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• A novel MOF-based paper sheet was developed for passive indoor moisture control. • The MOF paper sheet has excellent moisture buffer capacity and mechanical properties. • A numerical model was developed to find the optimal thickness of the MOF paper sheet. • The energy-saving potential of wallpaper made of MOF paper sheet was investigated. Passive indoor moisture control is a meaningful way to reduce HVAC operation and improve building energy efficiency. Metal-organic frameworks (MOFs) have been considered one of the most promising sorbent materials for passive indoor moisture control. However, the powder form of MOF is challenging to use in the built environment. Typical shaping methods like pellets, beads, and coating can lead to problems like partial pore blocking, insufficient mechanical stability, amorphization, and low material utilization. Here, we report a new sustainable MOF paper sheet comprising 25% cellulosic fibers and 75% MOF MIL-100(Fe) powder. The porous polymer matrix provides good support and fixation for MOF particles, so it's more applicable in the built environment. The crystalline and pore structure characteristics of the MOF in the membrane are well preserved. Hydro characteristic test results show that MIL-100(Fe) paper sheet has high water vapor uptake, S-shape isotherm, mild regeneration condition, and high MBV. Then, a numerical model was developed to calculate the relation between a material's MBV and its thickness. MIL-100(Fe) paper sheet with the most efficient thickness was applied as wallpaper in a building energy simulation model. The simulation result shows that the MIL-100(Fe) paper sheet is promising for passive indoor moisture control. It can remove about 45%-55% of the latent cooling load in most European climate types. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon neutrality feasibility of buildings was reviewed. • Carbon neutrality of buildings is feasible, but complexity varies. • Multi-building carbon neutrality case studies are limited. • Climate type affects neutrality; high-rise research is deficient. • Long-term lifecycle impacts of carbon measures remain unclear. Achieving carbon–neutral building stock by 2050 contributes to coping with the detrimental impacts of global warming since buildings account for almost 37% of final energy-related CO 2 emissions. This paper reviews the publications on carbon neutrality (CN) feasibility at both single and multi-building complex scales. Publications are retrieved from the Scopus database, and the snowball method is used to find the most relevant studies. The paper addresses the primary question: "Is it possible to reach the life cycle carbon neutrality of buildings?". Various information such as building life cycle carbon assessment, building characteristics (usage type and number of stories), climate type, mitigation measures, and simulation results are extracted, classified, and analyzed. Technically, reaching CN is feasible, but it is challenging given the need to implement multiple mitigation measures simultaneously, especially in the regions with low emissions intensity of the electricity grid, and may not always be economically feasible. A lack of successful case studies for large multi-building complexes, such as cities, is evident in the literature. It can be attributed to the limited availability of input data for carbon assessments as well as the complexity of simulations. Knowledge gaps in the literature and suggestions for future works are also discussed in detail. Due to the small number of studies on the topic, conclusive paths to reach the CN of buildings, specific to different climates and types of buildings, remain unclear, and thus, further research is necessary. [ABSTRACT FROM AUTHOR]

[Display omitted] • Self-shading in facade components is less studied compared to building form. • Solar radiation remains the predominant performance indicator in the studies. • Coupling Finite Element Method could improve building energy simulation results. • SPEA-2 is the most commonly used genetic algorithm in self-shading studies. • Opaque materials, such as metallic solids, are less studied in self-shading. Self-shading in facades can serve as a strategy to reduce heat gain and improve the energy efficiency of building envelopes. It leverages the form of the building envelope or its components to provide partial protection against solar radiation. Although self-shading strategies have been studied in various contexts, there still exists a lack of a comprehensive review of the current state of research in this field. This study addresses this gap by conducting a review of the literature related to self-shading facades. The scoping process included a total of 234 papers, out of which 38 relevant papers were selected for the review. These are discussed under the topics of self-shading morphology, materials, research methodology, climate and orientations, as well as performance indicators. The findings of the literature review show that self-shading façades reduce heat gain through various self-shading aspects, including morphology, shaded surface area, and construction materials. Opportunities for further exploration and improvement remain, particularly for self-shading of opaque building components. While there have been several studies on self-shading brick wall configuration, other common materials used in the building industry such as steel have received limited attention in research to date. Furthermore, there is still little consensus on best-performing angles, forms and orientations of self-shading facades, making it difficult to identify preferable shapes and orientations that can be applied in practice. This literature review also demonstrates the need for further advancements in simulation tools and more comprehensive methodologies to examine self-shading strategies. Addressing these gaps is crucial to improve building performance through self-shading in order to advance practical implementation. [ABSTRACT FROM AUTHOR]

• Innovative interpretable machine learning framework for machine learning control. • Shapley values and large language models are combined for improved interpretability. • Case study demonstrates interpretable control processes in demand response events. • Bridging trust gap in machine learning control usage for building energy management. The potential of Machine Learning Control (MLC) in HVAC systems is hindered by its opaque nature and inference mechanisms, which is challenging for users and modelers to fully comprehend, ultimately leading to a lack of trust in MLC-based decision-making. To address this challenge, this paper investigates and explores Interpretable Machine Learning (IML), a branch of Machine Learning (ML) that enhances transparency and understanding of models and their inferences, to improve the credibility of MLC and its industrial application in HVAC systems. Specifically, we developed an innovative framework that combines the principles of Shapley values and the in-context learning feature of Large Language Models (LLMs). While the Shapley values are instrumental in dissecting the contributions of various features in ML models, LLM provides an in-depth understanding of the non-data-driven or rule-based elements in MLC; combining them, LLM further packages these insights into a coherent, human-understandable narrative. The paper presents a case study to demonstrate the feasibility of the developed IML framework for model predictive control-based precooling under demand response events in a virtual testbed. The results indicate that the developed framework generates and explains the control signals in accordance with the rule-based rationale. [ABSTRACT FROM AUTHOR]

• Utilizes EnergyPlus software to calibrate an energy simulation model for hospital in India. • Calibration parameters are systematically identified and adjusted through an iterative process. • The study highlights the need for a nuanced approach in modeling healthcare facilities. • Findings aim to foster sustainable hospital infrastructure in warm-humid climates. This research paper addresses the calibration of an energy simulation model for a naturally ventilated hospital building located in Mangalore, Karnataka, India, in a warm, humid climatic zone. The study aims to enhance the accuracy of energy consumption calculations through a calibration process by developing the energy model of a reference hospital building using EnergyPlus software. The required architectural design data, construction details, occupancy and schedule data and services are collected through documents, energy audits and documentation, measured drawings, and semi-structured interviews. Calibration parameters are systematically identified and adjusted through an iterative process. The monthly electricity bill is used to validate the simulation model. The simulation model reached during the validation process has an excellent Coefficient of Variation-Root Mean Squared Error of 4.24% and a Normalized Mean Bias Error of −4.42%, both of which meet the ASHRAE-approved accuracy standards. The paper also discusses the challenges and emphasizes the need for a nuanced approach to modeling healthcare facilities. Notwithstanding the challenges faced, the study offers insightful information about how to calibrate simulation models for hospital energy usage with the significant influence of scheduling for artificial lighting and equipment usage. By pioneering a manual calibration approach tailored to hospital simulation models in the warm humid climate of India, this paper offers a novel and practical solution to address the challenges of energy performance analysis in resource-constrained environments. The calibrated simulation model presented in this study is a valuable tool for assessing and improving the energy performance of naturally ventilated healthcare facilities. By creating alternative space layouts, the research findings aim to foster the development of hospital infrastructure that is both ecologically friendly and energy-efficient in a specific climatic context. This research is assured to make a substantial contribution to the advancement of sustainable hospital design in warm-humid climates, with implications for both academia and industry. [ABSTRACT FROM AUTHOR]

The prediction of the occupancy in buildings is essential to design efficient energy control strategies that optimize consumption and reduce losses while guaranteeing the comfort of the occupants. For this reason, many works address the problem of detecting, estimating, and predicting buildings' occupancy using different techniques, devices, and technologies. The occupancy prediction process can be described in four stages: data acquisition, modeling, evaluation, and testing, which are closely related. This paper reviews the most relevant recent literature on building occupancy estimation and prediction, analyzing the key aspects of its stages. A detailed description of the variables and design considerations is presented, including measurement methods, sensor selection, modeling techniques, evaluation metrics, and different applications. Through its examination, this paper elaborates significant remarks on the interaction between the stages, providing an overview of the suitable design of the occupancy prediction process. Finally, current and future trends are discussed. • A systematic review of the occupancy estimation and prediction process is presented. • Data acquisition, modeling, evaluation, and testing are the four general stages. • The importance of sensor fusion in overcoming individual limitations is presented. • Occupancy detection methods include deterministic, stochastic, and machine learning. • Some potential future research directions are discussed. [ABSTRACT FROM AUTHOR]

• A method for estimating CO 2 generation rate by age is proposed. • Identified the trends and age segment points in CO 2 generation rates from ages 5 to 70. • Described the individual differences in CO 2 generation rates using body surface area. • Metabolic rate is the primary factor causing errors in CO 2 generation rate estimation. • ASTM D6245-18′s method requires higher metabolic rate input as compensation. In most applications of indoor CO 2 , such as building ventilation rate estimation and demand-controlled ventilation, the CO 2 generation rate of building occupants is a crucial parameter. Currently, ASTM D6245-18 provides an estimation formula for the CO 2 generation rate, but there are indications that its results tend to be underestimated. This paper utilized more than 7,200 sets of CO 2 generation rate data from over 1100 participants aged 5 to 70 to propose a segmented regression model for estimating CO 2 generation rates based on gender, age, and activity metabolism as independent variables. Based on the patterns of CO 2 generation rate with age and metabolic rate, two critical age breakpoints were identified: 10/11 years for girls/boys entering puberty and 13/15 years for girls/boys entering the stable growth phase. The reliability of the established model was validated through literature-based data across various scenarios. The results revealed that using the activity intensity determined by the recommended metabolic rates in the ASHRAE Handbook, our estimations were closer to the experimental values compared to ASTM D6245-18, which required higher metabolic rates for compensation. The method in this paper requires only age, gender, and activity metabolic rate to estimate the mean CO 2 generation rate for different occupants, offering a simple calculation approach for key parameters in CO 2 applications within buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Classification of PVSD based on different ways of integration with building skins. • PVSD is mostly studied by using simulation methods. • Growing number of studies focuses on multi-objective optimization of PVSD. • Discussion of payback period provides a economic reference for the application. Existing studies indicate that as an important part of building integrated photovoltaics (BIPVs) technology, photovoltaic integrated shading devices (PVSDs) have become one of the important means to achieve energy reduction and green architecture. However, there is little existing literature that has systematically reviewed PVSDs, and existing reviews only discussed PVSDs from the perspective of basic parameters and control systems. Little is known about what methods can be used to systematically evaluate PVSDs and how economically applicable PVSDs are to the market. This paper aims to review all the PVSD-related studies in the last ten years from the perspective of research methods and economic feasibility assisted by Citespace software to filter related studies. This review concluded four important findings: 1) 68 % of existing PVSD-related studies adopted the simulation method to assess PVSDs in the last ten years, followed by the studies that adopted simulation and experiment methods; 2) EnergyPlus is the most frequently used software to perform simulation; 3) For the studies that carried out multi-objective optimization of PVSDs, 64 % of studies evaluated PVSDs by coupling energy performance with visual comfort. It is followed by those that coupled thermal comfort (39 %); 4) The shortest economic payback period of PVSD products in the retrieved papers was only 1.5 years, and the longest can reach 20 years. In future research, multi-objective optimization will become the study focus, and more studies about the aesthetic integration of PVSDs should be considered. This review provides a methodological and economic reference for researchers. [ABSTRACT FROM AUTHOR]

In the current global energy landscape, energy storage has the potential to become a key technical support for global carbon neutrality. Drawing insights from a comprehensive overview of existing energy storage systems, this paper proposes a three-phase crystalline energy storage and heating system characterized by intermittent operation. The unique thermodynamic property of lithium bromide gifts the system with remarkable energy storage density and heating capacity. To further investigate its operating characteristics, this study employs the Newton iteration method, with energy storage density as a pivotal technical indicator for evaluation. The results reveal that the existence of crystals mainly benefits nighttime heating supply. By adjusting input factors, it becomes evident that increasing the temperature of high-temperature heat source can enhance the heating supply by 16.92 %, which also leads to an increase in energy storage density to 2986.85 MJ/m3. Furthermore, the raising temperature of low-temperature heat source results in a substantial 61.78 % escalation in heating supply. Additionally, the dynamic investment payback period of the system is estimated to be 2.4 years, signifying its economic viability. The findings of this paper can provide valuable theoretical support for the application of crystalline energy storage systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • Aerogel sandwich construction building insulation offers higher energy savings ratio. • Functional silane modification reduces shrinkage of Z-direction from 57.5% to 2.3%. • Sandwich aerogel created super-efficient thermal insulation and sound absorption performances. • SA@UGFW demonstrates ultra-light, high-strength and efficient self-cleaning capability. In the context of "carbon neutrality and emission peak", aerogel that can meet national energy saving and emission reduction requirements while achieving efficient noise reduction has become a hot research topic. However, the development of multifunctional applications is greatly limited by the fact that pure aerogel usually exhibits relatively homogeneous properties. In this paper, SA@UGFW sandwich aerogel composites with functional silanes for structural strengthening were developed. SA@UGFW not only demonstrates ultra-lightweight (58 kg/m3) and high-strength properties (0.1 MPa) but also highly efficient self-cleaning capability (water contact angle ≈ 152.4°). The sandwich aerogel created super-efficient thermal insulation (0.015–0.017 W/(m·K)) and sound absorption performances (α max = 0.93, NRC = 0.51, 9.77 mm). SA@UGFW exhibited a cold surface temperature of 54.2 ℃ and a ΔT of 145.8 ℃ when the heat source temperature was 200 ℃, despite the sample thickness of only 9.77 mm. At this point, ΔT was higher than 70 % of the heat source temperature. The thermal conductivity of SA@UGFW was as low as 0.00975 W/(m·K), even in −40 ℃ environment. In addition, the sandwich aerogel also showed 0.0204 W/(m·K) of low thermal conductivity following ultra-low temperature (-196 ℃) treatment for 168 h. Simulation results show that aerogel building insulation reduces heating energy savings by 31.00 % and cooling energy savings by 27.34 % compared to traditional insulation. Therefore, the sandwich aerogel with multiple functions reported in this paper achieved the enhancement of indoor comfort and was expected to be widely applied in the construction field. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hygrothermal white-box models in China are proposed for specific cases. • Accuracy depends on climate features and building components based on assumptions. • Separate driving potentials for vapour and liquid transfer are suggested. • Hygrothermal reference year, measured or derived indoor climate database are more suitable input parameters. Moisture damage plays a key role for the durability of buildings and for the health and comfort of occupants. Various numerical models were proposed for engineers and architects to evaluate the hygrothermal condition before constructing in China. Although the physical discipline is universal, the complexity and variety of climates and building components make the moisture-related problems as well as the development and application of hygrothermal models distinctive in China. This paper is a comprehensive survey of the state-of-the-art in China to promote future advancement and academic communication between China and abroad. It investigated hygrothermal white-box models in China including modeling and input parameters. The paper presents that the assumptions of available models limit the application of hygrothermal white-box models in some situations. It also shows that there is a shortage of suitable indoor and outdoor climate databases. To sum up, the following items are necessary to establish hygrothermal simulation in building practice: models working with separate driving potentials for liquid and vapour transport; hygrothermal reference years as outdoor climate data; indoor climate data, either measured occupants' behavior or derived from building operation setpoints influenced by outdoor climate. [ABSTRACT FROM AUTHOR]

Low-carbon retrofit of heating systems in existing urban residential areas is one of the most critical approaches to achieving China's 2030 carbon peak and 2060 carbon–neutral goals. This paper proposes heat source classification technologies and heating evaluation indexes for heating system renovation, considering the optimization of energy structure and the demand for carbon trading. Most importantly, a decision-making method based on Entropy Weight TOPSIS applicable to the selection of heat supply schemes for a hypothetical 2 square kilometers urban settlement in Beijing is suggested in this paper. The results show that the electric heating utilization rate has the highest weight coefficient, followed by carbon trading revenue, heat storage utilization rate, and thermal heating utilization rate. Furthermore, geothermal cascade utilization is considered the optimal heating retrofit solution for this case. Eventually, the heating renovation route for the settlement level is recommended, which provides a solid foundation for future clean heating developments in China. [ABSTRACT FROM AUTHOR]

The current ultra-short-term cooling load forecasting models have not given due attention to the data pre-processing stage. In this paper, multivariate signal decomposition methods MEMD and MvFIF are used in the preprocessing phase to replace the complex signal with simpler subcomponents. The resulting increase in the number of features is tackled through a dimensionality reduction technique, PCA. Finally, prediction is done using two rigorous machine learning algorithms – LSTM and XGBoost. By combining these algorithms at different stages, four hybrid algorithms are formed - MEMD-PCA-LSTM, MEMD-PCA-XGBoost and MvFIF-PCA-LSTM, and MvFIF-PCA-XGBoost. Following a thorough performance comparison, this paper proposes MvFIF-PCA-LSTM for the prediction of ultra-short-term cooling loads. Additionally, experiments are performed to compare the running time of the proposed model, to endorse the importance of using PCA in the proposed model, and to evaluate the choice of parameters that undergo feature reduction. Compared to the base LSTM model assayed on the same datasets, the proposed model offered an improvement of 24.94%, 33.65%, and 23.82% in R2 values for SIT@Dover, SIT@NYP, and simulated datasets, respectively. MAPE achieved by the proposed model is exceptionally low, measuring at 1.13% for the SIT@Dover dataset, 1.42% for the SIT@NYP dataset, and a mere 0.36% for the simulated dataset. The best values of performance metrics computed for the proposed model demonstrate its accuracy in ultra-short-term cooling load prediction. [ABSTRACT FROM AUTHOR]

Solar district heating systems reduce carbon emissions effectively. Large-scale water pit thermal energy storages (PTES) have high heat capacities, low costs per cubic volume, and long lifetimes. Integrating PTES with a solar heating system can significantly increase solar heating efficiency and alleviate the time and climate constraints of renewable energy. An improved TRNSYS semi-analytical model of PTES (Type 1535–1301) is proposed based on the finite difference method. The model is characterized by discretizing the energy equation and then combining it with the analytical solution of the full differential equation to find the temperature variation. The model is validated with a full year measurement on a Danish PTES. In addition, a parametric investigation of grid discretization, storage geometry, soil properties, and diffuser location is performed. The control strategies are investigated to best utilize the PTES for demand response. The results indicate that the Type 1535–1301 model accurately predicts the year-round thermal performance of the PTES. The differences between the calculated and measured energies are lower than 1.3 %, and the differences between the calculated and measured temperatures are lower than 2.8 K. With an increase of the slope angle of the PTES side wall, the top and side heat losses decrease, and the storage temperature and thermal stratification effectiveness improve. Furthermore, the relative Fourier number (Fo m) is proposed to represent the soil thermal diffusivity. The rise of Fo m leads to an increase of heat loss and a decrease of storage efficiency. The analysis show that the top diffuser is best located at the highest position, and the bottom diffuser is best placed at the lowest position. At the same time, the middle diffuser is best placed at 29 % of the total volume from top to bottom. The semi-analytical model in this paper decreases the annual heat transfer computation time for a single grid to 57 ms. The model has been embedded into TRNSYS for convenient system prediction. The findings of this paper can be used as a reference for engineering practice and subsequent research. [ABSTRACT FROM AUTHOR]

• Features are selected from the perspectives of machine learning and field application. • Stepwise FS path combining optimization with machine learning algorithms is proposed. • Best performance using existing features and corresponding feature set are revealed. • Recommendations for feature supplementation to further improve performance are given. • Feature sets are verified to be general and effective by experiments and comparisons. Fault diagnosis (FD) is vital for enhancing chiller efficiency and reliability. Feature selection (FS) is the prerequisite and key to diagnose faults. This paper addresses two intriguing questions from machine learning (ML) and field perspectives. Question-1: Based on commonly installed sensors, what is the best performance that the FD models based on ML algorithms can achieve, and what features are relevant? Question-2: Which features can enhance diagnostic performance? and to what extent? This paper designs a stepwise FS process. First, a field investigation is conducted to gather information on sensors installed in actual chillers. Based on actual field installation, feature calculation cost, and thermodynamic mechanism, three levels of initial feature libraries are created, each containing an increasing number and type of features. An FS method combining an optimization algorithm with an FD model based on ML algorithm is proposed. In the end, the insight into the best diagnostic performance achieved by ML-based models using existing sensors and the corresponding optimal feature subsets is provided, and recommendations for feature supplementation to further improve diagnostic performance are also provided. Compared with other literature-reported feature subsets, the recommended feature subsets show better generality and effectiveness on seven commonly used ML-based models. [ABSTRACT FROM AUTHOR]

[Display omitted] • Review of the state-of-the-art data-driven methods for non-intrusive load monitoring. • Advanced NILM methods are accurate but more difficult to interpret and implement. • Existing NILM methods have not been evaluated in real-time testing scenarios. • Preparing appropriate training and test data is crucial for learning-based methods. • There is still more work needed to develop an accurate and useful NILM system. The rising demand for energy conservation in residential buildings has increased interest in load monitoring techniques by exploiting energy consumption data. In recent years, hundreds of research articles have been published that have mainly focused on data-driven non-intrusive load monitoring (NILM) approaches. Due to the high volume of research articles published in this domain, it has become necessary to provide a review of the up-to-date research in NILM and highlight the current challenges associated with its application. This paper reviews the state-of-the-art of NILM by following a structured assessment process to consider relevant and most recent documents in the literature. It presents the pros and cons of data-driven NILM methods, available datasets, and performance evaluation mechanisms. Even though research in NILM solutions has matured in recent years thanks to the use of deep learning models, there are still gaps in their effective deployment related to data requirements, real-time performance, and interpretability. Therefore, the paper also addresses the NILM development and implementation challenges and includes promising improvement measures that can be utilized to solve them. [ABSTRACT FROM AUTHOR]

• Investigation of a solar energy storage system under low-grade solar energy. • Dynamic heat storage and release processes under multiple parameters. • Matched collecting areas for different grades of meteorological conditions. • Performance optimization process of the solar thermal energy storage system. Designed and operational parameters matter greatly in how successful an energy storage system performs. However, a current challenge in matching suitable these parameters lies in the poor understanding of such systems' performance under dynamic weather conditions. In this paper, we investigated a phase change material (PCM) storage unit that is particularly aimed for poor-solar areas, and connected the unit to a flat plate solar collector to establish a complete solar thermal energy storage system (STESS). To optimize the system performance, different grades of solar radiation and outdoor ambient temperatures are introduced, and the effects of heat transfer fluid (HTF) flow rates and solar collecting areas are investigated numerically through multiple performance evaluation parameters. The results indicate that the solar collecting area has a significant effect on the performance of the STESS compared to that of the water flow rate. And different collecting areas of 1 m2, 1.5 m2, and 3 m2 with an optimal water flow rate of 0.06 m3/h are matched to different average daily solar radiation of 203.4 W/m2, 155.2 W/m2, 92.7 W/m2, respectively. Further for the heat release process, different heat release efficiencies from 88.32 % to 97.12 % with different energy consumption of the fan from 0.003 kWh to 0.063 kWh are obtained when the air flow rate is increased from 30 m3/h to 90 m3/h. One of the air flow rates of 60 m3/h is superior for the STESS considering higher heat release efficiency and lower energy consumption of the fan. The findings of this paper will contribute to optimizing the performance of a STESS in practical applications. [ABSTRACT FROM AUTHOR]

• A hybrid microgrid driven by solar energy for building is established. • An optimal dispatch strategy for a hybrid microgrid is proposed. • System performance is analyzed under isolated/grid-connected operation mode. • Operation characteristic is investigated on power selling/purchasing conditions. The optimal dispatch for hybrid microgrids is the crucial approach to decrease maintenance costs and enhance operational reliability. This paper aims to provide a feasible solution for the optimal dispatch of a solar thermal-photovoltaic hybrid microgrid. A distributed energy system of a building is established and the power load is analyzed. Operation parameters are optimized for hybrid microgrid in isolated operation mode and grid-connected operation mode. The operation performance with adequate and inadequate solar irradiation is evaluated by using fixed dispatch and optimal dispatch strategy. In addition, the performance of hybrid microgrid under power selling and purchasing conditions are analyzed. Results show that the operation mode of building hybrid microgrid can be adjusted to isolated/grid-connection modes based on actual power requirements. The optimal dispatch strategy shows great advantages on both solar adequate and inadequate conditions under isolated operation mode for hybrid microgrid, at most 14.3 % lower for maintenance than that of fixed strategy. In addition, the power selling to the external power grid can significantly improve the profit of the hybrid microgrid and generate earnings of $6102.1 per year under optimal dispatch. The research can offer an analysis approach for hybrid microgrid, and further increase clean energy utilization efficiency. [ABSTRACT FROM AUTHOR]

• Stabilized rammed earth (SRE) was produced and used for built full-scale building. • Thermal and moisture-related properties of SRE were characterized. • Monitoring of indoor hygrothermal fluctuations within a demonstrative building was discussed. • Temperature and humidity control capacity of SRE was proven by field measurements. • Building design strategies to improve the performance of SRE structures are highlighted. This paper presents the experimental testing and field evaluation of the hygrothermal performances of stabilized Rammed Earth (RE) from material– to building–scale level. Different passive design strategies to improve the energy efficiency of RE structures are also discussed based on a validated building energy model implemented on EnergyPlus. The accuracy of the model was approved through a two-month monitoring period within a demonstrative building during the summer season. The study involved the fabrication and characterization of a RE unit using local soil stabilized with 5 wt% cement and 15 wt% lime. Laboratory tests were conducted to assess the mechanical, thermal and moisture-related properties of developed RE. The results demonstrated that the stabilized RE exhibited excellent hygrothermal properties, with a moisture buffering capacity value of 2.76 g/m2%RH and a specific heat capacity of 983 J/kgK. Field observations confirmed the effective temperature and moisture regulation capabilities of rammed earth construction. Additionally, numerical findings indicated that the combination of wall thickness, night ventilation and a shading envelope can significantly reduce cooling energy demand by up to 31.8 %, decrease Indoor Overheating Hours (IOH) by up to 16.9 %, and mitigate indoor air temperature by approximately 5.15 °C and the peak indoor temperature by 2.6 °C during the hottest week of summer. [ABSTRACT FROM AUTHOR]

• This paper uses WSR systematic approach to analyze BECER policies. • BECER policy instruments are divided into three categories and nine dimensions. • Policy participants are categorized into four modularity classes in Henan, China. • Administrative instruments account for the largest proportion in Henan, China. • BECER policies were identified into eight topics in Henan, China. The building industry is a key sector that needs priority attention to achieve 'carbon peak 2030 and carbon neutrality 2060′. Building energy conservation and emission reduction (BECER) primarily relies on policy implementation. However, current policy studies are limited to specific perspectives of policy instruments, lacking a comprehensive approach. This study proposes an analytical framework based on the theory of 'Wuli-Shili-Renli' (WSR) to answer the question 'how to analyze BECER policies systematically'. Latent dirichlet allocation (LDA) topic analysis method was utilized to identify topics in the wuli dimension, while content analysis was employed to analyze policy instruments in the 'shili' dimension, and participants were investigated through Social Network Analysis in the renli dimension. Finally, Henan province was selected as a case study to apply the proposed WSR framework. The findings reveal that: 1) From a wuli perspective, BECER policy topics rely solely on government involvement and lack of topics that require societal or familial participation. 2) From a 'shili' perspective, administrative policies are dominated while voluntary and fixed policies are insufficiently represented. 3) From a 'renli' perspective, implementers of BECER policies are predominantly single entities with limited cooperation among different sectors. The proposed WSR framework offers an innovative approach to comprehensively researching BECER policies and can be a useful tool for formulating scientific BECER policies in specific regions. [ABSTRACT FROM AUTHOR]

• Oil fume control, thermal comfort and energy of commercial kitchen are explored. • Demand exhaust rate of fume hood is determined on oil control effect. • Conditioned air supply parameters are determined on thermal comfort. • On-demand ventilation reduces ventilation and air conditioning energy by 71.16%. • Airflow optimization reduces airflow by 20% and energy by 50.81%. To analyze the energy-saving potential of commercial kitchen ventilation and air-conditioning (VAC) systems and to create a good kitchen environment, this paper calculates the key influential parameters of the exhaust air volume and air supply temperature through CFD simulation. The former depends on the fume control effect and is closely related to the make-up air method, while the latter is governed by the thermal comfort. The energy consumption under different operational strategies is calculated using EnergyPlus. The following conclusions were obtained: (1) By summing the demand exhaust rate of the cookers with that of the fume hoods during the operation of multiple cookers, the pollution control effect can still be satisfied. Moreover, the airflow can be reduced by 20% through airflow optimization. (2) During summer, the conditioned airflow should constitute 30% of the total make-up air volume, with an air supply temperature of 22 ℃. Conversely, in winter, the conditioned airflow should comprise 40% of the total make-up air volume, with an air supply temperature of 14 ℃. (3) It is advisable to use a personalized conditioned air supply to create a local thermal environment, effectively reducing the energy consumption of ventilation and air conditioning systems. The energy-saving rates achieved through airflow optimization and on-demand ventilation are 51% and 71%, respectively. [ABSTRACT FROM AUTHOR]

• Evaporative porous clay layer for BIPV cooling with evaporation and thermal enhancements. • Hollowed clay layer achieved 16.5% PV temperature reduction compared to solid clay layer. • Hollowed clay layer showed 6.3% power and 4.5% efficiency improvements solid clay layer. • 11% reduction of PV temperature by ssing composite clay layer with iron-oxide fins. • Installation of hollowed clay layer led to the highest reduction in building's annual load. This paper investigates the employing of a passive evaporative cooling approach to mitigate the temperature rise of PV panels within building-integrated photovoltaic (BIPV) systems. The investigation explores enhancing the heat transfer capabilities of composite clay layers by incorporating thermal conductive materials such as iron-oxide and zinc-oxide. This enhancement aimed to improve the overall thermal properties of the composite clay structures, thereby boosting heat transfer rates. Additionally, two distinct designs were evaluated as well: one integrating hollowed clay layer and the other featuring a clay layer with extended fins to enhance both heat and mass transfer rates within the evaporative porous clay structure. A series of experiments conducted under real conditions using prototyped building rooms to simulate real BIPV systems to assess the cooling performance of the presented scenarios. The results revealed that the hollowed clay layer achieved the most substantial decrease in PV panel temperature, with a reduction of 16.5 % compared to the findings with a solid clay layer. In contrast, the composite Iron-oxide/clay layer demonstrated the smallest decrease in PV temperature at 11 %. Moreover, utilizing the hollowed clay structure led to a 6.3 % increase in average PV output power, as well as around 4.5 % enhancement in average photovoltaic's efficiency compared to the outcomes observed with the solid clay layer. Moreover, incorporating the hollowed clay layer resulted in the most significant decrease in the building's annual load, achieving a reduction of 79.6 %. This signifies approximately a 2 % higher enhancement in cooling capabilities compared to utilizing solid clay layer. Further, the cost of electricity production decreased by 7.7 % (from 0.104 $/kWh to 0.096 $/kWh) following the incorporation of the hollowed clay layer, while the payback time was reduced by roughly 0.8 years compared to utilizing the solid clay layer and by 1.7 years in comparison to the non-cooled BIPV system. [ABSTRACT FROM AUTHOR]

In recent years, research on road piezoelectric energy harvesters (RPEH) has made great progress in the field of road vibration energy harvesting. However, the research on the packaging structure and embedding method of RPEH is not comprehensive. In this paper, a new type of transducer unit structure is developed, which can improve the electrical energy output compared with the drum piezoelectric transducer, and its waterproof performance of the transducer unit is tested under different water depth and duration. Then, a new RPEH package structure is made, and the output performance is compared with the three commonly used package structures in the current research, and the field construction of RPEH with different embedding methods is carried out on the actual road to evaluate the electrical energy output capacity of RPEH under different embedding methods, and the best construction method of RPEH is summarized through durability test. Finally, the three-dimensional simulation model of RPEH with different construction methods is established, and the stress and voltage are analyzed by finite element method to verify the best embedding method. The test results show that the piezoelectric output efficiency of the new transducer unit is increased by 38.9 %, and it still has good electrical energy output after 24 h immersion at a depth of 1.5 m, and its waterproof level reaches IPX8. Under larger excitation frequency and load, the indoor output of RPEH in the new package structure has better output performance in indoor test. The bolt-spring package is used to apply pre-pressure to RPEH, and the output efficiency of RPEH is improved. When the excitation conditions are 10 kN, 10 Hz, the output power is increased by 11.1 %, 127.3 % and 247.4 % compared with the other three package structures. The maximum power of RPEH is 3.41 W by placing concrete on the bottom surface of RPEH sponge washer and using asphalt as the pavement layer to ensure the durability of piezoelectric pavement. The finite element analysis illustrates the strain and voltage variation of piezoelectric pavement with different embedment modes, and the simulation results are consistent with the test results. The research results provide an important basis for the design of RPEH packaging structure and embedding method in road piezoelectric engineering. [ABSTRACT FROM AUTHOR]

[Display omitted] • A comparative assessment of district energy modelling tools was carried out. • Different levels of data detail and features for reliable results were explored. • A lack of robustness for result validation at the district level was identified. • 29 energy modelling tools were evaluated with a focus on result validation. • The capabilities of such tools were stressed based on different criteria. In order to reach the goal of reducing emissions by at least 55% by 2030 and achieving decarbonization by 2050, the increasing emphasis on net-zero energy buildings/districts encourages the development of advanced modelling tools to better design and manage district energy systems. This paper presents a critical review of such tools, considering the different detail level of building data and analysing the reliability of obtainable results. Initially, it elaborates on the characteristics of data resources and formats, energy demand representations of individual buildings, and the interconnection between individual buildings and districts, which are subsequently used to analyse the accuracy level of case studies. Then, the most used evaluation criteria for comparing tools are revised. Five categories are defined: (i) input data and representation of buildings, (ii) district energy system components (i.e., generation, distribution, storage), (iii) outdoor environment, (iv) user behaviour and mobility, and (v) validation and licencing. 29 tools suitable for district energy systems modelling are critically analysed with a focus on accuracy and validation, as well as on their application and future perspectives. The results highlighted the importance of data reliability in modelling approaches and results. Difficulties in achieving accurate results included robust data acquisition, interconnection among individual buildings, outdoor environment, and modelling approaches. The results also emphasized that, although no tools can cover all the possible features at the current stage, this study can support the selection of the most suitable tool for specific applications at the district scale. [ABSTRACT FROM AUTHOR]

The renewal of green home appliances is a crucial measure for households to save energy and reduce emissions. However, how online reviews, especially those relate to energy-saving, affect green home appliance purchase behavior (GHAPB) lacks exploration. In this paper, we investigate over 1 million online reviews on about 3,116 types of air conditioner from JD. By applying word2vec, we divide energy-saving related information in the following three types: norm information, environmental health information and price information, and construct dictionaries for each. Then, the effect value of energy-saving information is quantified from perspectives of breadth, depth and intensity through sentiment analysis. The influence of energy-saving information in online reviews on GHAPB is finally analyzed by linear regression and machine learning models. The results show that all energy-saving information has positive impact on GHAPB, and environmental health information is the most important one. In addition, the attributes of online reviews impose a greater influence on GHAPB, comparing with those of products. The in-depth exploration of energy-saving information in online reviews provides targeted recommendations for the manufacturer and the retailer to promote the adoption of green home appliances. [ABSTRACT FROM AUTHOR]

As global decarbonisation requires the widespread adoption of solar photovoltaic (PV) electricity, addressing challenges related to land use has become relevant. The conflict between PV installations and other land uses, such as forestry or agriculture, highlights the urgency for alternative solutions. Integrating PV technology into the built environment is a compelling strategy to mitigate these challenges, enabling electricity generation precisely where it is needed. In the context of buildings integrated photovoltaics (BIPV), PV modules serve a dual purpose, functioning both as electricity generators and integral components of the architectural design. Therefore, the architecture requirements — specifically in terms of shape, size, and colour— become relevant for BIPV modules. This paper offers a general overview of the diverse colouring technologies employed for BIPV modules, describing their functioning, challenges, and advantages. An examination of the current landscape of coloured PV products involving considerations of pricing and power output is presented. Additionally, this work addresses the critical topics of reliability and stability in colour solutions, outlining methodologies for quantitative colour characterization. It provides foresight into the potential challenges facing installations in the future and explores the multifaceted social, economic, and environmental implications of this evolving technology. [ABSTRACT FROM AUTHOR]

• A method to identify building models under unknown internal heat gains is proposed. • The method is tested using simulation-generated data and experimental data. • The method leads to significantly improved models compared to conventional methods. • The estimated internal heat gains capture the trend of the actual ones well. An accurate grey-box building thermal model is an essential component for prediction-based control of split air conditioners. As the thermal performance of building envelope improves, the internal heat gains play an increasingly noteworthy influence on the building's thermal dynamics. However, the internal heat gains are usually not measured, which makes the conventional data-dependent identification methods of building thermal models fail. To improve the accuracy of building thermal models under unmeasured internal heat gains, a two-step parameter identification framework for resistance–capacitance (RC) models is proposed in this paper. First, the time-invariant building model parameters are estimated by designing a novel optimization objective function. Next, the time-varying internal heat gains are identified based on the mismatch in predictions of the estimated building model from the first step and the measured temperature. The effectiveness of the method is evaluated using data from a simulation case and an experiment case. The results show that the proposed method outperforms the conventional identification method as well as two other identification methods that handle the unmeasured internal heat gains. Besides, the internal heat gains estimated by the proposed method can capture the trend of the actual internal loads well. [ABSTRACT FROM AUTHOR]

Accurate industrial load forecasting is a prerequisite for ensuring the smooth operation of the power system. Due to the strong fluctuation and complex characteristics of industrial loads, it is difficult to accurately predict short-term power demand. To address this issue, this paper proposes a deep learning prediction model based on hybrid ensemble and error correction. The proposed model is divided into two phases: in the first phase, deep power features are extracted from multivariate data through a hybrid ensemble strategy consisting of Random Subspace, Boosting, Ensemble Pruning, and Multi-Objective Molecular Dynamics Theory Optimization Algorithm (MMDTOA). First, the strategy splits high-dimensional industrial data into multiple sub-datasets. Subsequently, for the features of each sub-dataset, the proposed MMDTOA is applied to perturb the parameters of GRU to generate base learning machines that balance accuracy and diversity. Finally, these base learning machines are integrated by kernel ridge regression stacking. Among them, the two-stage selection strategy and co-evolutionary strategy are embedded into the MMDTOA to enhance the optimization searching effect; in the second stage, a combined error correction strategy is proposed by utilizing the residual information in the prediction results. By combining the dynamic Gaussian error correction function and GRU error correction model, the prediction accuracy of the ensemble model is further improved; Experimental results on real Korean datasets show that the proposed method achieves a minimum value of 3.684% and 7.266% in NMAE and NRMSE, which outperforms eight comparative models, such as SVM, ELM, and CNN, with higher accuracy and robustness. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of two probabilistic approaches, Ensemble batch Prediction Intervals (EnbPI) a conformal prediction approach and XGBoost Location, Scale and Shape (XGBoostLSS), in predicting building energy consumption and in detecting systemic anomalies with proposed alarm matrix. The research questions focus on the effectiveness of these models in providing both point and probabilistic predictions and their utility in identifying collective anomalies. Both models showed good point and distribution prediction performance. For example, the observed point prediction had CV-RMSE in the range of 9 to 17%, outperforming recommendations from ASHRAE. Furthermore, a post-processing stage, the alarm matrix, effectively flags collective, repetitive anomalies, thus shifting focus from conventional point anomalies. The EnbPI-based method yields higher recall rates, with a trade-off of having more false alarms, while XGBoostLSS-based method excels in precision, minimizing overlooked alarms. Moreover, a robustness analysis was carried out to evaluate how these models performed when faced with training datasets containing anomalies. The robustness analysis revealed that the EnbPI-based method was more prone to overfitting, meaning its performance actually improved when the training data included some noise. On the other hand, the XGBoostLSS-based method was more stable, performing well with low levels of noise with performance drop when the noise level was high. While the findings contribute significantly to building energy consumption prediction and anomaly detection, future research could address performance in dynamic environments for the methodology and explore continual learning strategies. [ABSTRACT FROM AUTHOR]

Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, ▪ penalties are inputs used by the optimizer to find the optimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort. • Achieving low network return temperature by optimizing energy flows at district heating substations. • Maintaining the primary and secondary sides mass flows by introducing the new factor. • Finding optimal network temperatures and flow rates by estimating energy demands of the buildings. • Improved network techno-economical and environmental performance and thermal comfort of consumer. [ABSTRACT FROM AUTHOR]

Resilience is becoming increasingly important in power systems, microgrids (MG) and buildings. It evaluates their performance against disruptive events. The approaches mainly correspond to high-impact, low-probability events such as natural disasters and intentional attacks affecting the electrical systems infrastructure. However, resilience can encompass medium and low-impact events such as minor civil structural accidents, light faults and supply disturbances. Some resilience assessment advances are in buildings at the low-voltage (LV) level. They include vulnerability to natural disasters, reliability of supply, and service quality. These works usually use approaches independent of each other, leaving a gap between their relationship and interpretation. Therefore, there is a need to consolidate a resilience assessment strategy to guide the analysis of vulnerabilities and strengths in the same direction. This paper proposes a comprehensive approach to evaluate electrical resilience for buildings. It compiles quantitative strategies for studying electrical resilience, focusing on LV systems. It proposes a methodology for integrating the electrical system infrastructure's vulnerability, the supply's continuity and the voltage service's quality. Implementing this approach in a university building equipped with smart metering demonstrates the effectiveness of the proposed methodology for assessing electrical resilience. The results show a comprehensive resilience analysis and the possibility of extending the methodology to MG and LV distribution networks. • A categorisation of building electrical system disturbances by impact level and frequency. • A methodology to assess electrical resilience for each category of disturbance. • A Comprehensive assessment of electrical resilience, covering structural robustness, service continuity, and supply quality. • A case study application integrating critical loads, a backup system, and smart metering. [ABSTRACT FROM AUTHOR]

• Energy performance of HVACs in super high-rise buildings were field-tested and analyzed. • The representative issues leading to large energy consumption of HVACs were summarized. • Commissioning of VAV system and multistage chilled water systems were conducted. • The setting values of HVACs should be adjusted in time according to practical cooling demand. The HVACs in super high-rise buildings commonly consists of variable air volume (VAV) systems, multistage chilled and cooling water systems, primary-secondary chilled water system in chiller plant, and the chillers combination is much more complex, leading to the significantly higher energy consumption than that of normal buildings. This paper contains field tests and comparative studies on the operation performance of HVACs in two super high-rise buildings and summarized representative issues accounting for the high energy consumption. Field test results showed that the annual energy efficiency of the whole HVAC system, before being commissioned, was only 1.79 and 2.15 in two projects. The HVACs, typically VAV systems, chilled and cooling water systems, all suffered from over-supplying and energy wasting. Besides, the chillers commonly operated with a low partial load ratio (PLR), whose energy performance was much lower than expected. Aiming to dig out the typical issues, analyses were carried out from external, internal factors, and their interconnection. Furthermore, optimization methods were put forward with corrected vital indexes. Results showed that proper control strategies should maintain the VAV system, multistage chilled and cooling water system operating with significant distribution temperature differences, tiny distribution pressure drops, and high energy efficiency, thus achieving energy saving and ensuring space cooling effects. Besides, the control strategies, especially the number control of chillers, were influential during the whole cooling season to ensure the chillers operate with optimal PLR and thus achieve better energy performance. [ABSTRACT FROM AUTHOR]

Air-source heat pumps (ASHPs) operating in cold climates experience problems with frosting and high refrigerant temperatures. These problems increase energy consumption, and their severity depends on the climatic conditions. In the present paper, a methodology for identifying the prevailing problem between frosting and high discharge temperatures is presented. Three performance indices, the frosting index (FI), the discharge index (DI), and the total loss index (TLI), are proposed to quantify the impacts of frosting and high discharge temperatures on the annual performance of ASHPs in different climatic conditions. The FI and DI show which problem (frosting or high discharge temperature) dominates, and the TLI indicates the combined effect of frosting and high discharge temperatures on the performance of an ASHP. A thermodynamic model of an ASHP coupled with the TRNSYS building simulation tool is used to estimate the performance of an ASHP and the proposed loss indices to estimate the impact of both frosting and high discharge temperatures for 45 cities in Canada. The results can be extended to other parts of the world that experience similar climatic conditions The results reveal that in cities in ASHRAE climatic zones 5 and 6 (classified as cold regions) where the ambient air temperatures are predominantly between −15 °C to 6 °C, ASHPs are heavily impacted by frosting. The problem of high discharge temperatures in ASHPs is predominant in cities in climate zones 7 and 8 (classified as very cold and subarctic regions) where the temperatures are frequently below −20 °C in winter. Among the cities considered, St. John, NL has the highest fraction of heating hours experiencing frosting (90 %), where the annual increase in energy consumption due to frosting is 13.5 % of the annual heating energy consumption. The highest annual increase in energy consumption due to high discharge temperatures is in Isachsen, NU (zone 8), where the increase is 30 % of the annual heating energy consumption. Based on the proposed indices, another index called the performance gain index (PGI) is created, which can be used as a first step to assess the energy-saving potential of design modifications applied to ASHPs to solve the problems of frosting and high discharge temperatures. The PGI will aid in developing climate specific ASHPs. One possible design modificationis the use of a two-stage ASHP with an economizer. It is observed that the two-stage ASHP with economizer can mitigate high discharge temperatures and improve performance in very cold and subarctic regions (zones 7 and 8). However, it is not as beneficial in zones 5 and 6, where the impact of high discharge temperatures on performance is minimal and frosting dominates. Finally, a case study, using the PGI to evaluate the economic and environmental effectiveness of a two-stage ASHP with economizer is presented for the city of Saskatoon. [ABSTRACT FROM AUTHOR]

• Indoor-quality oriented approach is proposed for building post-retrofit analysis. • The approach is based on readily monitored indoor data and on mangers and users' opinions. • As case study, the student dormitory of University of Athens is presented. • Thermal comfort indices greatly vary with management strategy and personal choices. • The analysis of indoor air quality highlights the key role of mechanical ventilation. The rapid expanding of refurbishment incentives requires to deserve more attention on how to reach nearly zero energy standard without compromising occupant wellness, when the building is undergone to important architectural transformations. To overcome the above drawbacks mainly for student dormitories, the paper introduces a new indoor quality oriented post-retrofit evaluation approach that simultaneously quantifies the impact of design decisions on thermal, respiratory and visual comfort. Investigations on the building quality and readily monitored indoor variables are the core of the approach that also provides sensitivity analysis on some subjective parameters and questionnaires for the main involved stakeholders. The proposed approach is tested on a student dormitory belonging at National and Kapodistrian University of Athens, refurbished in the frame of Horizon Project Pro-GET-OnE. The results reveal an improvement in the passive control of thermo-hygrometric comfort as well as in the satisfaction level. The adoption of mechanical ventilation consistently ensures dioxide emissions lower than 430 ppm, TVOC below 300 μg/m3 and PM2.5 and PM10 lower than 6 μg/m3. More in general, it is remarkable the importance of considering all comfort domains and occupant behavior, as one design or management choice might improve one indoor quality domain at the cost of others. [ABSTRACT FROM AUTHOR]

• Prospective and consequential LCA and LCC are integrated in a PSK BIPV case study. • A prospective methodology links environmental and economic benefits of PSK systems. • Silicon-based PV results as more economically valuable than PSK modules in r-BIPV. • PSK f-BIPV systems will become more economically convenient than a glass façade. • f-BIPV results the most attractive PSK systems application to cut GHG emissions. This paper addresses an integrated economic and environmental assessment where a consequential and prospective life cycle approach is applied to perovskite (PSK) building integrated photovoltaic (BIPV) systems. Two configurations are considered: slanted roof (r-BIPV) installations, and the fenestration of façades (f-BIPV). Concerning r-BIPV, PSK systems are expected to compete with crystalline silicon (c-Si) modules; whereas a glass façade is chosen as an alternative reference for f-BIPV. Assuming economic rationality as the main criteria for the adoption of technologies, a deployment time for the PSK system is estimated and the economic and environmental advantages in the Italian context are evaluated. This assessment is based on three foreground scenarios addressing the gradual development of PSK systems. The results show that while c-Si modules are preferred in the market of r-BIPV systems, PSK modules will find space in f-BIPV applications. Depending on the selected scenario, the economic benefits accumulated by Italian energy users are assessed between −0.5 % and −10.3 %. On the other hand, PSK systems imply a variation of the national GHGs emissions that ranges between + 0.01 % and −7.5 %. Depending on the scenario, the PSK-based f-BIPV systems can provide either environmental benefits (i.e., cumulative energy demand) or burdens (i.e., ecotoxicity indicators). [ABSTRACT FROM AUTHOR]

Effective fault diagnosis of compressors in heating, ventilation, and air conditioning (HVAC) systems is critical to ensure service reliability and boost energy efficiency. HVAC compressors are distributed in different areas and work under heterogeneous conditions, which poses emerging challenges to their data-driven modeling. Most existing methods assume multiple category-balanced source domains for model training. Although domain adaptation and generalization methods have emerged to address the data distribution discrepancies in cross-domain fault diagnosis, limited source domains and imbalanced fault categories across domains still constrain the real-world applicability of data-driven models in HVAC compressor fault diagnosis under unseen working conditions. Therefore, this paper studies a significant fault diagnosis problem named single imbalanced domain generalization (SIDG) and proposes a corresponding network (SIDGNet) for intelligent HVAC compressor fault diagnosis. Specifically, a rare fault diagnosis module combined with focal loss is introduced to tackle the class imbalance problem. To achieve better diagnostic boundaries and resist unknown data distribution discrepancies, joint supervised contrastive learning and adversarial learning with specialized data augmentation are introduced as auxiliary modules to improve the robustness and generalizability of SIDGNet. An improved uncertainty-based dynamic weighting mechanism is developed to intelligently balance the weights of module-specific losses during training, which ensures an efficient and stable optimization process. Extensive SIDG fault diagnosis experiments conducted on HVAC compressors demonstrate the superiority of SIDGNet over existing models in SIDG scenarios. [ABSTRACT FROM AUTHOR]

[Display omitted] • A numerical analysis of a lightweight concrete roof with PCMs was performed. • Different roof configurations with and without PCM were numerically analyzed. • OpenFoam was used to model each roof configuration and code verifications were accomplished. • All roofs with PCM showed lower inner surface temperatures regarding the base case. • A maximum reduction of 65.5% in energy was achieved by case D-PCM-SP25Eu. The use of phase change materials in building envelopes has attracted the attention of researchers to minimize cooling and heating loads and for thermal comfort regulation. A research gap was found regarding the numerical analysis of lightweight roofs with PCM considering conjugate heat transfer. Thus, this paper aims to evaluate the thermal performance of a lightweight hollow concrete roof with a phase change material (PCM) in a subhumid climate. Three roof configurations with two PCMs were numerically analyzed through computational fluid dynamics. Simulations were conducted using the open-source code OpenFoam and were verified and validated with good agreements proving the confidence of the proposed methodology for building envelope design purposes. The roof configurations with fully-filled PCM exhibited the best thermal behavior, reaching higher temperature reductions between 1.4 °C to 2.0 °C, respectively, and reductions of indoor peak heat flux by approximately 47 % compared with the reference case. The roof fully-filled with an inorganic PCM with a melting temperature of 25 °C was the most energy-efficient roof configuration because it achieved the highest energy savings of 65.5 %, lowest electricity costs of 0.85–4.33 USD m−2, and lowest CO 2 emissions of approximately 18.31 kgCO 2 e m−2. The results demonstrated that the utilization of PCM into lightweight concrete roofs in subhumid climates is very promising. [ABSTRACT FROM AUTHOR]

This paper presents the MUSENILM model, a non-intrusive load decomposition model incorporating a parallel multi-scale attention mechanism to enhance energy monitoring and management in smart grids. The core innovation of the proposed model is its ability to extract multi-scale features, enhancing the model's understanding of time series data and achieving significant performance improvements on the UK-DALE and REDD public datasets. Specifically, when MUSENILM identifies the fridge electricity consumption pattern on the UK-DALE public dataset, compared to previous models, the accuracy improves from 88% to 91%, and the F1 score increases from 87% to 90%; on the load decomposition tasks of the remaining four appliances, the F1 scores are all improved, while the mean absolute error (MAE) and cumulative absolute error (SAE) for the five appliances are also reduced. Additionally, it shows better results compared to previous models on the REDD dataset. Moreover, when the MUSENILM model is transplanted to embedded devices and applied in smart grids, it can effectively identify illegal lithium battery charging events of electric bicycles in different scenarios, which is crucial for ensuring grid security and optimizing energy distribution. This research not only provides an efficient method for the field of NILM but also offers practical solutions for violation monitoring and management in smart grids. [ABSTRACT FROM AUTHOR]

By conducting an assessment of the existing building stock from the perspective of the Green Building Certification System (GBCS), it is possible to contribute to the improvement of energy utilization and reduction of greenhouse gas emissions as promoted through the Renovation Wave strategy by the European Union. These aspirations will simultaneously contribute to enhancing human well-being, indoor environmental quality, and comfort. This paper describes a novel methodology for enhancing the renovation of existing buildings at the design stage using automation within the Building Information Modeling (BIM) environment. By parameterizing the requirements of GBCS using a visual programming tool, real-time insight into the number of achieved certain issue credits in the certification process is enabled. It was also created to enable designers in the Architecture, Engineering, Construction, and Operations (AECO) sector to continuously monitor the development of design and make modifications as early as possible in the design process. Thus, cost overruns in design projects, as well as later renovations and building maintenance, are prevented. This research has utilized the capability of BIM to perform rapid compliance simulations with Building Research Establishment Environmental Assessment Method (BREEAM) requirements without the need for manual calculations and/or computer simulations in other software, which would increase the time required for analysis due to additional modeling requirements. The methodology is demonstrated using the automation of the Daylighting section of the issue Hea01 Visual Comfort from the Technical Manual SD225 1.4 − BREEAM International Non-Domestic Refurbishment 2015, and the validation was performed on a BIM model of an existing building belonging to the educational sector. The application of the described methodology in the case study reduced the time required for assessing the number of achieved daylighting credits by 40%, with potential savings extending up to 80% depending on the chosen criteria for analysis. The tool is designed to be applicable to other GBCS, building types, and newly designed buildings as well. [ABSTRACT FROM AUTHOR]

With the widespread integration of renewable energy sources into the power system and the diversification and complexity of the energy consumption patterns, which makes it more difficult to maintain a source-load balance of the power system. In the power systems, the results of the power load forecasting can be used for generator scheduling and load distribution. Therefore, the accurate power load forecasting is critical to operation of the power system. In this paper, a load forecasting model based on a dynamic adaptive and adversarial graph convolutional network (DAAGCN) is proposed, which combines a dynamic adaptive graph generation network (DAGG) and a generative adversarial network (GAN). Firstly, DAGG utilizes integrated time-varying embedding and node embedding to generate dynamic adaptive graphs for inferring spatial–temporal dependencies between different temporal loads. Second, the underlying spatial–temporal prediction model is adversarial trained based on the idea of a zero-sum game. The module parameters are optimized using the L1 loss and the adversarial training loss together as the training objectives of the model. Finally, short-term forecasting of electricity loads was performed on the datasets using DAAGCN, and the results were compared with several other forecasting modules. [ABSTRACT FROM AUTHOR]

• The study obtained that net positive energy generation for three different types of academic buildings in Bangladesh. • All studied cases were found feasible based on economic matrices i.e LCOE, NPV, PBY, PI. • The installation of PV systems at academic buildings resulted in a significant amount of carbon emissions reduction. • Alignment of sunlight and peak load hours of academic buildings presents a viable option to achieve net zero energy. Bangladesh faces challenges with global carbon emissions and energy security due to its heavy reliance on imported fossil fuels. This paper investigates the techno-economic viability of photovoltaic (PV) systems for achieving net-zero energy in academic buildings as a sustainable alternative. The study evaluates the performance of PV systems based on factors including annual energy production, capacity factor, net-zero energy potential, self-consumption/sufficiency ratio, renewable fraction, levelized cost of energy (LCOE), net present value (NPV), payback period, profitability index (PI), and carbon emission reduction. The results indicate that optimally sized PV systems can generate net positive energy: 6.18 MWh for universities, 4.04 MWh for high schools, and 3.62 MWh for primary schools. Economic viability was assessed under two billing methods (net metering and all energy buy/sell), and all cases showed positive NPV and PI values greater than 1. Decarbonization analysis revealed significant reductions in CO 2 emissions: 2026.5 tons for universities, 18.9 tons for high schools, and 9.7 tons for primary schools. Finally, a sensitivity analysis assessed the optimal PV system size for seven other regions in Bangladesh. These findings suggest that the widespread adoption of PV systems in academic buildings can play a crucial role in achieving net-zero energy, contributing significantly to the government and policymakers' pursuit of sustainable development goals. [ABSTRACT FROM AUTHOR]

• A pre-1919 primary school with single glazing in Edinburgh has an oversizing of its space heating system of 32 % at design conditions. • Supply temperature can be operated below 70 °C all year round. • Supply temperatures can be reduced below 55 °C all year-round during daytime. As part of the decarbonization of its existing building stock the UK needs to replace gas boilers with heat pumps and low-temperature heat networks. Both technologies are efficient at supply temperatures of 55 °C, with a maximum of 70 °C for heat networks, when existing systems have been designed to operate at 82 °C. Most space heating systems are expected oversized and operate most of the year in part-load, which facilitate the temperature reduction, but little is known about the degree of oversizing. This paper uses the case study of a pre-1919 school in Edinburgh to evaluate the true oversizing of its heating system and its consequences on minimum supply temperatures. This building is still equipped with single glazing and is therefore a worst-case scenario. An oversizing of 32 % was estimated which enables the use of a supply temperature below 70 °C all year round. Moreover, the supply temperature can be kept below 55 °C all year round during daytime due to internal heat gains. In conclusion, this building shows a nascent readiness for low-temperature heat. It would benefit from energy efficiency measures such as double-glazed windows, but this is not pre-requisite. Those results are important for the heat network industry when sizing their systems, but also energy and asset managers when they schedule renovation programmes to have their buildings low-temperature-ready. [ABSTRACT FROM AUTHOR]

• An explicit definition of smart building envelopes is presented. • A summary and the characteristics of smart building envelopes is provided. • A review of the diverse types of smart building envelopes is conducted. • A detailed overview of the optimization of smart building envelopes is introduced. • Research gaps are summarized, and future study directions are outlined. The building envelope refers to the fundamental components on the exterior of a building responsible for environmental isolation and load-bearing. It serves functions such as thermal insulation, wind protection, waterproofing, and structural support. A smart building envelope retains these traditional functions while also autonomously adjusting to environmental changes, thereby achieving a comfortable indoor environment and efficient energy utilization. In recent years, with the rapid advancement of new materials and intelligent control technology, research related to smart building envelopes has been on the rise. Various types of smart building envelopes are gradually finding application in engineering practice. This paper offers a comprehensive overview of the current status and progress in this field, covering three main aspects: definition and characteristics, composition and types, optimization, and application of smart building envelopes. By analyzing the latest research developments, discussing typical application cases, and examining key technical challenges and research trends, it aims to provide a reference and foundation for researchers and practitioners interested in smart building envelopes. [ABSTRACT FROM AUTHOR]

Most buildings in Europe were constructed between 1850 and 1960, a period in which energy efficiency was not considered much. However, many of these structures have architecturally valuable facades that should be preserved, making internal thermal insulation the only practical solution. Therefore, internal insulation has grown in popularity despite the potential risk of moisture-related problems behind the insulation and on the external surface of the original wall. To ensure the structure's durability and the residents' wellbeing, insulating solutions must undergo real-life testing to demonstrate their effectiveness and moisture safety. The present paper is a compilation of case studies performed over the last decade in Denmark, it compares across four case studies of residential buildings. The apartments were insulated internally with either diffusion-tight or diffusion open and capillary active insulation systems. In some of the cases, internal insulation was applied in combination with hydrophobization of the existing facade. Temperature and relative humidity were measured in the indoor climate, at the intersection between insulation and masonry, and in some cases also at the wooden beam ends or the spandrels. Additionally, the risk of mold growth was calculated. The current study focuses on the wall's hygrothermal performance in relation to the thickness of the masonry and insulation, the wall's orientation, the indoor moisture excess, the effects of hydrophobization and the role of indoor climate. The results indicate that − while internal insulation of masonry more or less result in expected energy savings − areas with thin masonry or very thick insulation give an increased risk for mold growth. In addition, high indoor moisture excess in combination with diffusion open insulation system increases the risk for mold growth, while the other parameters played a less decisive role. [ABSTRACT FROM AUTHOR]

The purpose of the air conditioning system (A/C system) is to meet the cooling capacity needs of individual rooms. In order to optimize energy-saving in the A/C system, it is essential to conduct a study on the operational status of various equipment, considering the comfort demand of the human body and dynamic electricity prices. This feature will enable collaborative control of the A/C system. Consequently, this paper presents a two-stage operational optimization strategy. Firstly, the comfort requirement is taken into consideration, and the room's set temperature is treated as a decision variable. An optimization strategy is proposed to meet real-time electricity prices and indoor cooling demand. Furthermore, by optimizing the cooling demand, the operation of the main equipment in the A/C system is also optimized, leading to the derivation of an optimal cooling strategy at the minute level. According to this strategy, there are significant reductions in the energy consumption and the carbon emission relative to the rated operating conditions at each time period. The energy consumption can be reduced by up to 4.11%, and the carbon emission can be reduced by up to 30.81%. [ABSTRACT FROM AUTHOR]

Cleaner end-uses of energy, including electrified heating, represent a cornerstone of a decarbonised energy transition. On the other hand, many governments have adopted measures to mitigate energy poverty and facilitate access to modern energy services, including heating. Both objectives may be interrelated, since energy poor people are less likely to use cleaner (and costlier) heating fuels. This paper analyses the impact of energy poverty on the decision to use different heating sources and identifies whether the events of COVID and the Ukraine war have affected this decision. Thus, a multinomial probit model is estimated using information from a large database of Spanish households in 2019, 2021 and 2022. The results show that being energy poor increases the probability to use carbon-intensive energy sources for heating compared to electricity, and that COVID and the war in Ukraine have affected this relationship. The increase in energy poverty over those years has negatively affected the decarbonisation goal with respect to heating choices. The influence of those events on the probability to use electric heating depends on some household and dwelling features. Therefore, mitigating energy poverty increases the welfare of energy poor people while supporting the choice of cleaner heating. [ABSTRACT FROM AUTHOR]

To reduce global carbon emissions and limit climate change, controlling energy consumption in buildings is an important piece of the puzzle. Here, we specifically focus on using a demand response (DR) algorithm to limit the energy consumption of a residential building's heating system while respecting user's thermal comfort. In that domain, Reinforcement learning (RL) methods have been shown to be quite effective. One such RL method is Monte Carlo Tree Search (MCTS), which has achieved impressive success in playing board games (go, chess). A particular advantage of MCTS is that its decision tree structure naturally allows to integrate exogenous constraints (e.g., by trimming branches that violate them), while conventional RL solutions need more elaborate techniques (e.g., indirectly by adding penalties in the cost/reward function, or through a backup controller that corrects constraint-violating actions). The main aim of this paper is to study the adoption of MCTS for building control, since this (to the best of our knowledge) has remained largely unexplored. A specific property of MCTS is that it needs a simulator component that can predict subsequent system states, based on actions taken. A straightforward data-driven solution is to use black-box neural networks (NNs). We will however extend a Physics-informed Neural Network (PiNN) model to deliver multi-timestep predictions, and show the benefit it offers in terms of lower prediction errors (−32% MAE) as well as better MCTS performance (−4% energy cost, +7% thermal comfort) compared to a black-box NN. A second contribution will be to extend a vanilla MCTS version to adopt the ideas applied in AlphaZero (i.e., using learned prior and value functions and an action selection heuristic) to obtain lower computational costs while maintaining control performance. • Evaluated a PiNN for multi-timestep predictions. • Implemented and assessed advanced MCTS for residential heating DR. • The PiNN enables MCTS to obtain higher rewards compared to a Black-Box model. • Incorporating prior knowledge via NN enhances tree search efficiency. [ABSTRACT FROM AUTHOR]

Previous studies have shown that coincident design day (CDD) can make the calculated design cooling load of an air-conditioning system more rational and accurate. The conversion process of room heat gain to cooling load in radiant cooling systems is different from that in convection cooling systems. The radiant time series method (RTSM) of generating the coincident design day is inapplicable to radiant cooling systems. In this paper, the radiant and convection time series method (RCTSM) is proposed to generate the coincident design day for radiant cooling systems. The relative deviation of design cooling load, computation time for generating coincident design days, and the correct rate are introduced to evaluate the applicability of RCTSM for generating coincident design days. Fifteen room/building characteristic parameters are taken into account to construct 10,000 simulated sample rooms for each city. Compared to the theoretical design cooling load, the relative deviations of design cooling load determined by RCTSM are generally within 7.23 %. RCTSM is suitable to calculate design cooling load and generate coincident design day for radiant cooling systems. The computation time of RCSTM for generating coincident design day is less than one of sixty-third of that of heat balance method. RCTSM can significantly enhance the efficiency in generating coincident design day. The correct rates of the coincident design days generated using RCTSM is higher than those using heat balance method. These results indicate that RCTSM is more suitable to generate the coincident design days for radiant cooling systems than heat balance method. [ABSTRACT FROM AUTHOR]

The paper aimed to investigate the eco-lightweight concrete external walls suitable for the hot-summer and cold-winter (HSCW) zone, utilizing locally available renewable materials. The study was devoted to two types of concrete: one without wheat straw (C-W-WS) and another containing wheat straw (C-WS). The effect of wheat straw addition on physical and mechanical properties, thermal and hygroscopic properties, as well as the non-air-conditioned thermal environment and energy consumption of buildings, had been discussed. The results indicate that the maximum reduction in the thermal conductivity of C-WS is 13.70 % by altering the straw content and particle size. Moreover, the results of using DesignBuilder software and its application on external walls made of these materials show that, in the case of C-WS, the daily mean indoor temperature is improved compared to the case of C-W-WS. In addition, based on Adaptive Predictive Mean Voting (APMV), buildings with C-WS external walls achieved a mean improvement of 4.37 % in meeting thermal environment standards compared to buildings with C-W-WS external walls during summer design week. Moisture absorption by external walls increased energy consumption, and the annual air conditioning energy-saving of the Chongqing rural residence with C-WS external walls was 3.12 %. [ABSTRACT FROM AUTHOR]

This paper proposes a transfer learning (TL)-based control-oriented model development framework. In particular, this study examines the transferability from virtual (source) to existing (target) buildings to overcome the data imbalance issue of the data-driven approach. The target system is a cooling system comprising two supply air fans and four condensing units. First, synthetic data rich enough to provide fundamental knowledge about the target system were generated using the EnergyPlus model. A data-driven model was subsequently developed to learn the underlying dynamics of the system. By adopting TL using an imbalanced dataset measured from the target system, the knowledge that the model learned from the virtual data was transferred to the target system of the existing building. The results showed that the transfer learning model could accurately describe the dynamic behavior of the target system and predict the supply air temperature with marginal errors (CVRMSE: 5.4%, MAE: 0.96 ℃). In other words, the TL from virtual to existing buildings can overcome the data imbalance issue for developing a reliable data-driven model. [ABSTRACT FROM AUTHOR]