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In Ireland, residential energy policy has been influenced by thermal inefficiency, high electricity prices and energy poverty. These factors have driven the fabric-first approach, where buildings must undergo extensive thermal retrofitting to qualify for heat pump subsidies. The cost-effectiveness of this approach has not been properly scrutinised to date. This paper addresses this knowledge gap by exploring the cost-effectiveness of the fabric-first approach and other decarbonisation strategies for the residential sector and broader energy system. The study develops scenarios by modifying the Heat Loss Indicator (HLI) threshold, which evaluates the fabric and ventilation heat loss per unit of floor area and is used to determine heat pump subsidy eligibility. The paper also considers scenarios that permit installing sub-optimal performing heat pumps that do not achieve peak performance levels in dwellings with higher HLIs. The analysis uses the TIMES-Ireland Model (TIM), a model of the whole energy system that reflects the interdependence of mitigation pathways across different energy sectors. Our study shows that Ireland's fabric-first approach requires 22 times more thermal retrofits to meet climate targets than alternative pathways. The alternative pathways based on the latest heat pump performance data are more cost-effective, showing that higher heat pump HLI thresholds can reduce the average sectoral marginal CO 2 emission price by 60% compared to the current fabric-first approach. The cost savings indicate that shifting to an HLI threshold of 2.3 W/K/m2 is close to optimal, while cost savings are minimal when moving from an HLI of 2.3 to 3 W/K/m2. Furthermore, electricity prices and household awareness are essential to the cost-optimal transition to residential heat pumps. [ABSTRACT FROM AUTHOR]

• Propose IVMD-WOA-LSSVM cooling load prediction method. • Using VMD to extract Subsequence with different data characteristics from original load data. • The WOA algorithm adopted in this paper has faster convergence speed, can effectively avoid falling into local extreme value, and achieve the optimal effect. The global environment faces major challenges due to the widespread use of air conditioning systems, and accurate and effective cooling load forecast is a vital component of energy conservation and improving the system's operational efficiency. This paper presents a method for predicting the cooling load of air conditioning using Variational Mode Decomposition (VMD) and an enhanced least square support vector machines(LSSVM). The whale optimization algorithm (WOA) was employed to determine the ideal modal decomposition count and penalty factor combination. After optimizing the parameters, the VMD algorithm was employed to decompose the initial cooling load sequence. The redundant information between input variables is eliminated by random forest (RF) algorithm to reduce the model dimension. In the end, the LSSVM model, which has been optimized using WOA, is employed for predicting each component. The final results of cooling load prediction are obtained through reconstruction and superposition. The results of the simulation demonstrate the efficacy and viability of the suggested approach. The root mean square error of the coefficient of variation is 0.0344, which indicates that the proposed model has good robustness. The R2 reaches 0.9901, which indicating that the model has good prediction accuracy. [ABSTRACT FROM AUTHOR]

Nearly-Zero-Energy and Zero-Emission Buildings are one fundamental part of the world strategies addressed at mitigating the global warming trend and coping with the goal of setting 1.5 °C the planet temperature increase with respect to the pre-industrial conditions. The present paper refers to a specific case study, the Smart Energy Building (SEB) located in the Savona Campus of the University of Genoa. The SEB is a very innovative building for both the envelope (ventilated highly insulated facades) and the energy systems (including a ground coupled heat pump and an exhaust air-to-air heat pump for building ventilation); the building is equipped also with a PV module field on the rooftop for electricity production. The present paper first evaluates the heating and cooling loads of the building by means of an EnergyPlus model and analyses the impact of different control strategies related to the temperature setpoints. In particular, case #1 refers to a minimum temperature to be assured by heating only in winter and to a maximum one to be avoided by cooling only during summer; on the other hand, in case #2 the temperature inside the building is controlled within a defined range (both heating and cooling modes) all year long. From EnergyPlus simulations, the hourly heat loads have been averaged as monthly values and employed for the calculation of the required overall length of the ground heat exchangers of the geothermal heat pump of the SEB building. The ground-side analysis is performed by applying the Authors' ASHRAE- T p8 method, in its recently released web version. For the BHE (borehole heat exchanger) calculations, a parametric analysis has been performed in terms of ground thermal conductivities. Finally, the PV electrical production has been estimated by EnergyPlus simulations and compared with the corresponding measured one, thus showing the net energy zero behaviour of the present building. [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]

[Display omitted] Semi-transparent photovoltaic (STPV) glazing is a promising building integrated photovoltaic (BIPV) technology for converting traditional windows into on-site energy-producing building components. Besides electricity generation, there is also an urgent demand to enhance the thermal management of STPV glazing. Passive radioactive cooling paint is an emerging nanomaterials technology that shows great potential to reduce the painted surface temperature. Aiming to further promote the PV window's energy efficiency, this study designed and investigated a novel coated semi-transparent photovoltaic (CSTPV) glazing supplemented with highly transparent passive radiative cooling coating. In addition, the STPV/CSTPV's impacts on indoor visual environments were investigated, especially on the rarely studied aspect of interior colour rendering performance. This paper employs a combination of numerical simulation and experimental measurements to comprehensively assess these innovative glass systems in terms of electricity generation, thermal performance, daylighting efficiency, and the impact of color on the indoor environment. The results indicate that the CSTPV windows can significantly reduce the heat gain rate by about 15 % and achieve excellent electricity generation, with an annual output approximately 3 % higher than that of STPV windows. Despite the radiative cooling coating, CSTPV glazing maintains a level of daylight illumination comparable to that of STPV glazing, with glare levels remaining within acceptable limits. Furthermore, the study emphasizes the effects of CSTPV and STPV on the indoor Color Rendering Index (CRI) and Correlated Color Temperature (CCT) performance. All samples measured have CCT values within the range of neutral light, and CRI values exceeding 96, demonstrating satisfactory high-quality indoor lighting conditions. This research shows that CSTPV and STPV glass technologies not only significantly enhance the energy efficiency of buildings but also provide a scientific foundation for sustainable development in architectural design. [ABSTRACT FROM AUTHOR]

The optimization of HVAC system control affects the thermal comfort of building environment and air-conditioning energy consumption. However, most existing studies concentrate on human thermal comfort and neglects to incorporate air-conditioning energy consumption required to establish a controlled indoor thermal environment within the policy, which results in air-conditioning system high energy consumption. This paper suggests an indoor temperature intelligent control method based on thermal comfort and energy consumption of air-conditioning, and the TD3 algorithm is employed as an intelligent control algorithm to obtain the optimal setpoint that maximizes the joint benefit of the occupant's thermal comfort and the energy saving rate of air-conditioning system. The air-conditioning indoor temperature intelligent control model based on TD3 algorithm is trained by using the experimental data, and the method is simulated in TRNSYS simulation software to corroborate the efficacy and energy saving rate of the method. The results indicate that the indoor temperature intelligent control model based on thermal comfort and energy consumption of air-conditioning has less optimal adjustment of indoor temperature setpoints, which is more stable for the air-conditioning system and can satisfy the thermal comfort demand at the initial control stage to prevent significant fluctuations in human thermal sensation compared to the linear control method based on thermal sensation and the fuzzy control method based on thermal sensation. Besides, the average daily energy consumption can be reduced by 6.54% and 3.37% respectively, when compared to the linear control method based on thermal sensation and the fuzzy control method based on thermal sensation. [ABSTRACT FROM AUTHOR]

• A stochastic optimization-based transactive energy management is proposed. • Cloud energy storage (CES) is considered in the transactive framework. • The optimization model includes different costs of smart homes and CES. • The solving process is implemented in an automated and decentralized manner. • Smart contract is deployed in Ethereum blockchain. In modern paradigm of smart grid, prosumers can trade energy peer-to-peer in a transactive energy market to reduce energy costs and improve energy efficiency. In addition, cloud energy storage (CES) is a type of shared energy storage systems with high economic efficiency that can provide energy storage services for prosumers and become an active player in energy trading. However, transactive energy implementation in power systems has several challenges such as data privacy and security. In existing researches, energy trading mechanisms rely on central authorities, where security and privacy cannot be maintained. Therefore, this paper presents a decentralized stochastic optimization model of transactive energy management based on blockchain in the presence of CES. In this framework, the smart contract based on Alternating Direction Method of Multipliers technique is deployed on the Ganache and Ethereum blockchains, and the YALMIP toolbox along with CPLEX solver handle decentralized optimization in the MATLAB software. The numerical results demonstrate that not only the capacity of distributed generations is fully utilized, but also total costs of smart homes are reduced by more than 27%, and the total grid's delivery power to smart homes is decreased by more than 24% compared to the inflexible scheduling. [ABSTRACT FROM AUTHOR]

The demand side is required to increase its energy flexibility to tackle the demand–supply mismatch caused by integration of renewable energy sources into the energy mix. Model Predictive Control (MPC) is one of the promising measures for achieving this goal. MPC requires operational data from the building to train its predictive model. The quality of this data affects the performance of MPC. This paper aims to analyze the impact of a dataset's size and excitation method on flexibility harnessed by MPC. Five datasets have been generated that differ in their size and excitation method. They are used to train three modeling algorithms on two buildings. Next, generated predictive models are integrated into an MPC framework, to assess the impact of datasets on the performance of MPCs for activating the energy flexibility of a building. The case studies are two similar dwellings where one is equipped with Electric Heaters (EH), and the other uses an UnderFloor Heating (UFH) system. Results show that the flexibility of the EH case is more sensitive to the size of the dataset. Furthermore, it is shown that the suitability of the excitation signal for flexibility activation depends on the modeling algorithm. The results show that choosing an ill-suited dataset might cost the MPC 35 % and 20 % of the potential flexibility for EH and UFH cases, respectively. [ABSTRACT FROM AUTHOR]

• Thermal insulating mortars were exposed to different accelerated ageing procedures. • Ageing tests included elevated temperature, freeze–thaw and elevated relative humidity levels. • The ageing procedures were designed to be equivalent to 10 years of natural ageing. • The long-term thermal conductivity and mould susceptibility of the mortars are assessed. • A maximum increase of thermal conductivity up to 30% is obtained after ageing. The use of thermal insulation materials in the opaque walls is one of the best strategies toward the improvement of the thermal performance of the building envelope, thereby contributing to increase the energy efficiency of the built environment. In this context, an increasing number of studies are focusing on the development of mortars with enhanced thermal performance, i.e., thermal insulating mortars. However, considering the innovative character of these materials, reliable data on their long-term performance is still lacking, particularly concerning the thermal performance and suitability to refurbishment works. The aim of this paper is to evaluate the long-term thermal conductivity and biological colonisation of industrially produced and experimentally designed thermal mortars with EPS, cork and silica aerogel aggregates. The long-term performance of the mortars was assessed prior, during and after exposure to three accelerated ageing tests including elevated temperature, freeze–thaw cycles and high humidity levels. For each test, an empirical model (i.e., Arrhenius law, Peck model, and Coffin-Manson equation) was used to compute the acceleration factor, thus allowing to correlate the accelerated and natural ageing results and to estimate the degradation levels that would be obtained after 10 years of exposure to natural weather conditions. The results of the thermal conductivity show that a maximum increase of 10 % and 30 % can be obtained after ageing for the industrially produced and experimentally designed mortars, respectively. Moreover, all mortars were susceptible to mould growth, with the greatest biological colonisation levels obtained after the high moisture content ageing for the experimentally designed mortars. [ABSTRACT FROM AUTHOR]

The principal aim of the different authors regarding the issues concerning the interoperability between Building Information Modelling (BIM) and Building Energy Modelling (BEM) platforms is focused on workflow automation. This article aims to focus on the modeling practices, by suggesting practical modeling recommendations for BIM modelers, which improve the exportation results to BEM platforms. The project followed a two-phase methodology consisting first of the proposal of a line of modeling recommendations based on a literature review, and second of the application of the recommendations, using an experimental model. At the outset, the authors recompiled research papers, software tutorials, and the main author's usage (by tool testing). The recommendations were organized by categories and fields of implementation. For the second phase, a single-family housing project was assigned for the testing procedures. The project was modeled with the two main BIM platforms available for the research (Autodesk Revit and Graphisoft Archicad). The testing procedures included the implementation of the recommendations by categories, and each category was evaluated by two premises: the geometrical consistency of the exported model with the project (by using the Aragog gbXML viewer tool), and the utility of the imported model on a BEM platform (by using Design Builder software with EnergyPlus). The results of each tested model were contrasted and analyzed, to determine if the implemented recommendations were successful for improving the exportation process. In conclusion, it was found that the recommendations improved the geometrical consistency and the elements characterization with the building systems, and an energy analytic simulation was successfully fulfilled with the optimized model imported from both BIM software. Although the recommendations made small improvements from the base to the optimized model, the result for each exported model was a successful energy analytic simulation. • Interoperability workflow between BIM and BEM platforms needs standardization before using an automation tool. • The article analyzes the geometric structure of BIM and BEM models and their impact on exportation. • The researchers proposed guidelines to enhance exported BEM files by refining BIM modeling practices. • Applying recommended modeling practices improved the exported results for both tested BIM platforms. • Implementing recommended BIM modeling practices aids BEM for improved geometry quality and data collection. [ABSTRACT FROM AUTHOR]

[Display omitted] • Improving energy efficiency in centennial Korean historic buildings via retrofit. • Development of retrofit with diatomaceous earth and phase change materials. • Reduce condensation risks without damaging the building's historical value. • The historic building withstands outdoor climates despite no insulation. • Save energy solely with interior finishing materials, without insulation. Historic buildings require groundbreaking retrofit technology to prevent damage to the exterior appearance of the exterior. This paper presents a retrofit technology to increase the energy efficiency of old historic buildings that were heavily exposed to hygrothermal conditions. The study employed an innovative interior finishing material composed of diatomaceous earth and microencapsulated phase change materials to achieve a trade-off between performance improvement and heritage conservation. Based on previous studies, an optimal formulation was identified through evaluation. The developed retrofit technology displays crack resistance, high latent heat capacity, and remarkable moisture stability. Our analysis revealed that the application of this retrofit solution would effectively reduce moisture levels and mitigate condensation risks without compromising the building's historical integrity. The retrofit technology yielded a reduction in cooling and heating energy consumption by over 10% while concurrently addressing moisture stability through a decrease in the thermal transmittance of the exterior walls. By utilizing passive technologies and the unique properties of diatomaceous earth, our pioneering research on the sustainable preservation of historic buildings has laid the foundation for widespread retrofit adoption in heritage conservation. [ABSTRACT FROM AUTHOR]

Due to the growth of electricity consumption in residential and commercial buildings, there is a growing burden on utilities in urban areas. Sustainable buildings are being developed by integrating renewable energy generation to make an eco-friendly environment. An isolated building-integrated (IBI) DC microgrid fed by a hybrid solar PV (photo voltaic) generator-thermo electric generator (TEG) is one viable option for obtaining self-sustainable energy for the building. The battery is an important component in such IBI DC microgrid fed by the hybrid solar PV generator-TEG for providing reliable power to the loads. However, the operation and planning of the IBI DC microgrid are challenging, and therefore it needs a suitable load scheduling strategy. This paper proposes the optimal load scheduling strategy (OLSS) under demand response to schedule the household loads in the building. The proposed OLSS aims to balance two conflictive objectives, user comfort and peak-to-average ratio (PAR), using a binary particle swarm optimization algorithm (BPSO). The proposed OLSS optimizes user comfort and PAR and reduces peak demand and battery usage. It also helps in the optimum utilization of power generated from hybrid solar PV generator-TEG by shifting the loads during sun hours. Reducing battery usage reduces the conversion loss and extends the battery life. To validate the proposed OLSS, the investigations are carried out using the data obtained across different seasons. The results show that the proposed OLSS schedules the household loads with the desired user comfort and reduces PAR by 22 % in future buildings. [ABSTRACT FROM AUTHOR]

• Choose raw power data as the feature, easy to obtain. • Construct a contrastive learning framework based on temporal data. • The encoder based on TCN can fully extract unlabeled device features. • This method can effectively identify multi-state and feature-similar devices. • The experimental case study verified its effectiveness and generalization. Traditional non-intrusive load monitoring (NILM) methods rely on massive historical labeled data. However, due to the privacy and high labeling cost of datasets, their generality and feasibility are limited, with poor performance in identifying devices with multiple states or similar features. To address this issue, this paper proposes an SSCL-LM framework based on temporal convolutional network (TCN), integrating contrastive self-supervised learning into NILM. Initially, through contrastive self-supervised pre-training, the framework learns well-represented load temporal characteristics from abundant unlabeled 1D power data. Then a small amount of labeled data is used to fine-tune the classifier to learn load categories represented by different temporal characteristics. Finally, test data is inputted into the model for load identification. Validating on the REDD dataset, the results demonstrate that with only 30% labeled data for fine-tuning, the proposed method achieves a 4.04% higher F1 score compared to traditional supervised methods. Moreover, utilizing only 1D power features, this method exhibits superior identification performance for devices with multiple states or similar features. Furthermore, this method enables performance transfer among loads of the same device with different parameters across different households, verifying its strong generalization and practicality. [ABSTRACT FROM AUTHOR]

[Display omitted] The rising global energy use in buildings, driven by climate change and economic expansion, is a major concern. In Algeria, the residential sector is a significant contributor, accounting for roughly 40 % of total energy consumption and 32 % of greenhouse gas emissions. This paper aims to Modeling and forecasting energy consumption in Algerian residential buildings. In this study, a bottom-up methodology was combined with Geographic Information System (GIS) analysis to quantify and project energy consumption patterns in Algerian residential buildings until 2100, as well as identify climate zones, which will help develop thermal standards. Commencing with the collection of pivotal data such as temperature and global horizontal irradiation (GHI), the research proceeds to ascertain the annual heating and cooling needs for residential houses across Algeria (at 906 uniformly dispersed spots) using the degree-days method. Besides, the energy consumption for other residential appliances, including lighting, refrigerators, and freezers are also evaluated. Utilizing a GIS software (ArcGIS 10.x), the obtained results for various building energy usage at 906 points are therefore interpolated to generate high resolution maps representing different regions in Algeria. The analysis predicts a significant rise in energy consumption from 47.54 TWh in 2022 to 74.4 TWh in 2100, underscoring the diverse energy requirements across nine identified climatic zones. Zone 5 emerges as the highest energy-consuming zone per household, attributed to its elevated Heating Degree Days (HDD) and Cooling Degree Days (CDD) values. Additionally, cooking emerges as the primary energy consumer nationally, accounting for 30.61 % of total consumption, with variations observed in the usage of appliances like air conditioners and fans, influenced by local climate conditions. Zones with high heating energy needs, such as Zone 5, could benefit from energy-efficient heating systems, while zones with substantial cooling energy requirements, like Zone 9, may require targeted cooling strategies. These findings emphasize the necessity of tailored energy policies to address the distinct demands of each climatic zone rather than setting standard strategies for all regions. Despite few limitations of this study, useful insights are gained that can aid policymakers and urban planners in crafting sustainable energy policies to address evolving residential energy demand in Algeria and beyond. [ABSTRACT FROM AUTHOR]

In building operations, Heating, Ventilation, and Air Conditioning (HVAC) system faults lead to substantial energy waste. Due to the rapid growth in the availability of sensing data and advancements in computational technology, AI-based methods have emerged as a powerful tool for Fault Detection and Diagnosis (FDD) of HVAC systems, significantly enhancing the efficiency of building operations. Several studies support supervised learning methods for FDD; however, these are limited in practice due to their reliance on labeled datasets, which are generally unavailable. In this context, unsupervised methods, notably those using cluster analysis, show great promise, particularly for analysing Building Automation System (BAS) data. This paper presents a methodology using OPTICS – an ordered clustering algorithm – for fault detection and explores the impact of PCA on its accuracy for fault detection, using both published and full-scale historical building data. While beneficial for reducing computational costs and enhancing noise reduction — thereby generally improving the clarity of cluster differentiation — PCA's dimensionality reduction may result in the loss of critical information, leading to some clusters being less discernible or entirely undetected. These overlooked clusters could be indicative of underlying faults, and their obscurity represents a significant limitation of PCA when identifying potential faults in complex data. [ABSTRACT FROM AUTHOR]

The shift toward urban decarbonization, which involves transitioning to heat pumps and integrating photovoltaic (PV) energy generation, poses substantial challenges to electricity grids in cities. Ithaca, NY, with its commitment to the Green New Deal, serves as a case study for cities confronted with these challenges. This paper explores how various building electrification scenarios might affect Ithaca's net-zero ambitions. We show that our reduced-order Urban Building Energy Modeling (UBEM) approach can accurately predict building energy consumption and rooftop solar PV energy generation for the 6114 buildings in the City of Ithaca. Building on that, we present three increasingly ambitious decarbonization scenarios that offer a holistic perspective on Ithaca's future electrification potential. In automating the reconstruction of building geometry from readily available LiDAR data, along with automated BEM setup and calibration, our methodology offers a robust toolkit to plan holistic decarbonization efforts. We argue that our approach, which forecasts both UBEM and PV for all residential and commercial buildings within a single framework, provides insights that cannot be reached with siloed methods. In the future, we believe that the abundance of readily available urban data sets will allow us to transition our approach to other jurisdictions across the US. • The monthly gas and electricity demand of 6114 buildings in Ithaca are modeled using UBEM. • We implement a validation approach via anonymized billing data that avoids privacy concerns. • We test three decarbonization scenarios regarding building electrification and photovoltaics • The scenarios offer top-level estimation of Ithaca's achieveable 'net zero days'. • Our fast scenario analysis provides a foundation for evaluating retrofitting interventions. [ABSTRACT FROM AUTHOR]

• The price-responsive flexible behaviour of an office building is studied. • The price signals convey changes in temperature setpoints, offering flexible demand. • A step-by-step approach for utilising flexibility in a building is proposed. • The study shows flexibility availability is dependent upon HVAC operational modes. • Demand increases notably for prices ≤ 10 % of the highest value. Flexibility in buildings is a low-cost alternative to support the electricity network with high penetration of variable generation. There is a limited understanding of energy flexible behaviour of the buildings in response to electricity market signals. In this paper, the price-responsive flexible behaviour of a commercial building with a cooling system has been studied. A first-order virtual battery model of the building and flexibility function-based approach have been used to understand the flexible behaviour of the building to varying price signals. The price signals delivered changes in building electricity demand profile, altering cooling temperature set points. The building structural thermal storage capacity was found to vary depending on demand changes. Low price signals provoke positive demand changes with respect to the baseline demand, taking the system to charging mode. Similarly, high price signals lead the system to discharging mode, reducing the instantaneous charging condition of the system. HVAC operating states, e.g., free cooling/economiser cooling and mechanical cooling, have been found to have a notable impact on electricity demand flexibility. The flexibility function shows that for a very low-price value, the cooling setpoint approaches the lowest comfort bound, and demand increases significantly. A deviation between the maximum and minimum electricity demand of 207 kW for the studied building system gives an estimated maximum energy flexibility capacity of 7248.2 kWh. The response of the building to variable price signals yields flexible demand, delivering cost savings of 13 %. [ABSTRACT FROM AUTHOR]

Air balancing is a crucial technology for reducing energy consumption in ventilation ducts and improving indoor environmental quality. Existing air balancing methods face challenges due to the complex and diverse internal duct topologies of ventilation duct systems, as well as the strong coupling of airflow between branches, making air balancing tuning difficult. To address the slow convergence speed and difficulty in precise control of air balancing in multi-branch ventilation systems, this paper proposes a fast wide-range air balancing control method based on deep reinforcement learning (DRL-AB). A system airflow control process with Markovian properties is designed, with state, action, and reward functions oriented towards reducing airflow errors. A dynamic single-branch target training mechanism is designed to reduce training costs while improving the agent's training efficiency and its ability to generalize to different target airflow levels. The proposed method's performance under various target airflow conditions is verified through experimental platforms. The results indicate that, in all test cases, the maximum percentage error is controlled within 3.33%, ensuring rapid and accurate convergence of a wide range of target airflows. This demonstrates strong universality, prevents waste of airflow energy, and enhances energy utilization efficiency. [ABSTRACT FROM AUTHOR]

• The impact of insulation moisture ingress and thermal bypass are investigated. • 2D computational fluid dynamics is used to simulate thermal bypass in buildings. • Moisture ingress can increase thermal transmittance by a factor of ∼2.2. • Thermal bypass can increase thermal transmittance by a factor of ∼3.5. • Insulation detailing and installation can significantly impact heat loss in buildings. Improving the performance of buildings is a key part of the global effort to limit greenhouse gas emissions and reduce energy consumption. Reducing the designed thermal transmittance of building envelopes through the addition of insulation is one of the primary measures adopted in many nations aiming to address the building sector. In theory, these globally adopted measures to improve design should result in considerable energy savings. The reality however is different. And a mismatch between design and in-use performance has been widely cited. This performance gap is extensively attributed to changes in occupant behaviour – the rebound effect. Considerably less research has investigated technical performance gap rationale. This paper aims to address this literature gap by investigating two technical reasons why design and in-use performance might differ; namely: i) moisture presence in the building's insulation and ii) thermal bypass of the insulation system. The study uses a combination of thermal resistance analogies and computational fluid dynamics, to ascertain the extent to which compromised insulation would impact the performance of building envelopes. An increase in envelope heat loss results in an increase in a building's energy consumption, and hence its associated greenhouse gas emissions. The study concluded that, in extreme cases, the presence of moisture in the insulation layers can increase the thermal transmittance of highly insulated envelopes by a factor of 2.2, while closed- and open-loop thermal bypass can increase it by a factor of 3.5 and 3.7 respectively. Although these might be considered extreme cases, the results align with findings from several past studies that quantified the gap between in-situ and design thermal transmittance. The results suggest that, while ensuring building envelopes are designed with adequate levels of insulation is important, ensuring that the insulation is adequately installed is potentially of equal importance. [ABSTRACT FROM AUTHOR]

The data-driven approach currently dominates the field of Automatic Fault Detection and Diagnosis (AFDD) in HVAC systems. However, a significant concern lies in the prevalent use of labeled experimental and simulation data, which often does not represent real-world operational conditions. This study unveils a comprehensive framework for AFDD in light commercial buildings, effectively leveraging unlabeled raw data extracted directly from their Building BAS. Its main goal is to provide a versatile methodology tailored for real-world applicability. Buildings classified as "light commercial" typically have less than 2,500 square meters of floor area and no more than six stories, such as small offices, medical facilities, banks, small manufacturing facilities, etc. A common feature of these buildings is the fact that the HVAC systems tend to be relatively simple and have similar configurations, thus making it easy to develop scalable and reproducible fault detection methods. This paper focuses on a practical case study in a light commercial building HVAC system situated in Montreal, Canada, encompassing a single Air Handling Unit (AHU) and four Variable Air Volume (VAV) reheating boxes to evaluate the framework. This comprehensive framework encompasses a sequence of sub-objectives: creating a sizable, synchronized raw dataset from diverse BAS sensor tags, comprehensive data cleansing to address inconsistencies, developing an anomaly detection method, investigating these anomalies to extract underlying rules, and finally, dataset labeling. An AFDD classification model is then applied to evaluate its ability to distinguish normal from faulty conditions across various fault types. The study highlights the potential of dimensional reduction techniques and unsupervised clustering for effective anomaly detection in light commercial buildings, as well as the power of the Decision Tree classifier for uncovering hidden patterns, especially in anomaly conditions. It also highlights the significance of addressing imbalanced datasets in AFDD and the complexities of detecting sizing-related faults. Despite these challenges, the framework exhibits robust performance in detecting and diagnosing a range of HVAC faults. It offers a systematic and adaptable approach for handling real-world operational data in light commercial building HVAC systems, extendible to other building types, bridging the gap between data-driven methods and practical applications. [ABSTRACT FROM AUTHOR]

• The algorithms are compared in terms of accuracy, precision, recall, and F1. • A SMOTE was conducted in order to avoid imbalances in the dataset. • Through an artificial neural network a connection weigh approach was conducted. • KNN outperformed the other analysed algorithms by less than 3%, on average. • Ridge Regression revealed to be a promising algorithm for the fault detection. Heat Pumps (HPs) technology had a remarkable diffusion and growth in the Heating, Ventilation, and Air-Conditioning (HVAC) field, with an increase predicted to be greater than 200% until 2030. Therefore, efficient asset management must be achieved by ensuring their maintainability and reliability. For this purpose, several methodologies have been used in the last years for HPs fault diagnosis. These methodologies include, with the advances of computational systems and reduced costs of several components, machine learning and other artificial intelligence techniques. In the present paper, a connection weight approach is used to identify the clout of each component for the fault detection (FD) of HPs. The results show that the method provides a more comprehensive assessment of the importance of each component, yielding a reduction of 50% of the features. A comparative analysis of supervised learning classification algorithms is performed, for fault detection of an air-to-air HP in cooling mode. Such algorithms include Naïve Bayes, Support Vector Machine, Logistic Regression, and K-nearest Neighbors. The faults considered include compressor and reversing valve leakage, improper condenser and evaporator fouling, liquid line restriction, refrigerant undercharge and overcharge, and also the presence of non-condensable gases. Algorithms are compared in terms of accuracy, precision, recall, and F1 Score. The best results were achieved with K-nearest Neighbors, since it presented the highest values for these four metrics, over 99% after a cross validation considering a reduced number of components. Naïve Bayes, Support Vector Machine, and Logistic Regression also achieved over 90% performance metrics. Other models were superficially analysed and Ridge Regression with metrics performance of 100%, on average, revealed to be a promising tool for fault diagnosis in HPs. [ABSTRACT FROM AUTHOR]

Grid-interactive efficient buildings (GEBs) optimize their operations by using flexible load techniques, such as precooling, to reduce peak electricity demand. However, precooling effectiveness depends on factors like building properties, weather, and utility rates. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. On top of that, we investigated the impact of building thermal mass, outdoor air conditions, and energy price profiles on the optimal precooling strategy. Our results show substantial cost savings and peak load reduction potential in buildings with heavy thermal mass, with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings, peak load reduction, and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings. • Proposed a parameterized schedule for precooling optimization. • Demonstrated the effective approximation of model predictive control by the proposed schedule. • Conducted a large-scale simulation analyzing diverse impact factors on precooling. • Examined effects of building thermal properties, weather conditions, and utility rates on precooling. [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]