energy consumption in these buildings, obtaining optimal thermal comfort in educational buildings remains one of the main concerns in achieving the grounds to promote students' best performance and efficiency. Extensive research has been done in this field, however, this research presents a new approach to the diverse use of nanotechnology techniques which improve its properties and components in the buildings, aiming to reduce energy consumption and increase thermal comfort. In this paper, thermal comfort and energy consumption are evaluated in a 12-class elementary school located in Shiraz City. Aeropan and nano-Phase change materials (nano-PCMs) is used in the window glass and walls of the studied case. This evaluation presents the simulation and experimental analysis of thermal comfort (PMV) and energy consumption of three classroom alignments in the school building including the Linear-shape (LS), the Integrated Linear-shape (ILS), and the U-shaped (US) alignment. The simulation was performed using EnergyPlus 9.6 software, while the experimental data was collected using TESTO 425 device. The result of this research shows that after applying nano-PCM and Aeropan techniques in window glass and walls, the US alignment has the highest reduction in energy consumption (monthly average of 11.80%) compared to LS and ILS alignments. This alignment includes an energy consumption reduction of 12.03% in the coldest, and, 11.66% in the hottest day of the year in addition to increasing the monthly average thermal comfort of school by the use of nanomaterials. [ABSTRACT FROM AUTHOR]

The energy consumption of buildings presents a significant concern, which has led to a demand for materials with better thermal performance. Thermal conductivity (TC), among the most relevant thermal properties, is essential to address this demand. This study introduces a methodology integrating a Multilayer Perceptron (MLP) and a Generative Adversarial Network (GAN) to predict the TC of concrete based on its mass composition and density. Three scenarios using experimental data from published papers and synthetic data are compared and reveal the model's outstanding performance across training, validation, and test datasets. Notably, the MLP trained on the GAN-augmented dataset outperforms the one with the real dataset, demonstrating remarkable consistency between the model's predictions and the actual values. Achieving an RMSE of 0.0244 and an R2 of 0.9975, these outcomes can offer precise quantitative information and advance energy-efficient materials. [ABSTRACT FROM AUTHOR]

This paper addresses the research gap concerning the impact of thermal diffusivity on cooling energy consumption in buildings, specifically in the context of Saudi Arabia's hot climate. The objective is to determine the optimal thermal diffusivity of building walls for different climate conditions in Saudi Arabia to minimize the cooling energy consumption of residential buildings. The study analyzes four locations in Saudi Arabia, namely, Riyadh, Jeddah, Abha, and Tabuk, to understand the correlation between optimal thermal diffusivity and cooling degree days. The study includes numerical modeling using the finite-difference method and developing a calibrated building energy model of an existing residential building. Sensitivity analysis is conducted to evaluate the influence of thermal conductivity and heat capacity on cooling energy consumption. Correlation analysis is performed to assess the relationship between optimal thermal diffusivity and cooling degree days. The results highlight the importance of finding a balance between thermal insulation and thermal mass for energy-efficient buildings, considering the unique climate conditions in Saudi Arabia. Finally, this study considered the development of the Saudi Energy Conservation Code (SBC-602), which provides guidelines and regulations for energy-efficient building design in Saudi Arabia by recommending the selection criteria for the optimal thermal properties of building walls while considering the outdoor climate conditions. By aligning the findings of this study with the requirements and recommendations of the SBC-602, the study aims to contribute to the effective implementation of energy-efficient strategies in building design. [ABSTRACT FROM AUTHOR]

Energy-efficient materials are essential in buildings to reduce energy consumption, lower greenhouse gas emissions, and enhance indoor comfort. These materials help address the increasing energy demand and environmental impact of traditional construction methods. This paper presents a comprehensive literature review that explores advanced materials and technologies for improving building energy efficiency, sustainability, and occupant comfort. The study applies a comparative analysis of peer-reviewed research to examine key technologies analyzed include building-integrated photovoltaics, advanced insulating materials, reflective and thermal coatings, glazing systems, phase-change materials, and green roofs and walls. The study highlights the significant energy savings, thermal performance, and environmental benefits of these materials. By integrating these technologies, buildings can achieve enhanced energy efficiency, reduced carbon footprints, and improved indoor comfort. The findings underscore the potential of advanced building materials in fostering sustainable construction practices. The methodology of this review involves collecting, analyzing, summarizing, comparing and synthesizing existing research to draw conclusions on the performance and efficiency of these technologies. [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 increasing population aging in China has led to a growing demand for nursing homes. The indoor thermal comfort of nursing homes affects the occupants' quality of life, building energy consumption, and carbon emissions. This study used thermal comfort questionnaires, environment tests, and physiological parameter tests to conduct a field survey of 954 occupants (including the elderly and the adult staff) in nursing homes in Shandong Province, China, and analyzed the thermal comfort of occupants. Results showed that in Shandong Province, there was a significant difference in thermal sensation between the elderly and adults under the same conditions. The neutral temperatures for the elderly and adults were 21.7 and 20.5 °C, the comfort temperature ranges were 19.4–24.0 °C and 18.6–22.5 °C, and the preferred temperatures were 23.8 and 23.1 °C, respectively. The elderly prefer higher temperatures than adults. Personal clothing insulation was significantly negatively correlated with operative temperature. Occupants' average skin temperature was significantly positively correlated with operative temperature and mean thermal sensation votes. Based on the simulation results of building energy consumption and carbon emissions, this paper proposes design strategies for nursing homes that balance thermal comfort and energy savings. [ABSTRACT FROM AUTHOR]

As a major contributor to global energy consumption and greenhouse gas emissions, the building sector is crucial in shaping energy and climate change policies. Understanding building energy consumption is essential for developing effective policies, and comprehensive datasets and analyses are increasingly important. This paper outlines a structured methodology for developing a sustainability assessment index for building energy efficiency retrofits throughout a building's life cycle, covering the design, construction, use, and out-of-use phases. It highlights the interdependencies among these phases, with the design plan influencing energy efficiency and material selection, the construction plan ensuring these goals are met, and the plans for energy management, demolition, and resource recovery focusing on sustainable practices. The keys to energy-efficient retrofits are sustainable materials, energy-efficient equipment, and green technologies, which help reduce energy consumption, emissions, and operating costs. Oversight and regulation are necessary to maintain standards. This research combines a literature review, surveys, interviews, the Delphi method, and an analytic hierarchy process (AHP) to develop a comprehensive evaluation system, categorizing 20 factors across a building's life cycle and assigning weights based on environmental, economic, and social dimensions. The system provides a scientific basis for assessing the sustainability of energy efficiency programs, validated through consistency testing. [ABSTRACT FROM AUTHOR]

In response to increasing global temperatures and energy demands, optimizing buildings' energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R2 of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates. [ABSTRACT FROM AUTHOR]

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

The thermal performance of windows is an important area of research to reduce the energy consumption of buildings and improve indoor comfort. The application of innovative glazing technologies can improve the energy performance of windows and transparent facades, resulting in significant energy savings. This paper presents research results on the energy performance of electrically heated windows. A comprehensive CFD and experimental analysis of the heat transfer processes in a window space depending on the size, power, and location of an electric heater was performed. The convective gas flows in the gas gaps and in the boundary layer were also analysed, and it is shown that a window with an electric heater can reduce the energy consumption of a room by 10–12%. This study is a pilot study to assess the feasibility and cost-effectiveness of electric local heating of a window or facade to minimise heat loss before full-scale implementation. The results of numerical modelling and experimental studies confirm the potential of the new technologies. [ABSTRACT FROM AUTHOR]

The energy challenges of overcoming climate change and economic and global political crises require not only the increased integration of renewable energies but also an optimisation of energy generation and use and, as a result, a reduction in energy consumption in various sectors. Thermal energy consumption in buildings in particular accounts for a significant proportion of final energy consumption. With respect to commercial buildings, a central problem in optimising the system settings is the lack of or only limited information about the actual room temperatures as well as the comfort requirements and temperature perception of the users in the rooms on the one hand and the operational management and settings specified by the facility management, for example, the heating curves of the heating circuits, on the other. The aim here is to create a bidirectional flow of data and information so that the compromise between the necessary room temperatures and the comfort of the users and the most energy-efficient operational management possible can be achieved. In this context, the paper presents a developed and tested web platform that makes it possible to optimise the operation of the system technology, particularly the heating system in the building, from an energy point of view and to involve the building user (e.g., office employees) and to pass on information to the facility management, thus pursuing a holistic approach. In the associated Living Lab project (called ComfortLab), it was possible to obtain over 6500 votes on temperature perception and combine this with building operation and the parameters relevant to facility management. This made it possible to bridge the gap between user requirements and room temperatures on the one hand and energy consumption and the inlet temperature of the heating system and supply circuits on the other. The use of the platform makes it possible to optimise the setpoint specification, specifically the inlet temperature of individual heating circuits, considering both regular building operation at times of presence and the setting of weekend and night setback times. The results show a diversified picture regarding temperature perception and possible room temperature reductions of several degrees Celsius and energy savings in the double-digit percentage range. [ABSTRACT FROM AUTHOR]

The growth of electric vehicles (EVs) and their integration into existing and future buildings bring new considerations for energy efficiency (EE) and balance when combined with renewable energy. However, for buildings with an energy efficiency label, such as Near Zero Energy Building (NZEB) or Positive Energy Building (PEB), the introduction of EVs may result in the declassification of the EE label due to the additional energy required for the charging infrastructure. This underscores the increasing relevance of demand-side management techniques to effectively manage and utilize energy consumption and generation in buildings. This paper evaluates the influence of electric vehicle (EV) charging on NZEB/PEB-labeled buildings of the Brazilian Building Labeling Program (PBE Edifica). Utilizing on-site surveys, computational modeling, and thermos-energetic analysis with software tools such as OpenStudio v. 1.1.0 and EnergyPlus v. 9.4.0, an energy classification was conducted in a building in the city of Belem, State of Para, Brazil. Subsequently, power flow simulations employing probabilistic models and Monte Carlo approaches were executed in the OpenDSS software v. 10.0.0.2 to examine the impact of EV integration, both with and without the implementation of demand-side management techniques. Analyses using the labeling methodology demonstrated that the building has EE level C and NZEB self-sufficiency classification. The assessment of the impact of EV integration on the building's total energy consumption in the base (current) scenario was carried out in two scenarios, with (2) and without (1) supply management. Scenario 01 generated a 69.28% increase in energy consumption, reducing the EE level to D and resulting in the loss of the NZEB class. Scenario 02 resulted in a smaller increase in energy consumption of 40.50%, and guaranteed the return of the NZEB class lost in scenario 1, but it was not enough to return the EE level to class C. The results highlight the need for immediate and comprehensive energy management strategies, as the findings show that the two scenarios present a difference of 41.55% in energy consumption. Nonetheless, these strategies are not enough if other consumption restrictions or energy efficiency measures are not applied to other building systems. [ABSTRACT FROM AUTHOR]

Currently, the construction and operation of buildings are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the perspective of sustainable development, and taking into account economy and thermal comfort, it is crucial to consider the influence of multi-objective realization on design parameters. In this paper, high-rise residential buildings in the cities of Xi'an and Yulin, which have differences in solar radiation, in the western solar enrichment area of China are taken as the research objects. The four objectives of building energy consumption, thermal comfort, life-cycle cost, and life-cycle carbon emissions are weighed using the SPEA-2 algorithm by adjusting eleven design variables, thereby obtaining the Pareto non-dominated solutions. The TOPSIS method is applied to obtain the suitable parameter combinations under different scenarios. The results show that the differences in climate and solar radiation influence the solution distribution, the range of objective function values, and the values of the design variables in Pareto non-dominated solutions. The obtained optimal scheme for the Xi'an area has an energy-saving rate of 61.7%, a TDHP improvement rate of 20.3%, an LCC of 254.8 CNY/m2, and an LCCO2 of 72.3 kgCO2/m2. The corresponding values in the Yulin area are 69.7%, 19.4%, 230.2 CNY/m2, and 0 kgCO2/m2. This reflects the potential of solar energy utilization to reduce buildings' energy consumption and carbon emissions. The methodology and findings can provide references for high-rise residential building design in Northwestern China. [ABSTRACT FROM AUTHOR]

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

The purpose of this research is to compare clustering methods and pick up the optimal clustered approach for rural building energy consumption data. Research undertaken so far has mainly focused on solving specific issues when employing the clustered method. This paper concerns Yushan island resident's time-series electricity usage data as a database for analysis. Fourteen algorithms in five categories were used for cluster analysis of the basic data sets. The result shows that Km_Euclidean and Km_shape present better clustering effects and fitting performance on continuous data than other algorithms, with a high accuracy rate of 67.05% and 65.09%. Km_DTW is applicable to intermittent curves instead of continuous data with a low precision rate of 35.29% for line curves. The final conclusion indicates that the K-means algorithm with Euclidean distance calculation and the k-shape algorithm are the two best clustering algorithms for building time-series energy curves. The deep learning algorithm can not cluster time-series-building electricity usage data under default parameters in high precision. [ABSTRACT FROM AUTHOR]

Purpose: The Heating, Ventilation and Air Conditioning (HVAC) system is a significant energy consumer in built environments, and the building energy consumption could be minimised by optimising HVAC controls. Hence, this paper aims to investigate the applicability of Variable Refrigerant Flow (VRF) air conditioning systems for optimising the indoor comfort of buildings in Sri Lanka. Design/methodology/approach: To address the research aim, the quantitative approach following the survey research strategy was deployed. Data collected through questionnaires were analysed using descriptive statistical tools, including Mean Rating (MR), Relative Important Index (RII) and Standard Deviation (SD). Findings: The findings revealed that VRF systems are popularly used in Sri Lankan apartment buildings. Furthermore, energy efficiency and comfort were recognised as the most significant top-ranked benefits, while ventilation issues and initial cost were recognised as significant challenges. Moreover, the allocation of trained technicians and provision of proper ventilation through a Dedicated Outdoor Air System (DOAS) were highlighted as applicable mitigation strategies for the identified challenges in VRFs. Practical implications: The study recommends VRF systems as a suitable technology to ensure energy efficiency, reduce GHG emissions and achieve climate performance within the built environment. The opportunities for adopting VRF systems for developing countries could be explored based on the research findings. The identified challenges would assist the design engineers and facilities professionals to devise suitable strategies to mitigate issues of VRF systems in developing countries. Originality/value: This research provides empirical proof of the energy efficiency and comfort aspects of VRFs. The study has explored and recommended VRF technology as a beneficial application to overcome the persistent energy crisis in developing countries. [ABSTRACT FROM AUTHOR]

A large proportion of building energy consumption in tropical countries like Thailand primarily comes from air conditioning systems used to maintain the comfort level of building occupants. This paper aims to evaluate the performance of an alternative cooling system based on the evaporative principle in terms of thermal characteristics and energy consumption. A simulation model using computational fluid dynamics (CFD) software ANSYS version 16.0 and an actual experimental setup at the laboratory level were built to verify the results of the proposed cooling system. Additionally, factors that influence performance, such as components of the building envelope and the building's orientation, are considered. This research aims to demonstrate the impact of building envelope material and building characteristics on the energy usage in air conditioning systems and to propose an energy-efficient cooling system. The results demonstrate that the proposed cooling system can reduce the temperature inside the building. However, the characteristics of the building also affect the energy performance. Thus, the proposed cooling system, in combination with an efficient envelope material, can achieve energy savings of around 35–43%. Therefore, a combination of the proposed cooling system and optimal building design can ensure comfort for building occupants while saving energy. [ABSTRACT FROM AUTHOR]