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

To align the buildings sector with the 1.5°C climate change trajectory, enormous improvements in energy efficiency are needed. It is therefore crucial that the tools used to evaluate buildings' energy use undergo robust testing. This paper tests, for the first time, the outcome of the Passive House Planning Package (PHPP) and an author-modified version of the Radiant Time Series Method (RTSM), following the Building Energy Simulation Test (BESTEST). The results show that while the validity of the modified-RTSM is slightly superior to that of the PHPP, both tools pass less than 35% of the cases -- necessitating further calibration and challenging the widely-held belief that the PHPP is 'validated'. As the PHPP and the modified-RTSM present a relatively simple and quick way of evaluating buildings' energy performance, calibrating their methodologies so they pass the BESTEST cases could put them at an advantage over the fully dynamic and resource-intensive tools. [ABSTRACT FROM AUTHOR]

Spawn is DOE's next-generation tool chain for whole building energy control simulation. Spawn couples traditional imperative load-based envelope modelling with new equation-based modelling of HVAC and controls. Spawn uses EnergyPlus for the former and the Modelica Buildings Library for the latter. Because it leverages the Modelica Buildings Library, Spawn can evaluate advanced energy systems at the building and district scale, including new architectures and controls for heat pump systems with storage, and the coupling of such systems to electrical distribution networks. Spawn's Modelica integration likewise enables it to simulate realistic control sequences and therefore to bridge energy simulation and control implementation workflows. From EnergyPlus, Spawn inherits efficient envelope simulation and the ability to use existing envelope model authoring tools. This paper describes the architecture and implementation of Spawn, which automatically couples Modelica and EnergyPlus for run-time data exchange. This paper closes with examples that illustrate Spawn's modelling and simulation processes. [ABSTRACT FROM AUTHOR]

Natural Ventilation Effectiveness (NVE) is a performance metric that quantifies when outdoor airflows can be used as a cooling strategy to achieve indoor thermal comfort. Based on standard ventilation threshold and building energy simulation (BES) models, the NVE relates available and required airflows to quantify the usefulness of natural ventilation (NV) through design and building evaluation. Since wind is a significant driving force for ventilation, wind pressure coefficients ( C p ) represent a critical boundary condition when assessing building airflows. Therefore, this paper investigates the impact of different C p sources on wind-driven NVE results to see how sensitive the metric is to this variable. For that, an experimental house and a measurement period were used to develop and calibrate the initial BES model. Four C p sources are considered: an analytical model from the BES software (i), surface-averaged C p values for building windows that were calculated with Computational Fluid Dynamics (CFD) simulations using OpenFOAM through a cloud-based platform (iia,b,c), and two databases—AIVC (iii) and Tokyo Polytechnic University (TPU) (iv). The results show a variance among the C p sources, which directly impacts airflow predictions; however, its effect on the performance metric was relatively small. The variation in the NVE outcomes with different C p 's was 3% at most, and the assessed building could be naturally ventilated around 75% of the investigated time on the first floor and 60% in the ground floor spaces. [ABSTRACT FROM AUTHOR]

Lightweight concrete offers numerous advantages for modular construction, including easier construction planning and logistics, and the ability to offset additional dead loads induced by double-wall and double-slab features. In a previous study, authors proposed incorporating lightweight aggregate into foamed concrete instead of adding extra foam to achieve lower density, resulting in lightweight concrete with an excellent strength-to-density ratio. This paper further investigated the performance aspects of foamed concrete with lightweight aggregate beyond mechanical strength. To evaluate the effect of aggregate type and foam content, three mix compositions were designed for the lightweight concrete. Specimens were prepared for experimental tests on thermal conductivity and drying shrinkage of lightweight concrete. Results showed that while both the increase in foam volume and the incorporation of lightweight aggregate led to higher drying shrinkage, they also contributed to improved insulating properties and reduced potential of cracking. Using typical multi-storey modular residential buildings in Hong Kong and three other Chinese cities as case studies, simulations were performed to assess potential savings in annual cooling and heating loads by employing the proposed lightweight concrete. These findings demonstrate the practical benefits of using foamed concrete with lightweight aggregate in modular construction and provide valuable insights for further optimization and implementation. [ABSTRACT FROM AUTHOR]

Building energy simulation analysis plays an important supporting role in the conservation of building energy. Since the early 1980s, researchers have focused on the development and validation of building energy modeling programs (BEMPs) and have basically formed a set of systematic validation methods for BEMPs, mainly including analytical, comparative, and empirical methods. Based on related papers in this field, this study systematically analyzed the application status of validation methods for BEMPs from three aspects, namely, sources of validation cases, comparison parameters, and evaluation indicators. The applicability and characteristics of the three methods in different validation fields and different development stages of BEMPs were summarized. Guidance were proposed for researchers to choose more suitable validation methods and evaluation indicators. In addition, the current development trend of BEMPs and the challenges faced by validation methods were investigated, as well as the existing progress of current validation methods under this trend was analyzed. Subsequently, the development direction of the validation method was clarified. [ABSTRACT FROM AUTHOR]

The applications of building energy simulation (BES) in designing heating, ventilation, and air conditioning (HVAC) systems are limited by the high costs of developing simulation models and the lack of references for determining the model parameters. This paper presents a software framework for selecting designs for rooftop unit HVAC (RTU) systems with BES. Specifically, this framework reduces the cost of using BES by automating the generation of EnergyPlus models. It also employs a systematic method for determining model parameters based on well-accepted datasets. We applied this framework in a comprehensive assessment of an advanced design of RTU systems in which 478 EnergyPlus models were developed without human involvement. The assessment reveals that replacing a constant-speed fan/coil with a multiple-speed fan/coil may not guarantee better overall performance. It also suggests the benefits of replacing furnace coils with heat pumps are subject to utility cost, weather conditions, and heating load profiles. [ABSTRACT FROM AUTHOR]

Featured Application: Our study introduces an innovative application through the deployment of the Totem Outdoor DELUX interactive kiosk, complemented by a user-friendly web application. This dual-component system is not solely a cutting-edge piece of smart furniture centered on hardware; it also encompasses a specially designed web application to engage the citizens of Balaguer. Together, they form a comprehensive infrastructure aimed at enhancing environmental awareness and promoting civic participation. This kiosk is a dynamic platform for displaying real-time data from sensors across the city, facilitating interactive questionnaires, and providing essential information about the city and town hall. The application of this technology directly contributes to several Sustainable Development Goals (SDGs) by fostering public awareness and participation in critical issues such as climate action, health and well-being, gender equality, innovation in infrastructure, and reducing inequalities. Specifically, the interactive totem allows for the collection and analysis of data regarding citizens' perceptions of their living conditions, environmental impact, and social inequalities. This practical application of our work exemplifies how technological innovation can be leveraged to enhance civic engagement, support sustainable development, and foster a more informed and participatory urban community. The concept of a smart city is becoming increasingly popular to improve citizens' quality of life. Institutions are also committed to enhancing the sustainability of cities by implementing the Sustainable Development Goals (SDGs). This paper presents a Balaguer case study investigating energy demand monitoring, decreasing energy demand, and citizen acceptance in a municipality district. The study collected data from three sources: (1) quantitative data coming from on-site sensors; (2) quantitative data from a simulation of the area; and (3) qualitative data from questionnaires developed with a totem located in the city center. This study shows the importance of citizen science in contributing towards the increased awareness of energy demand, renewable energy, and climate change. But it also shows how citizen science can improve research quality involving the municipality authorities. This study also was instrumental in contributing to the increase in awareness among municipality authorities and capacity building on the topic. This activity may also contribute towards the implementation of actions to reduce the energy demand in public buildings and helping them in deploying policies to decrease energy demand in buildings, increase the use of renewable energy, and increase awareness among citizens. The government will use the information gathered to develop policies for citizen improvement. [ABSTRACT FROM AUTHOR]

Reducing heat losses through the building envelope is one of the most important aspects to be met if the targets set by the European Union are to be achieved. In order to obtain a more realistic energy demand, dynamic heat transfer simulations are used to calculate the energy consumption of buildings, since steady-state calculations do not take into account the thermal mass in buildings. These dynamic simulations employ methods based on analytical models since numerical models are unsuitable for longer time periods. The analytical models used herein fall into the category of conduction transfer functions (CTFs). Two methods for computing CTFs that are addressed in this research are the Laplace method and the State-Space method. The objective of this paper is to verify the efficiency of the Laplace and State-Space methods for calculating the energy demand of a building in the case of heavyweight building elements and shorter sampling times, and to provide a means for improving the algorithms used by these methods. The Laplace and State-Space method algorithms were implemented in Mathematica, and the results were compared to EnergyPlus and TRNSYS, which use similar algorithms to calculate energy demand. It was shown in this paper that for the heavyweight wall element and a time step of 0.25 h, the difference between the total energy transferred through the inner surface was about 31% for EnergyPlus and 78% for TRNSYS compared to the reference solution. For the lightweight wall element, the results were stable for the time step of 0.25 h, but for the time step of 0.1 h, the differences were 45.64% and 303% between EnergyPlus, TRNSYS and the reference solution, respectively, compared to the State-Space and Laplace methods for which the maximum difference was 12.03% with a time step of 0.1 h. While dynamic heat transfer simulations are better than calculations based on steady-state boundary conditions, they also have their limitations and could lead to unsatisfactory results for short sampling times and if not applied properly. [ABSTRACT FROM AUTHOR]

The climate data used for dynamic energy simulation of buildings located in urban regions are usually collected in meteorological stations situated in rural areas, which do not accurately represent the urban microclimate (e.g., urban heat island effect), and this might affect the simulation accuracy. This paper aims at quantitatively evaluating the effects of heat island on a high-rise building's energy performance based on the microclimate simulation tool ENVI-met and the building energy simulation tool COMFIE. However, the computation of microclimate models is time consuming; it is not possible to simulate every day of a year in a reasonable time. This paper proposes a method that generates hourly "site-specific climate data" to avoid long microclimate simulation times. A coupling method of ENVI-met and COMFIE was developed for more precise building energy simulation, accounting for the heat island effect. It was applied to a high-rise building in Wuhan, China. The results showed that the yearly average urban heat island effect intensity at the height of 3 m was estimated to be 0.55 °C and decreased with height. Compared to the simulation considering the outdoor temperature variation with the height and orientation, using the original climate data collected in rural areas led to an overestimation of the heating load by around 5.8% and an underestimation of the cooling load by around 8.7%. Compared to the weather file at the height of 3 m near the north facade neglecting the temperature variation along the height, the heating load was overestimated by 8.2% and the cooling load was underestimated by 10.8%. The methods proposed in this paper can be used for the more precise application of urban building energy simulation. [ABSTRACT FROM AUTHOR]

The process of urbanization resulting from population growth is causing a transformation of natural landscapes into built environments, and contributing to a significant rise in air and surface temperatures in urban areas, resulting in what is known as the urban heat island (UHI). Ignoring the UHI effect and use of weather data from open fields and airport locations for energy and thermal comfort analysis can lead to over- and underestimation of heating and cooling loads, improper sizing of equipment, inefficiencies in the mechanical systems operation, and occupants' thermal discomfort. There is a need for computationally efficient urban canopy temperature prediction models that account for the urban morphology and characteristics of the study area. This paper presents the development and application of an artificial neural network (ANN)-based method for generating hourly urban canopy temperature and local wind speed for energy simulation. It was used to predict the urban canopy temperature of a neighborhood in downtown Vancouver and the resulting building energy consumption and indoor temperature in a typical building in the area. The results showed that the UHI effect increased the total cooling energy demand by 23% and decreased the total heating energy consumption by 29%, resulting in an overall negative effect on the total energy demand of the building, which was 18% higher in the urban area. The UHI effect also increased the number of hours of indoor temperature above the cooling set point by 7.6%. The methodology can be applied to determine the urban canopy temperature of neighborhoods in different climate zones and determine the varying urban heat island effects associated with the locations. [ABSTRACT FROM AUTHOR]

Innovative building materials are being used in building envelopes for reducing their heating and cooling needs. This paper aims to assess the thermal impact of using lightweight concrete in Lebanese building constructions by pouring an 8 cm thickness of lightweight concrete on the roof and the slab and replacing traditional hollow concrete block by lightweight concrete blocks. Thermal properties of two different samples were experimentally determined: the first one (558 kg/m3) used for the roof and the slab and the second one (1074 kg/m3) used for the walls. Then numerical simulations were carried out for a Lebanese traditional detached house using the characteristics of these two samples. The thermally improved Light Weight Concrete building (LWC) was compared to a traditional Lebanese house base case (BC) using a dynamic building energy simulation tool in the four different Lebanese climate zones: coastal, mid-mountain, mountain, and inland zones. The results highlight the effectiveness of integrating LWC to building envelopes by reducing energy consumption and improving thermal comfort in both winter and summer climate conditions and in the different Lebanese climatic zones. The paper demonstrates that the use of LWC in the vertical walls replacing the traditional hollow blocks can reduce the heating needs by up to 9% and by up to 13% for cooling needs. On the other hand, adding a LWC roof screed has a very high impact on cooling and heating energy consumption, which can reach up to 74% in cooling energy savings and up to 24% in heating energy savings. [ABSTRACT FROM AUTHOR]

• Publicly available data was collected to construct reference buildings. • Random personnel behavior was modeled using time use survey data and Markov chains. • Building scenes in unbuilt areas were created using scenario generation algorithms. • A 28.3 % reduction in prediction error was achieved compared to traditional methods. • A more precise spatial distribution of energy consumption is illustrated. Urban Building Energy Model (UBEM) is a widely used method for load forecasting in urban energy planning. It typically depends on reference buildings to simplify particular scenarios and uses energy calculation software for load forecasting. However, significant challenges exist when using this approach in unbuilt areas, because there is no established distribution of buildings or a widely recognized database of reference buildings. To overcome this technical barrier, this paper introduces a new workflow - Scenario Urban Building Energy Model (S-UBEM). This approach utilizes publicly available databases to establish a reference building database and a stochastic human behavior database. By utilizing these databases and planning documents, the methodology can construct building energy use scenarios for the undeveloped area, ultimately achieving load forecasting. In this paper, the proposed method is validated by transforming the built-up area into an unbuilt state. The results demonstrate that S-UBEM more accurately represents the spatial and temporal distribution of energy consumption. This is evidenced by a 28.3 % reduction in the annual energy prediction error compared to the conventional method, as well as by visualizing the distribution of energy consumption at the building level. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper introduces a novel building energy model reduction pipeline called ‘model-cluster-reduce’. It is centred around using clustering techniques to identify archetypes and eliminate redundant zones. An experiment was conducted in this paper using a detailed EnergyPlus model generated from building information modelling directly. A total number of four reduced models were generated and compared against the original model, random select models and an expert model. The reduced models estimated annual energy simulation and parametric simulation results within 5% error margin, while reducing the overall simulation time by 95%. The proposed method - which is aimed at large models where inter-zone heat transfer is not significant - can be used to approximate parametric simulations or optimizations with greatly reduced runtime. [ABSTRACT FROM AUTHOR]

This paper presents controlling and optimizing the energy performance of buildings using smart shadings. Simulations are carried out using EnergyPlus and multi-objective optimization is performed by jEPlus+EA through NSGA-II algorithm. Optimization of control strategies is performed for a typical office room on the middle floor of a building in Tehran. Slat angle, solar radiation, and the material of smart windows are selected as decision variables. Also, the annual total building energy consumption, the predicted percentage of dissatisfaction (PPD), and the discomfort glare index (DGI) are considered as three objective functions minimized simultaneously. The weighted sum method to select the final answer of Pareto solutions is used. In the first strategy, a comparison of the results of optimization with the initial values when the angles of slats are constant and equal to 45 °showed that the total annual energy consumption, DGI, and PPD indexes reduced up to 11.74%, 6.4%, and 46.6%, respectively. In the second strategy, the reductions were 28.73, 56.50, and 34.05%, respectively, in comparison with the double-glazing window. The results clearly show how the correct selection of architectural parameters and control strategies can greatly prevent energy losses while providing the thermal and visual comfort of the building occupants. [ABSTRACT FROM AUTHOR]

Hygrothermal properties of building materials, climatic conditions and energy performance are interrelated and have to be considered simultaneously as part of an optimised building design. In this paper, a new approach to evaluate the energy consumption of residential buildings in Morocco is presented. This approach is based on the effect of coupled heat and moisture transfer in typical residential buildings and on their responses to the varied climatic conditions encountered in the country. This approach allows us to evaluate with better accuracy the response of building energy performance and the indoor comfort of building occupants. Annual energy consumption, cooling and heating energy requirements were estimated considering the six climatic zones of Morocco. Based on the results, terms related to coupled heat and moisture transfer can effectively correct the existing energy consumption calculations of the six zones of Morocco, which currently do not consider energy consumption due to coupled heat and moisture transfer. [ABSTRACT FROM AUTHOR]

With ever-rising energy demand and diminishing sources of inexpensive energy resources, energy conservation has become an increasingly important topic. Building heating, ventilation, and air conditioning (HVAC) systems are considered to be a prime target for energy conservation due to their significant contribution to commercial buildings’ energy consumption in the US. Knowing a building’s occupancy plays a crucial role in implementing demand-response HVAC controls, with a corresponding potential for reduction of HVAC energy consumption, especially in office buildings. This paper evaluates occupancy modeling (both binary detection and multi-class estimation) using twelve ambient sensor variables. Performance of six machine-learning techniques is evaluated in both single-occupancy and multi-occupancy offices. Of the six, the decision-tree technique yielded the best overall accuracy (i.e. 96.0% to 98.2%) and root mean square error (RMSE) (i.e. 0.109 to 0.156). The contribution of each individual ambient sensor variable is evaluated via information gain. It is found that CO2, door status, and light variables have important contributions to the final modeling results. It is observed that the overall accuracy generally increases as the number of sensors increases. This paper also examines the possibility of building a global occupancy model, and explores the reasons for low performance of global occupancy estimation. Lastly, the occupancy model is used to estimate and visualize the accumulative room and thermal zone usage in an office test-bed building for three months. The results reveal that the effective vacancy accounts for a substantial portion of the operational hours, varying from 19.8% to 29.8% with an average of 23.3%, which bears significant potential for energy savings. Furthermore, the authors simulated HVAC energy consumption of the test-bed building for three months in DesignBuilder and EnergyPlus, and compared energy consumption of occupancy-based demand-response HVAC controls using the authors’ occupancy-modeling results to the conventional HVAC controls currently implemented in the test-bed building. The results demonstrate that 20% of gas and 18% of electricity could be effectively saved if occupancy-based demand-response HVAC control is implemented. [ABSTRACT FROM AUTHOR]

Building's energy and water consumption and associated utility costs can be significantly reduced if proper energy and water-efficiency upgrades are applied. Due to the limitations of the upgrade budgets and the availability of a wide range of energy and water-efficiency measures, decision makers are always faced with a challenging task to identify optimal sets of building upgrade measures. This paper presents the development of a new model that is capable of identifying an optimum selection of building upgrade measures to minimize equivalent annual cost (EAC) while maintaining the performance of existing buildings and complying with available upgrade budgets. The model is developed in three main phases: (1) formulation phase, where decision variables, objective function, and constraints are identified and formulated; (2) implementation phase that performs the model computations and specifies the model input and output data; and (3) performance evaluation phase where two case studies of university buildings are analyzed to demonstrate the capabilities of the model. Furthermore, a case study from the literature is used to verify the model performance and document its new capabilities. The primary contributions that this research adds to the body of knowledge are: (1) developing new model that can identify optimal selection of building upgrades while complying with user-specified requirements for building operational performance and available upgrade budgets in short computation time, (2) modeling a wide range of upgrade measures for building fixtures and equipment as well as envelope components, and (3) integrating updatable databases from vendors and suppliers to generate up-to-date and practical recommendations. The model is designed to use OpenStudio to analyze combinations of feasible alternatives such as HVAC systems, wall and roof insulations, window types, and water heaters to calculate the corresponding building energy consumption. The model is designed to perform its computations using binary linear programming to identify global optimum solutions in short computational time. The results of the case studies illustrated the capabilities of the model in optimizing building upgrade measures to reduce the building utility costs up to 38% while complying with specified upgrade budgets. [ABSTRACT FROM AUTHOR]

Weatherizing residential buildings to make them more energy efficient offers great potential for reducing greenhouse gas emissions, conserving resources, and improving occupant comfort and well-being in the face of climate change. However, weatherization adoption rates remain low. Previous research suggests that social norms and energy-related interactions within a community have greater influence on households' weatherization adoption decisions than financial incentives. Therefore, an increased understanding of how social factors can be leveraged to encourage more households to weatherize is needed. This paper describes a hybrid simulation model on households' weatherization adoption decisions in an urban community. The simulation model integrates a building energy simulation model with an agent-based model that is embedded in a social network to represent the interactions, adaptations, and decision making of individual households over time. The model is demonstrated for several experimental scenarios for a residential block in the City of Des Moines, Iowa. Results indicate that social connectedness, community leaders, and delivery mechanisms for weatherization assistance all have a significant impact on household weatherization adoption. These results provide insight into the potential of policy interventions to increase weatherization adoption. [ABSTRACT FROM AUTHOR]

This paper introduces a scientific approach for the simulation and analysis of electromagnetic radiation (EMR) patterns indoors. It applies physics laws in analyzing EMR interaction within real-like 3-D buildings with plastered walls, illustrated by a visually scalable representation. The research objective is to investigate the effects of different roof shapes and some architectural parameters on EMR patterns in buildings using a CST simulator. The effects of new materials such as treated cement mixtures and sheets of Carbon Nanotubes composite materials are also examined. The shielding effectiveness within the zones of human activity of the model buildings are evaluated. The results identify the determinant parameters for EMR shielding and pinpoint potential locations for energy harvesting. The simulation results of the multi-layered wall are verified using MATLAB. Field measurements are performed on an actual building. The measurement results support the efficiency of the simulator in evaluating EMR levels indoors with an acceptable error. [ABSTRACT FROM AUTHOR]

A large part of the energy for heating and cooling is consumed due to human needs for thermal comfort in the living environment. It is important to use a suitable method to design an energy-efficient building to satisfy the thermal comfort of residents throughout the year. This paper focuses on the implementation of the sensitivity analysis in the area of energy analysis of a prefabricated timber building on box models. The sensitivity analysis plays an important role in early planning, partially also presenting the energy analysis. A combination of the building energy simulation and the sensitivity analysis technique identifies key variables by order, which can either have a strong or insignificant impact on the further design of the energy efficiency of a building. Moreover, this paper presents a sensitivity analysis method performed on the case study of six different timber building models where design parameters, such as geometry, insulation features, glazing ratio and distribution, shading control and interior set-point temperatures, are varied in environments with different climatic conditions: moderate (Ljubljana), warm (Athens), and cold (Helsinki). The sensitivity analysis for a range of variable input parameters is conducted by using the global sensitivity analysis technique, i.e. the Morris method, which is based on the elementary effect. The main objective of the article is to provide guidance for researchers, professionals and building planners about the parameters they should focus on in the early planning of an energy-efficient timber building to reduce the energy required for heating and cooling, thereby ensuring constant thermal comfort in the living environment. The results of the sensitivity analysis, which serve as guidelines, show that the overall U-factor of a window, the solar heat gain coefficient, and a set-point temperature for heating are design variables that most affect the heating energy demand for the considered one- and two-storey box models in the three locations. The dominant parameters have on average 30% more influence on heating energy demand in cold climates than parameters in moderate climates and 88% more than in warm climates. While shading control and solar heat gain coefficient are the most influential parameters for cooling energy demand, with the dominant parameters in warm climates having on average 39% more influence than parameters in moderate climates and 49% more influence than parameters in cold climates. In the total energy demand for the operation of all box models under all climatic conditions, special attention should be paid to shading control. In addition, in moderate and cold climates, the overall U-value of the window and the internal set- point temperature in winter must be considered, and in hot climates, the solar heat gain coefficient is important. In the case of the two-storey box model, designers need to be careful about the glazing-to-wall ratio which depends on the orientation of the glazing (south-north or east-west). In this case, the dominant parameters have on average 47% more influence in warm climates than in cold climates and 56% more than in temperate climates. • Prefabricated timber-framed box models located in cold, moderate and warm locations were introduced. • Architectural parameters that minimize the total energy demand are searched. • The Morris method was used to identify the most influential parameters. • A guideline for early design of energy efficient timber buildings under different climatic conditions was developed. • Window characteristics, AGAW, shading and set-point temperature in winter have the strongest influence on energy demand. [ABSTRACT FROM AUTHOR]

Climate change and its effects are becoming clear on a global scale either from the perspective of global warming and the increase in the rate of occurrence of weather events of extreme magnitude. This has impacts also for sure on the standard building performance analysis approach, since the buildings designed today are supposed to withstand for the following decades climate impacts that may be different than those they were designed for.The paper proposes a simple, easy to use and freely available building simulation utility which performs morphing of existing weather data files and, by connecting to the Energy Plus simulation routine, allows to perform future climate building simulation analyses. Users are required to select one of the ASHRAE buildings models or provide one of their own choosing and to input the original weather data file. The tool will generate a future weather data file with the preferred assumptions (e.g. RCP scenarios, time frame) and elaborate results in terms of heating and cooling required for air conditioning.The paper proposes also an implementation of the tool to a case study aimed at showing the potential of the application proposed. A typical office building model from the ASHRAE library was simulated in two different locations under different climate change assumptions up to the year 2090. The analysis of the results in the two locations of Palermo (Italy) and Copenhagen (Denmark) highlight relevant increases in the current century of up to +20% of cooling requirements and similar reductions for heating in both case studies, if compared to current levels.The research targets a specific limit in the investigation of climate resilience of buildings and follows the principles described by SDSN in the definition of SDGs and the interest at the EU level towards climate neutral and innovative cities.In this context, the paper may contribute to the limited availability of easy to use and free tools available for practitioners to investigate the design of climate resilience buildings.

Double skin façades are façade technologies that have the perspective of reducing energy use and improving comfort in buildings due to their adaptable nature. Exhaust-air façades offer the possibility to utilize solar energy by recovering heat from the façade cavity. However, the cavity overheating can be detrimental on the summer performance. Predicting performance and optimizing the system during the design phase is a challenge, especially when the cavity-air is integrated into the HVAC system. Whole-building energy simulation (BES) software tools are an adequate tool for calculating whole building performance, although these can have limitations in the accurate replication of complex building elements. The paper analyses the available and applied modelling approaches within a BES tool, and compares the outputs in terms of cavity temperature, horizontal and vertical temperature profiles, and heat flux through the façade. The sensitivity of the results on the modelling approach is evaluated. Results can serve as a guide for practitioners on the selection of the modelling approach for a given task. [ABSTRACT FROM AUTHOR]

Solar radiation is an essential meteorological parameter for building energy efficiency analysis, and the quality of the data directly affects the analysis results. This paper investigates the estimation of hourly solar radiation based on the generation of the typical meteorological year(TMY) using various real meteorological parameters and limited solar radiation data. The focus of this paper is to use two types of neural network algorithms to improve the estimation accuracy and applicability, and to solve the problem of hourly solar radiation data acquisition in non-radiation areas. First, select two city station data and use three methods to generate TMY. Then, two neural network models, BP Neural Network (BP)，Convolutional Neural Network (CNN) are used to estimate the hourly solar radiation data and verify the results. Finally, by constructing a photovoltaic-integrated office building model, the accuracy of the hourly solar radiation estimation model is verified using energy consumption simulation and photovoltaic (PV) power generation simulation. The results show that this paper can well solve the problem of limited radiation data, which provides a new idea for the study of building energy efficiency in areas where radiation data is missing. • Multiple neural network is used to estimate the generation of solar radiation data using other meteorological parameters. • An advanced neural network is used to provide an effective TMY generation method in missing hourly solar radiation data. • The accuracy of the experimental results is verified by energy consumption simulation and photovoltaic power generation. [ABSTRACT FROM AUTHOR]

Recently, there has been a growing interest in sustainable agricultural methods aimed at reducing energy consumption and mitigating environmental impacts. Among these methods, the integration of rooftop greenhouses into buildings has emerged as a promising solution for urban agriculture, offering various advantages such as energy-saving effects on both the host building and the rooftop greenhouse, improved resource use efficiency, enhanced food security, and reduced environmental impacts. However, there has been limited research on the energy-saving impact of rooftop greenhouses under different conditions and detailed heat transfer analysis based on actual buildings and rooftop greenhouses. The objective of this study is to investigate the energy benefits of building-integrated rooftop greenhouses by evaluating various operating scenarios for both the building and the greenhouse. A container and upper rooftop greenhouse structure were constructed, and building energy simulation models for the target system were developed. Four different operating scenarios for the greenhouse and three different operating scenarios for the building were compared. Depending on the operating scenarios, the total energy loads of the container and RTG were reduced by 18.4% to 24.7% and 0.7% to 6.3%, respectively. The findings of this study can be utilized for the development of optimized greenhouse control strategies. [ABSTRACT FROM AUTHOR]

Building upon the knowledge accumulated from a comprehensive research program on double skin façades (DSFs), this study explores two design enhancements for buildings in tropical climates. Thermal and energetic benefits of the inclusion of mechanical ventilation within the DSF cavity and electricity generation from the PV systems have been determined across a wide spectrum of Brazilian climates. Using computational simulations, DSF models with two fan configurations, placed solely at the top of the DSF and distributed along the height of the cavity, are evaluated. Annual distribution of thermal comfort acceptance and impact on energy demands from HVAC systems and contribution from the PV systems is analyzed. Results demonstrated wide variations of energy performance in relation to the climatic conditions. The 'Cool' climate zone presented most significant benefits from the proposed design enhancements resulting in electricity surplus. On the other hand, electricity generated from PV in the moderate climate zone can only cover about 30% of the HVAC energy consumption, whereas energy savings in the 'hot' zone are the lowest with only 15% of electricity contributed from the PV systems. The results of this paper contribute to the early stage of design when considering the adoption of DSF in tropical climates. [ABSTRACT FROM AUTHOR]

To better predict the effective energy performance of buildings in cities, this study addresses the modelling of local external radiative, thermal and aeraulic conditions. After reviewing existing modelling approaches that are suitable for estimating the building boundary conditions in energy simulation, this paper analyses external conditions derived from a building energy model (BuildSysPro) or a microclimatic model (SOLENE microclimat). Comparisons are made for the different faces of a generic building standing alone or located in an urban environment, with or without a thermally efficient envelope. When the modelling approach is adjusted, the results highlight significant deviations on the estimated radiative temperatures and wind-based quantities around the isolated building. When accounting for surrounding buildings, the results show a substantial reduction in short-wave radiative fluxes, which is explained by an imbalance between solar masks and multireflections, and a reduction in the wind-driven ventilation potential. [ABSTRACT FROM AUTHOR]

• A theoretical framework is proposed to consider non-equilibrium configurations at the macroscopic scale. • The new formulation is validated using a full dual-scale model of coupled heat and mass transfer. • With the new model, a very good accuracy is obtained with a huge reduction of CPU time. • Dual-scale diffusion and molecular relaxation can be considered additively in the modified macroscopic formulation. • This framework can be applied to other configurations, such as coupled transfer in packedbeds. This paper proposes a macroscopic formulation of coupled heat and mass transfer that can consider non-local equilibrium often encountered in biosourced building materials (wood- and plant-fiber based materials). Transferring dual-scale effects and molecular relaxation at the macroscopic level involves a kernel function acting in a convolution product. To ease the computational solution of the set of equations, the memory function is decomposed as a series of exponential functions. Each function yields an internal variable that obeys a simple ordinary differential equation (ODE). This paper first describes the macroscopic and dual-scale formulations used as reference solutions. Subsequently, the modified macroscopic formulation of coupled transfer and its computational solution are presented in detail. The major outcomes of the present study, validated against reference solutions obtained with a comprehensive dual-scale model, are as follows: • Dual-scale diffusion can be approached accurately by two exponential functions, • Even though the dual-scale phenomenon and molecular relaxation do not occur at the same scale, both can be considered in the modified macroscopic formulation of coupled transfer additively, • The new macroscopic formulation, together with the computational procedure proposed in this study, can be applied to various configurations, namely coupled heat and mass transfer in packed beds. [ABSTRACT FROM AUTHOR]

Highlights • A new experimental procedure to test PCM-enhanced components is presented. • A modified Dynamic Heat Flow Meter Apparatus is used to perform the tests. • The thermal conductivity of PCMs is measured in different phases (solid-liquid). • Indications to solve the issues related to building PCM-specimens are provided. Abstract The implementation, in Building Performance Simulations (BPS) tools, of robust models capable of simulating the thermophysical behaviour of a Phase Change Material (PCM) represents a fundamental step for an appropriate thermal evaluation of buildings that adopt PCM-enhanced envelope components. Reliable and robust measuring procedures are essential, at a material and component level, to provide experimental data for the empirical validation of software tools. The traditional laboratory tests that are generally used for the validation of models present some limitations, because PCMs are usually subjected to conditions that may be very different from the real boundary conditions of the building components in which PCMs are applied. Furthermore, in many experimental full-scale mockups, the relatively small quantity of installed PCM and the combination of several thermal phenomena do not allow software tools to be tested in a reliable way. In this paper, an experimental procedure, based on a modified Heat Flow Meter Apparatus, has been developed to test the behaviour of PCM-enhanced components; the procedure, which is based on the measurement of the sinusoidal response, has been set up to provide data for the comparison and testing of numerical models and of BPS tools. Moreover, general indications and guidelines are provided to solve some issues related to building specimens that contain bulk PCM in order to obtain a more accurate measurement of their performance. The experimental results presented in this paper were obtained from two different bulk PCMs (organic and inorganic). It was found that it is important to evaluate different PCM typologies and different thermophysical boundary conditions, including partial and full phase transitions, to test simulation codes that implement PCM modelling functions. In fact, some phenomena, such as hysteresis and subcooling effects are more evident when partial phase transition takes place. The results related to the characterization of the thermal conductivity of a paraffin-based PCM have shown a significant increase (up to 42%) of the equivalent thermal conductivity from a solid to a liquid state, with an upward heat flux, thus highlighting that further investigations and improvements are needed to measure the equivalent thermal conductivity in the different PCM phases. [ABSTRACT FROM AUTHOR]

Abstract Building Information Modeling (BIM) initially emerged as a capability to transfer information and allow interaction or interoperability of various software tools used for architectural, structural, mechanical, electrical design, and building construction. Its application from developing 3D models, structural analysis, cost estimation, and mechanical analysis has now expanded to other applications such as energy simulation. Multiple computer-aided design (CAD) tools work as BIM authoring tools to generate BIM files in different formats containing various types of building information. In addition, there are various building energy modeling (BEM) tools capable of importing these BIM files to perform energy simulation. However, such tools have various capabilities and limitations and need to be investigated and categorized in order to facilitate choosing a proper tool for design professionals in different phases of project and purposes. In addition, interoperability and data exchange issues between BIM and BEM tools should be understood in order to find solutions such as developing proper corrective middleware tools to rectify them. This paper reviews the challenges, issues, and shortcomings in BIM-to-BEM interoperability process (BBIP) by proposing a detailed classification for these issues and studying the available solutions. The paper also explains how a corrective middleware, which is developed by the authors using Python, can be utilized to modify a gbXML file prior to adoption in energy simulation to resolve the issues related to building envelope in BBIP. To do so, initially a review is presented on research studies focused on different types of BIM schemas such as IFC and gbXML and energy simulation tools capable of reading these files such as Green Building Studio (GBS), DesignBuilder, Integrated Environmental Solutions-Virtual Environment (IES), and OpenStudio. In addition, some of the challenges in the application of BIM for energy simulation such as interoperability issues, lack of standards, and lack of easy solutions for extending existing BIM schemas and available corresponding solutions are also reviewed. With the focus on building envelope in mind, three case studies are discussed to observe the challenges and issues with respect to BBIP using Revit, GBS, and OpenStudio. Moreover, these case studies provide an opportunity to investigate the application of corrective middleware tools similar to what is developed in the study presented. Highlights • A brief review of Building Energy Modeling (BEM) tools is provided • Building Information Modeling (BIM) file types are reviewed and compared • Existing challenges, issues, and shortcomings in BIM to BEM interoperability process (BBIP) are identified • Existing solutions for issues in BBIP are reviewed and a corrective tool is developed and proposed to solve issues related to gbXML files involved in BBIP • A comprehensive categorization is proposed for issues in BBIP. [ABSTRACT FROM AUTHOR]

Occupancy is one of the main factors affecting building energy consumption. The occupancy data, which refer to the occupancy number in this paper, has been widely used in the building simulation field. However, due to the stochastic nature of occupant behavior, it is hard to predict and measure how many people stay in a given building. The rapid development of mobile Internet technology provides an efficient and convenient option for occupancy detection. This paper proposes a concept of typical occupancy data (TOD), which are extracted from real-time occupancy data collected by mobile devices. K -means algorithm is employed to generate the TOD data through cluster analysis. An energy performance model of an office building is used as a case study to demonstrate the effectiveness of the TOD data. [ABSTRACT FROM AUTHOR]

The building energy demand for heating and cooling is changing due to climate change. The adoption of climate change adaptation measures at the building scale aims at limiting heating and cooling demands. In previous studies on adaptation measures little attention has been paid to lightweight semi-portable buildings, which are increasingly used to temporarily house the growing number of small households (1-2 persons) in peripheral and derelict areas. In this paper the impact of passive climate adaptation measures and building orientation on heating and cooling demands is assessed for a detached, lightweight, semi-portable residential building by means of building energy simulations (BES), considering two climate scenarios for the Netherlands: current climate and a future climate (2050). The results show that the most efficient adaptation measure consists in a combination of exterior solar shading and an increase of thermal resistance of the building envelope, which reduces the annual heating and cooling demand-averaged over eight building orientations - by 11% for the current climate and 15% for the future climate. The impact of building orientation varies according to the climate scenario. Compared to the average over the eight orientations considered, the annual cooling demand for a single orientation varies between about −31% and +22% and between about −24% and +18% for the current and future climate, respectively. For the case without adaptation measures, optimizing the building orientation leads to annual total energy savings of about 4% for the current and 3% for the future climate. [ABSTRACT FROM AUTHOR]

Building and district energy systems become increasingly complex, requiring accurate simulation and optimization of systems that combine building envelope, heating ventilation and air conditioning, electrical distribution grids and advanced controllers. Hence, it becomes more challenging for existing simulation tools to provide integrated solutions for these multi-physics problems. Moreover, common building simulation frameworks tightly integrate model equations and their solvers in the program code, which affects model transparency and hampers tool extensions. This is contrasted by equation-based tools such as Modelica, for which different solvers can be used. In this context, the Integrated District Energy Assessment by Simulation (IDEAS) library is developed. After a recent development shift towards more detailed, multi-zone models, this paper presents a comprehensive, well-documented, overview of the buildings part of IDEAS. This includes new computational aspects of the library, improved usability aspects, an updated intercomparison with BESTEST and a verification based on IEA EBC Annex 58. [ABSTRACT FROM AUTHOR]

The building information modeling - BIM is a technology developed toward creation of computer based information model that encompasses whole building lifecycle. Toward that goal a number of information technology standards have been developed that enable different professions in AEC to cooperatively develop electronic building model. The paper gives overview of essential technologies, discusses their intended purpose, and gives outline of the currently achieved functionality. [ABSTRACT FROM AUTHOR]

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

Reducing heat losses through the building envelope is one of the most important aspects to be met if the targets set by the European Union are to be achieved. In order to obtain a more realistic energy demand, dynamic heat transfer simulations are used to calculate the energy consumption of buildings, since steady-state calculations do not take into account the thermal mass in buildings. These dynamic simulations employ methods based on analytical models since numerical models are unsuitable for longer time periods. The analytical models used herein fall into the category of conduction transfer functions (CTFs). Two methods for computing CTFs that are addressed in this research are the Laplace method and the State-Space method. The objective of this paper is to verify the efficiency of the Laplace and State-Space methods for calculating the energy demand of a building in the case of heavyweight building elements and shorter sampling times, and to provide a means for improving the algorithms used by these methods. The Laplace and State-Space method algorithms were implemented in Mathematica, and the results were compared to EnergyPlus and TRNSYS, which use similar algorithms to calculate energy demand. It was shown in this paper that for the heavyweight wall element and a time step of 0.25 h, the difference between the total energy transferred through the inner surface was about 31% for EnergyPlus and 78% for TRNSYS compared to the reference solution. For the lightweight wall element, the results were stable for the time step of 0.25 h, but for the time step of 0.1 h, the differences were 45.64% and 303% between EnergyPlus, TRNSYS and the reference solution, respectively, compared to the State-Space and Laplace methods for which the maximum difference was 12.03% with a time step of 0.1 h. While dynamic heat transfer simulations are better than calculations based on steady-state boundary conditions, they also have their limitations and could lead to unsatisfactory results for short sampling times and if not applied properly.

Relevant meteorological files are needed by simulation software to assess the energy performances of buildings or efficiency of renewable energy systems. This paper adopts the Sandia method to generate typical meteorological year (TMY), using a 35-year hourly measured meteorological dataset from four stations in the northern region of Nigeria. The cumulative distribution function (CDF) for each year was compared with that of the long-term composite of all the years in the period for the seven major weather indices made up of relative humidity, wind speed, minimum temperature, global solar radiation, precipitation, mean temperature and maximum temperature. The 12 typical meteorological months (TMMs) selected from the different years were used for formulation of a TMY for the zone. In addition, performance assessment of a 72-cell polycrystalline solar PV module using the generated TMY and long-term (LT) values was also conducted. Two statistical indicators, the mean percentage error and the root mean square error, were adopted to evaluate the performance of each TMY with the LT mean, and also that of the PV energy system. Findings show that the TMMs are evenly spread within the data periods across the sites while closest fit between the long-term mean and TMY are obtained with the global solar radiation followed by the mean temperature in all the sites especially in Bida and Minna. From the energy system analysis carried out, it was found that TMY data are able to predict the performance of the PV system to within 5% of the LT data. [ABSTRACT FROM AUTHOR]

Improved energy efficiency in the building sector is a central goal in the European Union and renovation of buildings can significantly improve both energy efficiency and indoor environment. This paper studies the perception of indoor environment, modelled indoor climate and heat demand in a building before and after major renovation. The building was constructed in 1961 and renovated in 2014. Insulation of the façade and attic and new windows reduced average U-value from 0.54 to 0.29 W/m²•K. A supply and exhaust ventilation system with heat recovery replaced the old exhaust ventilation. Heat demand was reduced by 44% and maximum supplied heating power was reduced by 38.5%. An on-site questionnaire indicates that perceived thermal comfort improved after the renovation, and the predicted percentage dissatisfied is reduced from 23% to 14% during the heating season. Overall experience with indoor environment is improved. A sensitivity analysis indicates that there is a compromise between thermal comfort and energy use in relation to window solar heat gain, internal heat generation and indoor temperature set point. Higher heat gains, although reducing energy use, can cause problems with high indoor temperatures, and higher indoor temperature might increase thermal comfort during heating season but significantly increases energy use. [ABSTRACT FROM AUTHOR]

The climate data used for dynamic energy simulation of buildings located in urban regions are usually collected in meteorological stations situated in rural areas, which do not accurately represent the urban microclimate (e.g., urban heat island effect), and this might affect the simulation accuracy. This paper aims at quantitatively evaluating the effects of heat island on a high-rise building’s energy performance based on the microclimate simulation tool ENVI-met and the building energy simulation tool COMFIE. However, the computation of microclimate models is time consuming; it is not possible to simulate every day of a year in a reasonable time. This paper proposes a method that generates hourly “site-specific climate data” to avoid long microclimate simulation times. A coupling method of ENVI-met and COMFIE was developed for more precise building energy simulation, accounting for the heat island effect. It was applied to a high-rise building in Wuhan, China. The results showed that the yearly average urban heat island effect intensity at the height of 3 m was estimated to be 0.55 °C and decreased with height. Compared to the simulation considering the outdoor temperature variation with the height and orientation, using the original climate data collected in rural areas led to an overestimation of the heating load by around 5.8% and an underestimation of the cooling load by around 8.7%. Compared to the weather file at the height of 3 m near the north facade neglecting the temperature variation along the height, the heating load was overestimated by 8.2% and the cooling load was underestimated by 10.8%. The methods proposed in this paper can be used for the more precise application of urban building energy simulation.

The paper presents an energy simulation-based study to investigate the impacts of climate change on energy consumptions of an office building located in five different cities, United States. Annual energy consumption of a medium-size office building was predicted by building energy simulation software—EnergyPlus using TMY3 weather data and future climate data. In this study, we have used two sets of further climate data with different climate models including (1) Hadley Centre Coupled Model, version 3 (HadCM3), and (2) NCAR Community Earth System Model version 1 (CESM1) with the Community Atmosphere Model version 5 (CAM5). A morphing method was applied to downscale the monthly weather forecast to hourly forecast for use in building energy simulation for the two General Circulation Models (GCMs): HadCM3 and CESM1. Using the generated future weather files, HVAC operation related mitigation measures including adjustment of thermostat setpoints, reduced HVAC operation hours, reduced VAV box minimum flow setting, and mixed-mode ventilation were simulated to compare with ASHRAE Standard 90.1 baseline simulated using TMY3 weather data. Simulation results on predicted whole building, heating and cooling energy consumptions were examined. The paper highlighted the importance of efficiently operating mechanical systems in buildings to ensure a more sustainable future. [ABSTRACT FROM AUTHOR]

The impacts of climate change, excessive greenhouse gas emissions, and the current energy crisis have motivated the European Union to adopt mitigation and adaptation strategies. These strategies primarily focus on the building sector due to its crucial role in addressing these issues. Among the strategies, the implementation of resilient technologies for the building envelope, such as vertical greenery systems (VGSs) is gaining ground. The literature analysis shows that existing models are not sufficiently detailed in their description of the overall thermo-physical phenomena of VGSs. The aim of this work is to overcome the research gaps by selecting and improving two mathematical models for green façades and living walls. A dedicated calculation code to estimate the effect of VGSs on a building's energy performance and indoor thermal comfort has been developed and implemented within the EnergyPlus calculation software (version 23.2). A BESTest Case from ASHRAE 140 was chosen to test the models and to assess benefits of VGSs. The results show that adopting green solutions for the building envelope can contribute to achieving the building's energy efficiency goals and that the modelling of these technologies can be easily carried out within a dynamic energy simulation of the building. [ABSTRACT FROM AUTHOR]

Net zero energy building (NZEB) is an efficient approach to boost the world’s sustainable development and climate mitigation. This paper provides a comprehensive review of multi-objective optimization (MOO)-based case studies of NZEB design. The critical analysis focuses on the “3Ps” criteria of sustainability, which encompass social, environmental, and economic aspects of protecting the people, planet, and profit. This study identifies the gaps in exploring case studies based on MOO techniques for decision-making regarding NZEB design in India and its requirements. Moreover, it demonstrates various passive design strategies, energy-efficient technologies, and renewable energy, and their impact on occupant comfort, energy model, and the building life cycle. It presents the global needs and requirements of NZEB, and definitions of NZEB in different countries and their policies. This review suggests a four-step methodological framework for NZEB design in India. It provides the potential and reliability of various algorithms, tools, and simulation engines to solve building problems. The framework presented here can aid designers in the decision-making of NZEB design, refurbishment, and renovation. Moreover, this study highlights the future research direction and potential challenges for implementing the proposed framework in India.

To align the buildings sector with the 1.5°C climate change trajectory, enormous improvements in energy efficiency are needed. It is therefore crucial that the tools used to evaluate buildings’ energy use undergo robust testing. This paper tests, for the first time, the outcome of the Passive House Planning Package (PHPP) and an author-modified version of the Radiant Time Series Method (RTSM), following the Building Energy Simulation Test (BESTEST). The results show that while the validity of the modified-RTSM is slightly superior to that of the PHPP, both tools pass less than 35% of the cases—necessitating further calibration and challenging the widely-held belief that the PHPP is ‘validated’. As the PHPP and the modified-RTSM present a relatively simple and quick way of evaluating buildings’ energy performance, calibrating their methodologies so they pass the BESTEST cases could put them at an advantage over the fully dynamic and resource-intensive tools.

The paper presents a review of existing literature in the field of coupling Computational Fluid Dynamics (CFD) with Building Energy Simulations (BES) to better predict indoor environmental conditions and building energy implications. CFD is capable of providing a detailed analysis of airflow profile and temperature gradients in the space as well as better prediction of heat transfer involving convection and radiation. Whereas BES can provide dynamically changing boundary conditions to CFD to facilitate a precise transient analysis. Combining the two simulations provides a powerful framework to accurately predict building performance parameters. The review examines the variables exchanged between the two simulations and establishes that the Convective Heat Transfer Coefficient (CHTC) as the most important exchanged variable that can significantly improve the accuracy of energy simulations. Issues regarding the application of co-simulation mechanism are then discussed in terms of simulation discontinuities, along with strategies adopted by researchers to overcome the same. In the later sections, the review evaluates the applicability of co-simulation from the perspective of year-long building energy simulations and presents an overview of methods used in research to implement the same. Finally, the conclusions are discussed and the scope for future research in the field is presented. Practical implication : The review presents a critical analysis of essentially all major coupling strategies that can be used to perform a BES-CFD coupled analysis along with their strengths, limitations and possible application scenarios. Additionally, the problems associated with establishing the co-simulation are examined and various adopted solutions are presented along with methods implemented towards extending the practical applicability of such an analysis to encapsulate year-long simulations. [ABSTRACT FROM AUTHOR]

Globally, a considerable amount of energy is consumed by the building sector. The building envelope can highly influence the energy consumption in buildings. In this regard, innovative technologies such as thermal energy storage (TES) can help to boost the energy efficiency and to reduce the CO 2 emissions in this sector. The use of phase change materials (PCM), due to its high heat capacity, has been the centre of attention of many researchers. A considerable number of papers have been published on the application of PCM as passive system in building envelopes. Researches have shown that choosing the PCM melting temperature in different climate conditions is a key factor to improve the energy performance in buildings. In the present paper, a simulation-based optimization methodology will be presented by coupling EnergyPlus and GenOpt with an innovative enthalpy-temperature (h-T) function to define the optimum PCM peak melting temperature to enhance the cooling, heating, and the annual total heating and cooling energy performance of a residential building in various climate conditions based on Köppen-Geiger classification. Results show that in a cooling dominant climate the best PCM melting temperature to reduce the annual energy consumption is close to the maximum of 26 °C (melting range of 24–28 °C), whereas in heating dominant climates PCM with lower melting temperature of 20 °C (melting range of 18–22 °C) yields higher annual energy benefits. Moreover, it was found that the proper selection of PCM melting temperature in each climate zone can lead to notable energy savings for cooling energy consumption, heating energy consumption, and total annual energy consumption. [ABSTRACT FROM AUTHOR]

This paper focuses on the effectiveness of passive adaptation measures against climate change, in the medium (2036-2065) and long term (2066-2095), for three case studies located in Florence (central Italy). In order to identify and highlight the passive measures which can provide comfort conditions with the lowest net heating and cooling energy demand, the input assumptions consider a constant thermal comfort level and don't take into account either the effect of HVAC system's performance and the degradation of the materials by ageing. The study results show that, in case of poorly insulated buildings, on the medium term, the reduction of energy needed for heating could be bigger than the increase for cooling, resulting in a total annual net energy need decrease, while in the long term the opposite happens. Conversely, considering a high level of thermal insulation, due to the large increase in cooling demand, the total annual energy need rises in both periods. Furthermore, attention should be paid to internal loads and solar gains that, due to the projected climate change, could become main contributors to the energy balance. In general, since the magnitude of energy need increase for cooling and decrease for heating is very significant on the long term, and varies in function of the type of building, the passive measure adopted and the level of thermal insulation, the research results lead to pay close attention to different types of energy refurbishment interventions, that should be selected in function of their effectiveness over time. [ABSTRACT FROM AUTHOR]

In this paper we propose an algorithm to find optimal design of skylight for a one-storey office building while saving source energy consumption. The algorithm simultaneously couples daylighting with energy performance and compares different ratios of skylight to floor area based on both lighting and HVAC loads. Our method integrates different simulation tools with numeric optimization and exhaustive search to find the optimal ratio. More specifically we implemented EnergyPlus as a thermal engine and Radiance as a daylight engine, both of which are embedded in Ladybug and Honeybee. As Ladybug and Honeybee is a plugin for Grasshopper, we developed the algorithm in Grasshopper environment. We used Python and Grasshopper to integrate different simulation tools and implemented two methods of numeric optimization (gradient descent) and exhaustive search (parametric analysis) in order to validate the final result. After applying the proposed algorithm for a small office building in San Francisco, both methods coincide that the optimal skylight to floor ratio is 5.5–6% while decreasing the energy demand by 19%. The experiments show that the energy efficiency only occurs for skylight ratios of 3–14%. In addition, we used sDA and UDI metrics to analyze daylighting performance of each parametric scenario. We infer that only a specific range of skylight ratios that are energy efficient (5–10%) provide adequate daylight and avoid glare. The integrative process and optimization method proposed in this paper enable designers to find a robust energy efficient skylight design. [ABSTRACT FROM AUTHOR]

Building simulation tools have been widely used for performance assessment. However, many studies [1] have reported that a performance gap exists between the reality and simulation output, mainly caused by unknown simulation inputs. Therefore, model calibration needs to be introduced. Calibration attempts can fail for the following reasons: coarse initial simulation model, long sampling time, uncertainty in the model, and sensor errors. The aim of this paper is to address the abovementioned issues. For this study, an existing office building was selected and two calibration approaches were presented: deterministic vs. stochastic. For stochastic calibration, a Gaussian Process Emulator (GPE) was introduced as a surrogate of the EnergyPlus model. The stochastically calibrated model performs better than the deterministically calibrated model. It is concluded in the paper that (1) the calibration quality is influenced by the degree of the details of the initial model, (2) the accumulated measured data under a sampling time of up to one day (e.g. gas energy consumption) might be unsuitable for calibration work due to the lack of ‘time-series trend’, and (3) the calibration quality is also influenced by sensor errors and further calibration needs to take these into account. [ABSTRACT FROM AUTHOR]