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Abstract: One of the most important factors in the thermal behavior of exterior walls is the thermal conductivity of the clay in their bricks. The higher the percentage of lightening additives incorporated, the better the thermal behavior of the bricks. But as their thermal characteristics are improved the mechanical properties of bricks deteriorate. The goal is to study the influence of paper pulp as a lightening additive on the thermal and mechanical properties of the fired clay, and to improve the thermal properties as far as possible without the mechanical properties falling below the required levels. This involved the preparation of test specimens that were then subjected to the corresponding assays of thermal conductivity and mechanical strength. It is found that around 15% of paper pulp can be added without the brick''s mechanical properties ceasing to comply with current legislation. This amount of paper pulp has improved conductivity properties by 39.69% compared to the clay without additives, recording a minimum conductivity value at 10°C of 0.45W/m-K. This decrease in the conductivity of the clay results in a 16% improvement in equivalent thermal transmittance for brick walls made in the same way. [Copyright &y& Elsevier]

[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]

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]

• Power drop values for horizontal PV panel were in the interval 7.2–87.2%. • Power drop values for optimal PV panel were in the interval 7.2–30.6%. • Fly ash caused PV power drop was greater in periods with no precipitation. • For 1 day without rain, the additional power drop of horizontal panel was 0.4–10 %. • For 1 day without rain, the additional power drop of optimal panel was 0.2–9.1 %. Fossil fuels are mostly utilized for heat generation in Serbia throughout the heating season in the built environment which usually lasts 6 months every year, thus fly ash often accumulates on photovoltaic (PV) panels decreasing their performance. This paper analyzes results of the outdoor experiment conducted at the Faculty of Sciences and Mathematics in Niš investigating the influence of deposited fly ash on power (P) drop for horizontally and optimally inclined PV panels during three heating seasons in the City of Niš. Innovations of the paper are reflected in concrete data of P drop for short time periods in real outdoor environment, additionally connected with precipitation values which represent natural way of panels cleaning, that are all together not frequent in available literature especially for continental climate regions. The experiment showed that for only one day without precipitation, the P drop for the horizontal panel rose by an extra 0.4–10 %, and 0.2–9.1 % for the optimal panel. Experimentally measured data in this paper are of high importance not only for showing the P loss of PV panels in soiling conditions and the necessity of periodical cleaning, but also for their utilization in urban PV plants design and further simulations and calculations. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influences of air temperature step changes were concluded and analyzed. • The variations in airflow and human metabolic rate related to thermal comfort were studied. • A framework of thermal adaptation stages at different time scales was proposed. • The models suitable for dynamic thermal comfort evaluation were discussed. • The underlying mechanism of thermal pleasure and alliesthesia was explored. Dynamic thermal comfort refers to the environmental condition with non-constant parameters, which has attracted more concerns compared with steady-state thermal comfort because of its benefits on human satisfaction, energy efficiency, and body health. This study reviewed over 100 relevant papers in this field to gain a comprehensive understanding of dynamic thermal comfort. Firstly, the dynamics of environmental parameters were analyzed mainly from two aspects, changes in air temperature and variations of airflow. All conditions of air temperature step change were concluded, and the comparison of dynamic airflow was from air velocity, wind volume, air speed, wind direction, pulsation frequency, and turbulence. Then, changes of metabolic rate were discussed as the typical human-related factor which influences the variation of thermal comfort significantly in reality. After that, the thermal history and thermal adaptation involved in dynamic scenarios were further analyzed. A classification method based on different time scales was proposed to illustrate the development of thermal adaptation. Also, the models for dynamic thermal comfort and the concept of thermal alliesthesia were discussed. It proved that the rational dynamic strategy could effectively enhance human thermal perception and be beneficial to energy conservation of air conditioner, and the subjective changes of human activity and behaviors also had great influences which could not be neglected. This paper could provide a reference for systematically understanding dynamic thermal comfort through its theory, models, and application, and also help in understanding the mechanism of thermal adaptation. [ABSTRACT FROM AUTHOR]

• Temperature is the main factor causing the cooling and heating load on residential buildings. • Radiation, humidity, & wind are secondary factors causing residential cooling & heating loads. • K-means algorithm effectively clusters the comprehensive climate-energy dataset. • Climate impacts vary by cluster, tailored energy management needed. Limited comprehensive methods exist for studying spatial energy consumption distribution, integrating statistical and energy data. This paper introduces a novel approach for analyzing residential heating and cooling energy demand distribution. It employs clustering algorithms to study climate variables' impact on energy demand distribution and assesses building energy demand intensity regionally, taking China as a case study. Initially compiling a dataset comprising climate characteristics, socioeconomic factors, and energy demand through data collection and simulation, the study compares clustering algorithms, highlighting the effectiveness of K-means in clustering high-dimensional climate-energy datasets. K-means analysis reveals temperature-based daily methods significantly affect building energy intensity, alongside factors like radiation intensity and humidity impacting regional energy demand variably. Additionally, climate's influence on residential building energy consumption intensity varies regionally, with total energy demand influenced by population and economic factors. This paper offers insights for energy management and policy formulation. [ABSTRACT FROM AUTHOR]

Positive energy districts and sustainable plus energy neighbourhoods are developed in the European context to reduce energy use and greenhouse gas emissions from the building sector. The planning and development of positive energy districts and sustainable plus energy neighbourhoods is complex and requires collaboration between stakeholders and new measures to achieve high energy efficiency, local renewable energy generation, energy storage and flexibility, and energy sufficiency. This paper examines the implementation of energy measures in the planning and design of four positive energy district and neighbourhood development projects in Norway and Sweden. The paper compares the two different institutional and energy system contexts and how these affect the development of positive energy districts, focusing on the perspectives of the municipality and developers. Existing academic literature and positive energy district guidelines are used to develop an analytical framework for the planning and design of positive energy districts and sustainable plus energy neighbourhoods. Results highlight an early focus on energy ambitions, wide stakeholder involvement, and the importance of aligning interests between stakeholders and working interdisciplinary in the planning and design phases to find optimal energy measures. Both the building and the neighbourhood/district level are important to increase energy efficiency, energy sufficiency, and energy flexibility, and consequently lower the environmental impact of the whole development project. [ABSTRACT FROM AUTHOR]

[Display omitted] • A multi-method approach analyses the impact of behavioural change on gas consumption. • Energy savings potentials derived from modelling and survey results are compared. • The impact of the gas crisis and energy prices on energy saving measures is examined. • The studied behavioural changes lead to a 2.0–3.5% reduction in gas demand. • A tailored policy package is recommended to promote changes in heating behaviour. The geopolitical situation and the energy crisis caused by the Russian invasion of Ukraine have led to proposals for immediate reduction in energy consumption within the European Union (EU). The REPowerEU Plan of the European Commission proposes behavioural changes as short-term measures to rapidly reduce the EU's dependence on Russian gas and oil. This paper investigates the energy saving potential resulting from changes in household heating behaviour. Through a comparison of modelling results with results of the analysis of empirical data collected in a survey of households in four EU Member States (Germany, the Netherlands, Greece, and Poland), the paper examines the adequacy of the short-term measures proposed by the EU and the instruments needed to leverage such measures and increase their potential impact. Although the reported changes in heating behaviour lead to a 2.0 % to 3.5 % reduction in residential gas demand in the countries studied, the study recognises that the EU targets for reducing the gas demand will not be met under current regulatory conditions, and considers the energy savings observed in this paper and in the literature as a short-term response to unexpected circumstances. The paper proposes a policy package to transform these responses into the long-term behavioural changes needed to achieve climate targets. Promoting uptake of household technical infrastructure, providing financial support and implementing information campaigns are suggested as effective approaches to achieving savings targets. [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]

• 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]

• A systematic literature review on smart building sensing system was provided. • Key information about sensing case studies in the built environment was reviewed. • Sensing data analysis for intelligent building operations were summarized. • Future challenges for applications of sensors in the built environment were given. This paper aims to provide a systemic review of how indoor sensors influence in managing optimal energy saving, thermal comfort, visual comfort, and indoor air quality in the built environment. The optimal management of energy saving and occupant comfort plays a vital role in the built environment because the occupant's productivity and health are highly influenced by indoor environmental quality. In order to do this, there must be a functional sensing system that connects the environment variables (e.g., temperature) with building environmental control systems such as the heating, ventilation, and air-conditioning system. This paper starts with an overview of the importance of energy saving and occupant comfort in the built environment. It then discusses sensors and their importance in the built environment and reviews the different types of sensors, which explains them in terms of how they influence the indoor built environment and occupant productivity. The paper further explores the application of sensors in the built environment and analyzes this in terms of energy saving, thermal comfort, visual comfort, and indoor air quality. Following this, the data analysis is discussed in terms of data, information, and knowledge accrued from the sensors. Lastly, the paper discusses the future challenges for the improvement of building indoor environmental quality and energy saving by the application of sensors. [ABSTRACT FROM AUTHOR]

This paper presents a simulation study of an active energy storage device intended to enhance building operation. This device –which is designed for installation in the ceiling plenum of an office, a mechanical room or in other convenient locations– consists of an arrangement of several panels of a phase-change material. It may be charged or discharged as required with an air stream passing between the panels, thus operating as a PCM-air heat exchanger (PCM-HX). The first part of the paper focuses on the design of the PCM-HX. Several design configurations are evaluated; investigated parameters include the PCM-HX dimensions, the number of air channels and airflow rates. The paper also includes an experimental validation of the PCM model. Performance criteria that were considered in the parametric study include the amount of stored heat, the time needed to charge/discharge the PCM storage and the overall energy density of the device. The second part of the paper evaluates different control strategies aimed at reducing peak demand and the size of HVAC system. The impact on peak load of a linear ramp for the temperature setpoint is investigated: it was found that a two hour linear ramp in temperature setpoint –together with a PCM-HX configuration with six air channels– can reduce the peak heating load by 41% as compared to a benchmark case without the PCM-HX. [ABSTRACT FROM AUTHOR]

• A novel agent-based model (ABM) was developed to explore occupant behaviors in the context of built environment. • Relevant data was collected using customized sensor nodes and paper-based survey in a test bed building. • A validation study was conducted to test the occupant behavior model with the methods of visualization and calculation of quantification metrics for performance evaluation. • The proposed research framework has the potential to improve both building energy efficiency and occupant comfort during the life cycle of a building. Occupant behaviors are one of the dominant factors that influence building energy use. Traditional building energy modeling programs use typical occupant schedules that often do not reflect actual situations. Robust occupant behavior modeling that seamlessly integrates with building energy models will not only improve simulation performance, but also provide a deeper understanding of occupant behaviors in buildings. This paper presents a development and validation approach to a novel occupant behavior model in commercial buildings. A robust agent-based modeling (ABM) tool, namely Performance Moderator Functions server (PMFserv), is used as the basis of the occupant behavior model. The ABM considers various occupant perceptions and interactions with window, door, and window-blinds based on the environmental conditions. An elaborate agent-based model that represents an office space in an existing building is developed. This is followed by a validation study of the ABM through the use of embedded sensors that capture the indoor ambient conditions and a survey to record actual occupant behaviors. By comparing the recorded behavior data with ABM output, this paper discusses the proposed ABM's prediction ability, limitations, and extensibility. Finally, the paper concludes with the potential of integrating the occupant behavior model with building energy simulation programs. [ABSTRACT FROM AUTHOR]

• HVAC system stock is heterogeneous and changes over time. • A HVAC stock modeling and an archetype building energy modeling were integrated. • Disregard of the heterogeneity and its change can cause error in energy modeling. • Energy reduction by technological innovation relies on the share of the HVAC stock. Urban building energy model (UBEM) is an important tool in coping with energy issues of building sector. This paper focuses on an issue that the stock of heating, ventilation, and air-conditioning (HVAC) systems has been oversimplified in UBEM without considering its heterogeneity. It has been observed that this oversimplification may incur a significant error in the result. To address this issue, this paper proposes a framework that integrates a logistic regression based HVAC system stock modeling method and the bottom-up type engineering modeling method using building archetypes considering HVAC system configurations. A case study with Japanese office building stock revealed that (1) the disregard of the heterogeneity resulted in an error of 15% in the annual primary energy consumption, (2) the historical change in the HVAC system stock could decrease the consumption up to 5%, and (3) its composition has a significant influence on the energy saving due to energy efficiency increase in heat source machines used in HVAC systems. The presented method would contribute not only to improving the reliability of UBEM but also to providing an analytical capability of considering energy efficiency technologies related to HVAC systems along with the alternation of technology selection in the building stock. [ABSTRACT FROM AUTHOR]