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[Display omitted] • The state of art of wet substrate materials is presented. • Five materials were tested and compared to Kraft paper as wet substrate. • Absorption, evaporation and moisture-wicking capacities were determined according to norm NF ISO 20158, NF EN ISO 9073-6 and AATCC 198. • Taking into account the cost, our results suggest that Fibre fabrics (tight straight weave) is a good choice for evaporative cooling Pad purpose. Due to the ever-growing demand for air-conditioning to bring the indoor air temperature to a comfortable level despite the unreasonable electricity consumption, research is more oriented towards techniques in connection with new methods enabling more energy savings and less adverse environmental impacts. Dew point evaporative cooling systems hold among the most promising. Their ability to use the evaporation of water to absorb heat from the air in order to lower its temperature below its wet bulb level without adding humidity, is an extremely low-energy and eco-friendly cooling principle. The properties of the material used in the construction of the wet channel surface in an evaporative cooler, i.e, its moisture wicking ability, diffusivity and evaporation capacity, can alter cooling efficiency and performance considerably. The irregular fibres help to divert moisture and enlarge the wet area, thus promoting evaporation. A measuring device was mounted to test the capillary rise of evaporative fabrics, which is one of the techniques used to assess the liquid moisture transmission performance of fabrics. A variety of materials (fabrics) woven from different types of fibres have been experimentally tested and compared to Kraft paper, which is commonly used as a wet surface medium in evaporative cooling systems. The wicking rate of fabrics obtained from the test device correlated well with the NF EN ISO 9073-6. Consequently, the test device clearly demonstrated differences between the fabrics used in the study, and can be used to determine vertical wicking behaviour of fabrics. Most textile fabrics have been found to have superior properties and qualities in terms of moisture wicking ability than the Kraft Paper. Also, that the type of weaving and the compactness of the fabric improved the capillary rise. Compared to Kraft paper, the absorbency of some fabrics has been found 160% to 355% higher. An estimation evaluation regarding both moistures transfer and mechanical properties revealed that two of the fabrics were the most suitable for evaporative cooling applications. [ABSTRACT FROM AUTHOR]

A significant focus of research and new technologies for reducing energy use in buildings is on operating the systems more efficiently during the times when the systems are active. However, there is a large potential for energy savings in determining the periods when the systems should or should not be active. This scheduling aspect of operation is often overlooked even though relatively simple solutions can unlock substantial energy savings. This paper describes a smart building start (SBS) algorithm that considers multiple zones in a building to determine individual schedules for room controllers as well as the central systems based on solving a simple optimization problem. Application of the SBS algorithm to multiple interconnected systems enables a staggered start-up that minimizes peak loads and also ensures comfort is within a target range with minimal system run time. The SBS algorithm extends the capability of traditional optimal start and is designed to be simple to deploy and robust. Simulation results as well as results from tests in a real building with a VAV system are presented. The presented algorithm is applicable to any type of building with a zonal or multi-zone HVAC system. To function, it needs to be able to change setpoints in rooms and monitor room temperatures, as well as, if desired, turn the central system on or off. [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]

In order to improve the thermal insulation effect of the PCM in the hot summer environment, this paper applies a combination of phase change energy storage technology, ventilation technology, and solar power generation technology. The zero energy operation of the device is realised while improving the roof insulation effect. In addition, three experimental models of the same size were constructed and the ventilation layer was innovatively designed. The experimental studies were carried out under two different conditions of outdoor environment measurement and indoor environment simulation for three consecutive days. The results show that the temperature of the inner surface of the roof and the indoor air temperature of Model 3 are lower than that of Model 2 under both experimental conditions, while the temperature of the inner surface of the roof and the indoor air temperature of Model 2 are lower than that of Model 1. In addition, the thermal performance of Model 3, such as the maximum reduction of the indoor temperature (MTR), the decay factor (DF), the time lag coefficient (TL), and the reduction of the heat flux (HFR), is also significantly better than that of Model 2. Moreover, the energy saving rate of Model 3 can reach 59.4%, which is 10% lower than that of Model 2. In conclusion, the new phase change ventilated roof proposed in this paper has better energy saving effect. [ABSTRACT FROM AUTHOR]

This paper introduces a digital simulation workflow that calculates annual eye-level illuminance and energy demand from daylight and electric lighting sources for an array of view positions across 9-channels of spectral data. This Radiance-based workflow combines parts of the LARK spectral lighting code to offer better spectral resolution and accuracy when computing melanopic lx to evaluate the non-visual health potential for view positions within a digital model. The authors have implemented a series of annual climate-driven simulations and post-processed time-series of resulting data using the R statistical analysis software to compute Equivalent Melanopic Lux (EML) and energy demand (kWh) for an array of view positions and view directions in a digital model. This allows us to compute the energy demand of a given lighting and shading control scenario, which is optimized by hourly daylight availability to meet a recommended level of melanopic lx. This combined workflow uses digital models of an interior space to evaluate the annual potential of non-visual light exposure under different architectural, lighting, and shading control scenarios. This paper demonstrate the utility of this workflow by evaluating a luminaire-level lighting controls (LLLC) system to meet eye-level light exposure recommendations from the WELL Building Institute Feature L03 Circadian Lighting Design and compare the energy demand between targeted melanopic lux thresholds and shading control scenarios. [ABSTRACT FROM AUTHOR]

The increasing population in Indian cities is leading to an increased load on the built environment in urban areas which is prone to effects such as the reduction of outdoor open spaces and decline of environmental quality. In this line, focus on outdoor thermal comfort studies has become an important aspect. The current status of outdoor thermal comfort in India and its comparative scenario are not available to researchers. In this paper, a critical review is performed on the studies published in the past 10 years, those focused on a built environment from micro-scale to macro scale. For the review, 18 papers were finally selected after performing the searches in major databases and filtering out irreverent studies. Following this, the shortcomings and gaps are identified and the future scope is stated. Most importantly, future studies are necessary to cover missing climatic regions and urban areas which are not yet been explored. The review also seeks the focus on the standardization of thermal comfort indices and the range of neutral values as per climatic regions. Along with the quantitative studies, a qualitative approach is also required in the research. Furthermore, future studies need to include psychological aspects such as adaptation and acclimatization. This review is the foremost study of the Indian context. It will act as a reference for researchers, architects, planners, and urban designers to improve their knowledge of outdoor thermal comfort and understand the gaps that need to be addressed. • Built environments are represented with geometrical parameters such as SVF, H/W, LCZ and orientation. • Tmrt and PET have commonly evaluated indices whereas UTCI, DI, THI, and SET are also evaluated by some studies. • Most of the studies estimated the thermal neutral values along with the neutral range • Some studies focused on the effect of physical and climatic parameters on thermal comfort level. [ABSTRACT FROM AUTHOR]

With the goal to reach net zero carbon emission, countries around the world are expected to switch from fossil fuel to more eco-friendly alternatives. This suggests that heating, transport, and energy sectors will be more dependent on electricity. Heat pump technology is expected to be the most common and clean technology to be used for heating in buildings due to its Coefficient of Performance. Air-source heat pumps are the most common type due to their ease of installation. This paper investigates how different climates around the world would affect the air-source heat pump electricity consumption and its defrost cycles; and hence the potential overall effect on the grid. A novel heat pump simulation model has been developed to understand the behaviour of the heat pump in cold weather scenarios. An experimental validation has been implemented to ensure the accuracy of the simulation model. Global case studies of cities from around the world including Amsterdam, Copenhagen, Helsinki, Stockholm, Vancouver, Ruse, Moscow, Reykjavik, Harbin and Nottingham are selected to assess the defrost cycles of air source heat pumps. The innovation of this research is in the in-depth investigation of heat pump defrost cycles. Electrifying the heating sector is expected to increase electricity consumption significantly, especially in cold weather. A detailed case study of the UK is presented with heat pumps and the examination of the impact this would have on the electricity grid is presented. Heating demands for buildings in cold weather was also simulated to calculate the required heating demand. The results show that the wide implementation of heat pumps in the UK, for example, would increase the total daily demand of electricity by approximately 144% relative to the present level of grid energy demand. In addition, the average daily energy consumption would increase by roughly 106% over the cold season. The findings are critical, and the novel methodology is applicable to many countries on global level in relation to the future effect of heat pumps on the grid and predicting the power demand needed by the additional heat pumps based on geographical location, u-values of building elements, and heating demand. It is crucial for a household to improve the insulation of their dwelling as this could considerably reduce the power demand by the heat pump and therefore the overall electricity consumption. This paper aims to inform future technology developers and policy makers regarding the expected effect of heat pump technology on the grid and the possible sudden peaks in demand. [ABSTRACT FROM AUTHOR]

• A repository on occupant-centric control and operation case studies is proposed. • A survey was developed to collect and present the data systematically. • The current database includes 58 OCC case studies with different approaches. • Results show a lack of studies on lighting and building operators. • Occupant modeling is more commonly implemented for responsive controls. Occupant-centric controls (OCC) and operations have emerged as a key concept in shifting the focus from conventional building- (or system-) centric operations to a more occupant-centric approach. Despite the potential of OCC to meet occupants' demands and bridge buildings' energy performance gap, its implementation in real-world settings has been limited. In addition, there is a lack of standardization in methodologies and terms to facilitate meaningful comparisons among case studies. Therefore, this paper aims to present a repository of OCC case studies, offering a platform for standardization and presenting key information about practical implementations of these strategies in real-world scenarios. To accomplish this, descriptors, terms, and concepts about OCC case studies were discerned through a literature review. These elements were systematically integrated into a structured survey to capture comprehensive information on OCC field study implementations. This paper provides an overview of the survey structure and insights into the current dataset, which comprises a total of 58 OCC case studies. By publishing the case study repository, we intend to establish standard categories for OCC strategies and to offer researchers and practitioners a database through which they can understand trends and possibilities for implementing OCC strategies. [ABSTRACT FROM AUTHOR]

Buildings' operation constitutes 36% of the German energy consumption. Currently, operators lack the knowledge on energy-saving techniques. There is a shortage of cost-effective and easily-implementable solutions to evaluate building performance. The cause of this problem lies with the semantically heterogeneous operational data used in technical applications. Integrating the data into monitoring applications demands substantial and costly manual efforts. This paper presents a method that enables automated generation of technical monitoring applications for existing buildings. The method outlined represents existing automation stations as digital twins and employs artificial intelligence to map the heterogeneous data to a standard and create semantic digital twins of buildings. The paper introduces a method using natural language processing for the semantic processing of data. The developed method involves a four-stage process for classification of data points, which are subsequently mapped to a uniform vocabulary. To classify the data points, language models were trained on a created dataset of 54,125 data points. Following successful training, the models can process semantically heterogeneous data points. The results, demonstrating an average F1-Score of over 95%, indicate that the developed method is suitable for automating data point mapping. The models were implemented as an Industry 4.0 service and integrated into an application. • Using Natural Language Processing to map semantically heterogeneous data points to a vocabulary. • Creating semantic digital twins of buildings, their systems and technical components. • Enabling interoperability of technical systems through semantic matching and digital twins. • Automated generation of technical monitoring applications for existing buildings. • Implementing an Industry 4.0 environment as a framework for building applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • Harnessing energy flexibility is a complex socio-economic-technical challenge. • Research opportunities are identified in five areas and three development stages. • Most tools/methods are limited to the operational stage and the building level. • Outcomes from pilots are not sufficiently disseminated nor used in future research. • A systemic approach is required for effective energy flexibility exploitation. This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases: market and policy, early planning and design, and operation. We review methodologies and tools and discuss barriers, challenges, and opportunities, incorporating policy, economic, technical, professional, and social perspectives. Although various legal and regulatory frameworks exist to foster the development of energy flexibility for small buildings, financing mechanisms are limited with a significant number of perceived risks undermining private investment. For the early planning and design phase, planners and designers lack appropriate tools and face interoperability challenges, which often results in insufficient consideration of demand response programs. The review of the operational phase highlighted the socio-technical challenges related to both the complexity of deployment and communication, as well as privacy and acceptability issues. Finally, the paper proposes a number of targeted research directions to address challenges and promote greater energy flexibility deployments, including capturing building demand side dynamics, improving baseline estimations and developing seamless connectivity between buildings and districts. [ABSTRACT FROM AUTHOR]

In a global effort to reduce carbon emissions from the building sector, reliable on-site thermal intrinsic performance measurements could positively contribute to reducing the performance gap in newly constructed or retrofitted buildings. A measurement is considered reliable when it is both accurate and precise, meaning it is, on average, on target with a finite, ideally low level of uncertainty. This paper aims to assess the reliability of a Heat Transfer Coefficient measurement using the Sereine method. The paper conducts an extensive uncertainty analysis, defining a new expanded coverage interval and thereby introduces an innovative approach to addressing uncertainties related to weather and building type. This approach relies on an extensive set of numerical experiments to quantify the measurement uncertainty under diverse measurement conditions. An experimental campaign in an externally insulated house demonstrates good agreement among all Sereine tests. It reveals that the expected performance consistently falls within the final widened coverage interval, which suggests reproducibility of the measurement. Given its thorough and transparent uncertainty quantification, the Sereine method could be a cornerstone for reliable performance measurement and, consequently, a valuable tool for future performance contracting protocols or building certification. • Thorough uncertainty analysis and quantification necessary for HTC measurement. • Outdoor temperature and building type influential on HTC measurement precision. • The Sereine method develops first ever systemic uncertainty estimation. • Reproducibility of the Sereine HTC measurement achieved in an experimental campaign. [ABSTRACT FROM AUTHOR]

This paper describes the development of community energy management systems (CEMS). A CEMS allows optimal energy sharing within energy communities, as it is a central system that makes the global management of the entire community. The proposed CEMS is based on mixed-integer linear programming (MILP), operating under the receding horizon concept of Model Predictive Control (MPC). A systematic classification of electric appliances, the use of external information such as weather information and energy prices, as well as the use of intelligent forecasting techniques enables the proposed approach to achieve an excellent efficiency. It also allows for an easy installation of as well as a smooth scaling with an increasing number of houses. The system is tested in a real community in Algarve, Portugal. Different simulations are compared to experimental operation and include cases with and without sharing of energy, different resources allocated to the houses considered, and the use of different tariffs. CEMS formulations include sharing of energy without restriction, as well as employing different allocation coefficients strategies. The results show that for the community under study when managed by CEMS such as the one presented in this paper, it would result in significant cost reductions when compared to the case where there is no energy community. [ABSTRACT FROM AUTHOR]

[Display omitted] • The paper reviews 43 articles defining methodologies for climate classification. • The paper provides overview of climate classification and validation methods. • Need of standard methodology or nomenclature for climate classification. • The optimal approach for conducting climate zoning remains a topic of debate. Climate classification simplifies the representation of expected climate conditions by eliminating the need to describe each climatic parameter. Climate classification is frequently used as a basis for recommendations through codes and standards for buildings because a given climate has nearly similar design conditions. Furthermore, building energy efficiency programmes are designed based on the climatic classification to determine appropriate design strategies to eliminate the under-design or over-design of buildings for similar climatic regions. This necessitates the use of realistic and reliable climate classifications. Over the past two decades, numerous research studies have proposed climate classification methods. However, there is a lack of universal acceptance of climate classification methodology to balance complexity and accuracy. This research provides an overview of various climate classification methods and indicators used to assess the significance of climatic conditions. Moreover, this study highlights pressing issues, proposes a feasible climate classification methodology, and offers substantial possibilities for future study. This study organizes and evaluates the various classification and validation methods used by researchers, variables, and the granularity of data used for the classification. The usual criteria for defining a climate are temperature, relative humidity, cooling-degree days, heating-degree days, and solar radiation. About eighty-five percent of the classified climates are using at most three variables. The number and characteristics of climate zones vary with changes in the resolution of climate data, classification methods, and the inclusion of different sets of parameters. Clustering methods are becoming more important for climate classification as these methods can handle multiple variables more accurately. To tackle the complex relationship between climate and building thermal design, it is recommended to use climate variables with cluster analysis and validation based on thermal loads. This paper will give researchers an aerial perspective of the scope and direction of ongoing research in this field. [ABSTRACT FROM AUTHOR]

• Thermal efficiency of glazed balcony was examined considering different parameters. • The effect of heat retention of insulation on the energy saving was evaluated. • Inside temperature was increased with ability of black surface to absorb radiation. • Thermal efficiency of balcony was enhanced by 42.8% using recommended applications. This research was conducted to evaluate energy saving and the thermal efficiency in glazed balconies and sunspace areas. The effects of double-glazing, black walls and black carpeted floors, and insulation material on the thermal efficiency of residential balconies in the cold seasons of the year were investigated. For this purpose, the interior sun walls were covered with recycled black-painted papers, and the effect of the applied insulations on the thermal performance of the glazed balcony were investigated. In addition, a model of the glazed balcony was simulated using the ANSYS/Fluent program. By numerically analyzing the simulated model, the temperature distribution inside the balcony and the air velocity due to the natural convection in the domain volume have been visualized. It was found that the maximum thermal efficiency was achieved as 42.8 % for the case where double-glazing has been applied and walls are insulated and covered with recycled black papers. Regarding the differences between the balcony air temperature and the outdoor temperature, the temperature difference was 14 °C for the situation where all insulation measures were taken and effective absorbers were used, while this value was around 9.3 °C in the case of testing only glass window application. As a consequent, by considering larger sunspaces in large buildings such as university faculties, libraries, and offices, this study reveals more research fields to be investigated and using solar energy more efficiently. [ABSTRACT FROM AUTHOR]

[Display omitted] • A wireless-energy green city concept integrating WPT, renewable energy, and infrastructure is proposed. • Security-electrification, digitization, and intelligence are bolstered by the incorporation of WPT into urban settings. • The SDGs are advanced by the wireless-energy green city's energy, environment, and social economy benefits. The significance of energy stability in urban development has increased as the use of intelligent devices continues to expand and extreme weather poses a threat to the integrity of electric networks. Wireless power transfer technology is well-known for its dependability, security, and adaptability, and has reached billions of market share in China alone in the cell phone and electric vehicle sectors over the past decade, with the electric vehicle wireless charging market expected to grow at a compound annual growth rate of over 30% by 2023. Combining WPT with renewable energy, electric vehicles, and recharge infrastructure is a promising new application for smart city development. This paper examines the history, characteristics, and six classifications of WPT technology through the lens of urban development, in addition to its successful implementations in seven contexts, such as portable devices and transportation, based on a survey of 161,425 papers. We propose a dependable and efficient research-to-application life cycle for high-power WPT operations, which is capable of achieving efficiencies of approximately 90 percent at approximately 50 percent of the original cost. In addition, the significance of incorporating WPT technology, renewable energy sources, and urban infrastructure into the design of sustainable cities is highlighted. This study determined that WPT is advantageous for promoting the construction of space and ground photovoltaic plants, as well as photovoltaic rooftops, and also serves to accelerate the promotion of electric vehicles, smart devices, and charging roads. The findings of this study cast light on three problem areas and six future prospects of WPT, as well as its multifaceted benefits in terms of energy, the environment, and the social economy of sustainable cities. [ABSTRACT FROM AUTHOR]

The built environment accounts for approximately 38% of global emissions. The evaluation of returns linked to investments in building decarbonization is impeded when confronted with uncertainties surrounding future energy prices, building emission regulations, and real estate market conditions. This paper develops an integrated framework that combines energy modeling with investment analysis to support the adoption of decarbonizing technologies under a context of uncertainty. The integrated framework includes projections of costs and benefits associated with the adoption of technology (i.e., changes in rental cashflows, vacancy rates, maintenance and energy costs) under a wide range of potential pathways of the energy markets and regulatory environment, and local real estate market demand for green buildings using Monte Carlo simulations. Additionally, this paper develops the approach to investigate the potential value of flexibility in designing for building electrification (i.e., the ability to fully electrify gas heating systems at a later year). We illustrate the use of this approach in a case study of a new office building in New York City, faced with design choices between natural gas and electrified heating systems. We consider 10,000 scenarios changing future greenhouse emission penalties, investments in the local electric grid, local real estate market conditions and energy prices. In approximately 60 percent of simulations, the most profitable decision for the building owner is to adopt a natural gas-powered heating system. However, adopting a building design that provides a building the flexibility to fully electrify at a later date is more profitable than a natural gas-heating building in 96 percent of scenarios. A sensitivity analysis shows that the most influential parameter in the valuation of a design option is the size of the green premiums associated with improved energy efficiency. [ABSTRACT FROM AUTHOR]

• Automatic MiC envelopes and layout generation by a novel SSG-MOO method. • Multi-objective optimization formulation for passive MiC design. • A pilot MiC study in Hong Kong produced 5 selected Pareto optima out of 625. • Up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement against the baseline. • A multi-level analysis of results and design strategies for practitioners. Modular-integrated Construction (MiC) is an emerging construction technique promoted in the building sector for high productivity and low waste emission in the construction phase; yet, the standardized modules also bring new challenges, such as balancing passive energy efficiency and spatial daylight autonomy, to the operational phase. This paper proposes a Symmetric Skeleton Grammar-based Multi-Objective Optimization (SSG-MOO) method to formulate parametric MiC envelopes and detailed layout, with the two objective functions being energy efficiency and interior daylight performance in the operational phase. Pareto optima of the SSG-MOO, computed by the Non-dominated Sorting Genetic Algorithm II, are generally verified and analyzed in three levels, i.e., MOO's solution space, SSG layout, and MiC design parameters. A case study of MiC residential building in Hong Kong demonstrated the SSG-MOO method through five new passive MiC designs (i.e., spatial reorganization of three architectural modules, and parameter tuning of the envelops and corridors), achieving up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement compared to the baseline design. The contribution of this paper is two-fold, including a novel and sound SSG-MOO formulation for parametric MiC designs, and offering time-efficient and evidence-based design tactics for MiC designers and industrial practitioners to push boundaries of MiC. [ABSTRACT FROM AUTHOR]

[Display omitted] • The LCA methods and findings of 41 papers on air-conditioning systems are examined. • Retrieved studies presented discrepancies in the LCA goals and scope definition. • The global warming potential (GWP) was the most evaluated indicator across papers. • Grid-connected electrical heat pumps generally had a higher life cycle GWP. • Improvements related to AC energy sources and other life cycle aspects are proposed. Air conditioning (AC) has become the fastest-growing energy end-use in buildings worldwide, and its adoption is expected to increase further due to various socioeconomic factors and climate change. Despite this, the decarbonization of space cooling is progressing slowly; hence, the emissions burden at each stage should be examined in detail. Given this context, it is crucial to assess the environmental performance of AC systems by considering a comprehensive approach like the Life Cycle Assessment (LCA). While several studies have already targeted the LCA of AC systems, a clear overview of those studies' methodologies, assumptions, and results is still lacking. Therefore, this systematic review examined the methods and key findings of 41 peer-reviewed articles. Although the results revealed discrepancies among the goals and scope, impact assessment methods, and indicators, most articles predominantly focused on the global warming potential (GWP) impact, and its reported quantification was further analyzed. It was observed that grid-connected vapor-compression heat pumps/chillers (conventional systems) presented higher GWP impacts than other AC systems powered by renewables, which in contrast, exhibited higher impacts in some toxicity and resource depletion-related indicators. However, the share of GWP showed different distributions, as the pre-operation emissions of solar-powered AC systems (either PV or thermal) were higher than those of conventional systems, mainly driven by reduced energy consumption usage. This review concludes with some recommendations for each stage of the life cycle. Since the operation phase generally showed the highest impact, more efforts should be devoted to accelerating the transition to a 100% clean electricity power system at a national scale. In contrast, the need to power AC systems with on-site renewables should be carefully assessed based on the context. [ABSTRACT FROM AUTHOR]

As the built environment is among the main contributing sectors to climate change, it is needed to investigate new paradigms to push decarbonization efforts towards the ambitious objectives defined internationally. It is a shared understanding that shifting the perspective from the single building to the district perspective is required to fully take into consideration the complexity of all interactions undergoing within the built environment, thus the concept of Positive Energy District emerged as a district with annual net zero energy import and net zero CO 2 emission working towards an annual local surplus production of renewable energy. In this framework, this paper explores the investigation of the potential for achieving the level of Positive Energy District in a group of non-residential buildings in Balaguer, Catalonia, Spain. These buildings, occupying 8,825 m2 in the city centre, require significant refurbishment for improved energy performance. The analysis includes building energy modelling and simulation, renovation studies, several alternative balance calculations, and carbon emissions assessment. The paper also considers mobility and embodied energy and their impact on energy/carbon balances. The results show that Positive Energy Districts carbon and primary energy balances are not met with rooftop PV installations when retrofitting an existing district towards the Positive energy target but further significant PV areas (roughly + 50%) are required to meet merely the use stage balances: negative results are traced when mobility and embodied energy are computed. A formulation alternative to the simple mathematical balance to facilitate the diffusion of Positive Energy District as catalyst of urban decarbonisation could be needed, including context factors and alternative systems (e.g., rating systems). [ABSTRACT FROM AUTHOR]

Increasing use of volatile renewable energy sources causes challenges in balancing supply and demand. Therefore, demand-side flexibility has rising importance for system operators and balancing authorities. Flexibility management methods are needed to integrate loads like ventilation systems of different buildings (e.g., residential and commercial) into flexibility service. However, the available methods described in research papers require further development for implementation in practice. Heating and cooling systems have received much attention from researchers, but the potential of ventilation systems has been left out of focus. Therefore, this paper provides a complete set of novel flexibility management methods for ventilation systems created from an aggregator's viewpoint. The flexibility is quantified through capacity (e.i. the amount of power consumption that can be altered), forced ventilation rate duration, and the tendered price for the service. The proposed methods were tested on a building model constructed and simulated in IDA ICE. The data processing and flexibility management methods were applied in MATLAB. Two types of ventilation systems with different sensor configurations were considered: constant and variable air volume. Forced ventilation rate duration is calculated using energy and mass balance analysis where the root means squared error was 10 to 33 min, depending on the system type, measured parameter, and sensor location. The flexibility service pricing model was tested on the 2022 years' manual frequency restoration reserve (mFRR) activation and balance energy market data. [ABSTRACT FROM AUTHOR]

K-12 schools are the largest energy consumers in the public sector, with their HVAC energy consumption representing the largest portion of their total energy use. While transitioning these schools to grid-interactive HVAC system operation through advanced controls offers significant financial and environmental benefits, and model predictive control (MPC) has been identified as a promising solution to achieve that, very few MPCs are affordable and have been deployed in K-12 schools. This situation raises concerns about the unclear real-world benefits of MPC technology among facility managers and industries. To address this gap, this paper presents a low-cost MPC solution that requires minimal control infrastructure costs and a unique field demonstration at a K-12 school, conducted for both cooling and heating seasons. This work adopted a previously developed MPC and extended it for use in the school application. The MPC aims to coordinate multiple packaged units to eliminate unnecessary peaks and shift cooling or heating loads in response to grid signals based on load conditions, while maintaining thermostat temperatures within school-defined bounds. Throughout the field tests, the MPC achieved a 24% reduction in peak demand during the cooling season and shifted cooling or heating loads by up to 16% in response to the school's utility tariff, considering load conditions, while also allowing end-users to override thermostat setpoints. The paper also discusses the limitations of this study and future research directions for better performance of the MPC at K-12 schools. • First MPC demonstration in K-12 school buildings for peak demand reduction and load shifting. • Experimentally demonstrated a scalable MPC for multiple RTUs without hardware retrofit. • Hierarchical MPC is improved in consideration of practical challenges such as user's override and poor data resolutions. • Achieved 24% peak demand reduction and showed 16% of load shifting potential. • Share lessons learned during the demonstration of practical challenges. [ABSTRACT FROM AUTHOR]

[Display omitted] • Incentive initiatives on energy-efficient renovation of existing buildings in China were analyzed. • The dynamic evolution of incentive initiatives was analyzed from different perspectives. • A critical analysis of representative initiatives in two batches of pilot cities was conducted. • Implementation barriers and limitations on energy-efficient renovation of existing buildings were summarized. • Recommendations and pathways of promoting related incentive initiatives were proposed. Under China's national strategy of carbon neutrality by 2060, it is urgently necessary and challenging for the governments to proactively explore policy tools to facilitate energy-efficient renovation of existing buildings. Currently, a considerable number of studies have been conducted on building energy-efficient renovation and its derivative topics, however, a comprehensive overview on incentive initiatives related to existing renovation practices in China is still scarce, such as a lack of critical correlation analysis between national and local initiatives, a lack of the synthesis and critique towards the latest policies and related achievements, and inadequate generalization of the diverse and multi-layered barriers and challenges in building energy-efficient renovation practices. To address these issues, this paper adopts a diversified policy segmentation approach to deeply analyze the dynamic evolution of the incentive initiatives from both national and local level perspectives, as well as to establish the related network of policy linkages between national to local, and between different localities. In addition, this paper presents a critical analysis on representative initiatives in two batches of pilot cities, and proposes good practices and valuable experiences for building energy-efficient renovation. Finally, this paper further summarizes and discusses the barriers to building energy-efficient renovation from four perspectives: governments, householders, enterprises and research institutions, and proposes a series of targeted and feasible pathways and strategies. This study can provide theoretical guidance and targeted recommendations for the formulation of policies, standards and regulations for building energy-efficient renovation in China. [ABSTRACT FROM AUTHOR]

[Display omitted] • Localized heating systems are a proper solution for providing thermal comfort. • Providing personalized heating by radiation or conduction is more convenient, so the inhaled air is kept cool and pleasant. • Local radiant heating systems directly heat the body without heating the environment, leading to lower energy consumption. • With local radiant heating systems, proper thermal comfort and sensation can be achieved even at low air temperatures. Localized heating systems are a proper solution for providing thermal comfort while lowering energy consumption in cold environments. Local radiant heating systems provide thermal comfort at lower air temperatures than convective systems which heats air, resulting in cool and pleasant inhalation air. This paper reviews experimental studies regarding local radiant heating systems considering thermal sensation and comfort. These systems can heat body directly, often with a big view factor toward the body. They do not necessarily heat the air and environment, leading to lower energy consumption. Other types of heating systems combined with local radiant heating systems are also reviewed in this paper. According to reviewed studies, localized methods improved thermal comfort, and the same thermal sensation could be achieved in lower air temperatures, especially when using local radiant heating systems. To design local radiant heating systems properly, improvement of existing local thermal comfort and sensation modeling based on radiant heat transfer is needed. [ABSTRACT FROM AUTHOR]

Post-occupancy evaluation (POE) is necessary to evaluate the building's performance in order to formulate future design development after its utilisation. This paper evaluates indoor thermal performance, sound pressure level, and illuminance in a multipurpose auditorium by combining zoning and activity-based POE. The evaluation was done in three activities: (A) student graduation, (B) theater performance, and (C) public lecture with different types of clothing preferences. Three different zones were used to define the precise location of the building improvement. Field measurements were taken for thermal performance, including Operative Temperature (T op), Humidity (Rh), Sound Pressure Level (dB), and Illuminance (lux). User perceptions were collected from 171 respondents (89 men and 82 women). The field measurement showed that average operative temperature varied across the activities with 26.15–25.68 °C, 25.47–24.95 °C, and 23.74–23.17 °C on activities A, B, and C, respectively. Activity A's (1 clo) room's operative temperature showed that 65% of data are outside the ASHRAE standard, indicating that the room condition is unsuitable for 1 clo. Activity A's TSV shows a slightly warm sensation (0.21 to 0.93) and is still in the comfort range. With the same clothing value (0.6 clo), Activity B's T op indicates a more suitable condition than Activity C's. The average TSV in Activity B indicates slightly cool (-0.54 to −0.90), with the lowest TSV found in Activity C (-1.09 to −1.41). Although TSV positively correlates with PMV , Thermal Preference (TP) revealed that the occupant expects cooler conditions in all activities. Sound pressure level and illuminance performed well according to all activities. This paper recommends chiller setting adjustment in the multipurpose auditorium based on activity characteristics. Combined POE based on zoning and activity can indicate which aspects of the auditorium need to be improved. [ABSTRACT FROM AUTHOR]

The paper proposes a two-stage home energy management system accounting for optimal energy dispatch, power profiles predictability, and building thermal behaviour. The first look-ahead stage aims at minimizing the energy exchanges with the upstream grid based on load and weather predictions. The second step adapts the controls in real-time in order to mitigate forecast errors while remaining as close as possible to the look-ahead commitments. The originality of the paper lies on the use of different granularities of the building models. Especially, this allows to account for the approximations in the system equations embedded in the predictive controllers. Furthermore, a real-time tuning of those model equations is performed based on the deviations observed on the state variables – errors between controller predictions and measurements. Ultimately the proposed approach allows to cope with two generic drawback of traditional model predictive control: i) environment forecast errors and ii) models approximations. Several simulations are carried out to under different weather conditions, demonstrating an average energy exchange reduction of at least 25% and better system predictability. The work is completed with sensitivities analysis of the controller settings, the system parameters and the quality of weather/load forecasts. [ABSTRACT FROM AUTHOR]

• Climate change and urban setting impact energy use of buildings. • Energy use is higher due to overlapping effect of climate change and microclimate. • A method is proposed to account for future and urban environment in thermal simulation. • The results can assist in sizing passive ventilation systems. Urban settings and climate change both impact on energy use and thermal comfort inside buildings. This paper first presents a study of changes in energy demand in residential buildings considering the overlapping effect of climate change and urban heat island intensity in two European locations; Cadiz (Spain) and London (United Kingdom), representing temperate and hot European climates and moderate and dense urban settings. Future-urban weather files were generated and simulations were run considering energy demand and indoor thermal comfort. In hot climate regions such as the one of Cadiz, future climate will increase the cooling demand and the additional impact of the UHI leads to a further increase of up to +28% of total energy demand compared to the current climate without considering urban effects. Future-urban weather conditions will be detrimental also for buildings in London, where the annual energy demand is predicted to increase by up to the 16% if future climate and urban effects are included. This is due to a higher increase in cooling demand compared to the reduction for the heating need. The paper also presents a method to take into account microclimatic conditions in naturally ventilated buildings, especially the effect of wind variations around the building which impacts natural ventilation rates. Air and surface temperature and wind speeds were studied using ENVImet and the resulting microclimatic conditions were used as inputs to the EnergyPlus Airflow Network model for the calculation of the building ventilation rates. It was found that ventilation rates are reduced (in comparison to meteorological weather files) and this reduction impacts negatively on internal operative temperatures. A thermal comfort analysis was carried out indicating that the selection of a suitable weather file and microclimatic conditions is essential for more accurate predictions of internal thermal comfort and will assist in the sizing of passive and active systems to avoid overheating. [ABSTRACT FROM AUTHOR]

The micro-grid with combined cooling, heating, and power supply system (CCHP) is a complex integration that assembles various distributed generators (DGs). The motivation of this paper is to select a reasonable configuration of DGs for a CCHP-MG by means of uncertainties models, improved operation strategy and algorithm. The DGs in a CCHP-MG are composed of wind turbine (WT), photovoltaic (PV), battery energy storage (BES) system, biomass gasification (BG), Stirling engine, heat exchanger, absorption chiller, electric heater and cooler. In terms of uncertainties models, the uncertainties of variations in wind speed, solar irradiance, temperature, and load demand are considered with 8760 h of a whole year. The paper proposes a multi-objective optimization model for the CCHP-MG system under three objectives associated with the minimized system cost, greenhouse gas (GHG) emissions and power waste. In this study, the double-layer operation strategy is raised for replacing the conventional objective of loss of energy supply probability (LESP) in the CCHP-MG system. Based on the non-dominated sorting genetic III (NSGA-III) algorithm, methods of 1/8 unit sphere and angular distance are proposed for an improved NSGA-III algorithm (INSGA-III) to increase computational efficiency and avoid local convergence. Finally, the multiple analysis and comparisons demonstrate that the cooperation of the double-layer operation strategy and INSGA-III has better performance with superiorities of economy and reliability. The results in the case study show that the system cost, GHG emission, and energy waste of the selected CCHP-MG system respectively reached 85.23, 72446.7, and 7690.46. The study confirms that the improved operation strategy, INSGA-III algorithm, and multiple analysis framework can be extended to design CCHP-MG system in other places. [ABSTRACT FROM AUTHOR]

In a context of massive renovation of residential housing, stakeholders need decision-support tools based on knowledge of the current building stock and an accurate simulation of energy demand. For this purpose, we developed a val- idation/calibration method on a territorial/national scale in order to represent the real consumption of housing. This methodological approach provides (1) more reliable identification of energy-saving measures (changes in technology or behaviour) and (2) improved knowledge of the energy simulation tool and its post-calibration performance for optimisation issues. The main contribution of the calibration method described in this paper is the geographical scale concerned: all French residential housing has been modelled, simulated and calibrated with national data (geometries and attributes) on buildings. Furthermore, some occupants' socio-professional characteristics have been taken into account to reflect their actual energy behaviours. This is different from traditional approaches that focus only on a few buildings or archetypes. This paper also describes the application of this methodology on an Open Source simulation software in order to be easily verifiable and usable. This linear model will also be used to optimise renovation solutions at territorial scale in future work. All data used in this paper are Open Data and thus available to the scientific community. This method enabled more than 18 million buildings to be calibrated while reducing the Normalized Root Mean Square Error, between simulated and real energy annual consumption, from 52% to 24% for gas and from 24% to 15% for electricity. In addition, the user of the method is free to prioritise either the maximum error reduction or the number of calibration coefficients if a simpler model is desired. This paper also discusses the results obtained from this method for future improvement. [ABSTRACT FROM AUTHOR]

This paper analyzes electricity use in buildings and explores key research questions related to assessing, rating, labeling, and benchmarking building energy use. The analysis reviews annual Energy Use Intensity (EUI) data from 3060 fully electric-powered buildings across six categories: residential, educational, offices, hospitals, hotels and restaurants, and wholesale and retail trade services. One-fifth of the analyzed buildings is derived from an energy dataset that offers a higher time-scale resolution, providing more detailed insights into energy usage patterns. The analysis employs various factors, including the seasonal variation factor (SVF), daily variation factor (DVF), monthly factor (M f), and hourly factor (H f), to gain a detailed understanding of actual energy usage at different time scales. The findings indicate that electricity use is less sensitive to seasonal variations than other energy carriers that provide heating and cooling. The paper provides hourly and monthly peak load percentages (PPL), which can indirectly assist in assessing hourly schedules for building energy performance simulations. The monthly energy use pattern varies between building categories, with high-energy months alternating between winter and summer. For non-residential buildings, high-energy hours correspond directly to the daytime, while for residential buildings, there is a shift towards the evening, and the daily amplitude is lower. Furthermore, the analysis of daily and monthly variability of heating energy carriers, even if non-electric, sheds light on the implications of electrification for these loads. • The study evaluates annual electricity data from 3060 fully electric-powered buildings. • Median EUIs are meaningful energy performance benchmarks by building category. • Residential and educational buildings exhibit EUIs of 90-120 kW h/m2. • Offices and hospitals demonstrate EUIs of 160-170 kW h/m2. • Monthly and hourly factors establish energy benchmarks for finer time resolution. [ABSTRACT FROM AUTHOR]

As the global population becomes more concentrated in urban environments, higher numbers of people will be exposed to urban air pollution. The environmental and human health benefits of green roofs are widely recognized. The aim of this paper is to promote green roofs as an effective passive technique for pollution mitigation and adaptation to climate change. During the heating season, the ambient concentrations of PM1, PM2.5, and PM10 were measured above a green roof and a reference roof on a school building, located in New Belgrade, the second-most populous municipality and business center of Serbia's largest city. The percent reduction of PM10, PM2.5 and PM1, in January 2020, above the green roof compared to the reference roof was 7%, 16.6%, and 17.6%, respectively. The results show that lightweight green roof improve air quality in terms of PM concentrations for all months considered. In this paper, correlation analysis and the use of Pearson's coefficient were used in the process of analysis to determine the relationship between PM10, PM2.5, PM1, and ambient parameters: relative humidity, ambient temperature, and wind speed. It was found that the statistical correlation expressed by the Pearson coefficient between all PM particles and wind speed was statistically significant in all observed months except September. Also, the degree of significance of the correlation between PM particles and humidity and temperature of ambient air varies by month. [ABSTRACT FROM AUTHOR]

Building performance simulation is a powerful tool applied both in research and for building design including heating, ventilation, and air-conditioning systems. Several research studies have used this tool to investigate the potential of energy savings measures for swimming facilities. However, in these facilities the technical complexity is a considerable challenge in modelling since many complex phenomena occur in the various sub-systems. The building industry traditionally plans and design these complex buildings by heuristic rules of thumb, and these empirical design rules may lead to significant differences between real and predicted performance. Therefore, it's important to develop simplified models with acceptable accuracy for simulation-based design of swimming facilities. The scientific literature introduces different simplifications for the air handling unit modelling, but the impact of these simplifications is hardly analyzed. Therefore, the paper investigates the model complexity of the air handling unit in a Norwegian swimming facility by comparing a novel simplified model and a detailed model which serves as a digital replica of a real unit. The simplified decoupled model is found to have an acceptable accuracy for early-stage design, bearing in mind the overall uncertainty at this design stage. However, when carrying out a detailed investigation of a swimming facility, the detailed model should be applied, since this complex heuristic rule-based model, is taking into account phenomena like thermal-coupling effects between the swimming pool and the air handling unit and the control errors of the real internal controller. In conclusion, the paper derives useful guidelines for building performance simulation design of air handling unit, which is the main energy-intensive component in indoor swimming pool facilities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Discusses the concept of building performance simulation versus new digital trends. • Reviews Artificial Intelligence, Machine Learning, Digital Twins, Cyber-Physical Systems, Internet of Things, Data Mining. • Explores common notions about differences, similarities, strengths and limitations of each concept. • Reviews emerging work on the interface between these concepts. • New concepts enjoy traction and energize the building performance research area. In an increasingly digital world, there are fast-paced developments in fields such as Artificial Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the Internet of Things. This paper reviews and discusses how these new emerging areas relate to the traditional domain of building performance simulation. It explores the boundaries between building simulation and these other fields in order to identify conceptual differences and similarities, strengths and limitations of each of these areas. The paper critiques common notions about these new domains and how they relate to building simulation, reviewing how the field of building performance may evolve and benefit from the new developments. [ABSTRACT FROM AUTHOR]