1. Introduction
1.1. Background
Construction projects nowadays are becoming complex and demanding, as the designer needs to fulfill multi-dimensional constraints. To meet this demand, information and communication technologies (ICTs) can play their role as they have proved to be successful in other complex engineering problems. BIM is an extension of traditional 2D concept development techniques (i.e., plans, sections, and elevations) to a 3D environment. The concept of BIM was first introduced in the building industry by Robert H. Sproul who proposed a system that encompasses the entire process of building design and construction, including management, maintenance, and service life. However, there are varying opinions on the roots of BIM. A study by Gobesz [1] believes that ideas about the computer modeling of structures that began in the 1970s were to incorporate the growing needs of construction industries, and this became the foundation for existing Computer-Aided Design (CAD) standards to integrated BIM standards.
BIM has its origins in the construction industry but has become a standard within the building and engineering industries as well. It represents a new paradigm within the Architectural Engineering and Construction (AEC) industry. The technology supports data modeling, 3D rendering, virtual construction environment, construction management and project delivery [2]. Unlike software applications that tend to focus on a limited number of functional-based deliverables, BIM gives model-based cost estimation, helps visualize the projects in preconstruction and improves the scheduling and sequencing of each project. BIM supports the collaboration of all relevant disciplines [3], including architecture, interior design, engineering and construction services providers involved in a project as well as planners, investors/owners, government agencies and consultants [4]. BIM is a neutral platform, meaning it can be tailored to support the way each stakeholder views the project. For example, a structural engineer can use BIM to review structural calculations, while an architect can examine design and layout to determine how much space is available for building equipment. Similarly, investors/owners can assess the amount of rent that will be generated from different spaces in a building, and government agencies can evaluate whether the facility meets public health standards [5]. However, like any other new technology development, there are significant challenges that need to take care of the recent advances in the construction industry for a variety of applications (based on demographics, economics, financial needs and user demand, to name a few).
Urbanization, demand for life quality, global warming and growing economies have increased the energy utilization of buildings significantly. With recent concerns about the energy efficiency of buildings, integrating BIM with Building Energy Modeling (BEM) provides an innovative opportunity to exploit the full potential of BIM to optimize energy consumption from existing energy-efficient building automation systems (EBASs). BEM is a form of computational analysis that can be used in conjunction with BIM or Computer-Aided Engineering (CAE) systems. In such a scenario, BIM models could be used to create digital maps that show where and how much energy is being consumed in buildings.
1.2. Building Energy Modeling
BEM is widely used in the design, commissioning and operation, achieving compliance with energy standards and economic optimization of a variety of building structures and their entities. For instance, simulating how a cooling system responds to a specific set of operating conditions with BEM is cheaper than building a test unit. BEM can be widely applied in energy engineering, which is one reason that it has been growing in importance.
A BEM is a computer simulation that considers all of the physical effects within a building, such as thermal conduction, convection, radiation and phase changes [6]. Calculating the heat flows through a building using these physical effects is known as the thermal steady-state approach. The simulation is performed in a 3D space, and the location of each surface within the building is expressed by its height above or below a reference level on which the surfaces are assumed to be at ambient temperature. The surfaces can then change their temperature. For example, one wall of a room could have an opening that allows heat to leave or come through during different seasons of the year.
Building energy models are categorized as 2D or 3D based on the amount of detail and spatial complexity they provide. While 3D models are required for full analysis and compliance, especially for complex or tall buildings, 2D models are easier to use, quicker to set up and acceptable for preliminary evaluations. In contrast, 3D models offer a more realistic picture of a building’s energy performance. The decision between these models is based on the precise objectives and specifications of the energy analysis for a particular construction project. While 2D BEMs might not directly simulate thermal radiation, they can offer effective approximations for some situations, especially when the impact of radiation is insignificant in comparison to conductive and convective heat transport. However, more sophisticated modeling methods that take radiation into account, such as three-dimensional (3D) BEMs, are advised for more precise and thorough energy simulations, particularly in circumstances where thermal radiation is substantial. By taking the spatial distribution of temperatures and surfaces within a building into account, 3D models may more accurately depict the complexity of radiative heat transfer [7].
1.3. Integrating BIM with BEM
BIM and BEM are becoming obvious choices as technologies to capture, store and render building information in a standardized way become more widespread. Both technologies have many benefits, but they need to be integrated to be fully utilized in energy modeling studies. The modeling of energy consumption is key for the development of energy-efficiency strategies. BIM has great advantages in the areas of design and construction, but it falls short in other aspects. A BIM model is a 3D visual simulation that can be used to show changes to the building configuration and its structure over time as well as thermal zones [7]. However, BIM struggles at times with issues of visualization and data integrity resulting from the integration of different types of data. For example, the completeness of data in a BIM model depends on the number and calibration of its sources and on how well these sources are maintained [8]. A BIM model could be well calibrated, but it cannot accurately reflect the building’s energy utilization if the model does not have valid underlying data.
The integration of BEM with BIM is the opportunity to overcome such issues. A digital map of heat flux can be generated using 3D imaging software that integrates high-quality thermal images obtained from a sensor network along with data from energy bills and maintenance logs [9]. It can then be used as a tool to enable integrated design and operation programs for buildings that take advantage of low carbon emissions, increased efficiency, and improved occupant comfort [10]. The integration of these two technologies could prove to be a powerful asset in the reduction in energy utilization and further development of new high-performance building types. A more complete understanding of how buildings function is possible when parametric design tools are also used to calculate energy utilization [11]. Using BIM for design and BEM for energy simulation provides an improved way of communicating information about buildings to other stakeholders, such as city planners, engineers or even consumers.
BIM–BEM integration can be used for the creation of modules for the United States Green Building Council (USGBC) Energy Star rating system to improve sustainability in building design, construction and operation.
The integration of the two programs provides several advantages. One major advantage is a rapid building database that contains all the key information extracted from software simulation tools and can then be used to create BIM models with minimum human interaction. Integration could also help in the comparison of different designs, which is essential in choosing the best design among various alternatives. Another important benefit of integration is the fact that it allows analysis to be conducted directly on BIM models, which saves time and money, as issues can be identified in early design phases before they become difficult to rectify cost-effectively. A particularly useful tool is the ability to compare building designs and submit an analysis of different options based on both BIM and BEM data.
1.4. BIM, BEM and LEED
The Sustainable Sites Initiative (SITES), a part of USGBC, is an environment-focused program that works with designers and developers to create building performance products that promote environmental stewardship and sustainable construction. The SITES Leadership in Energy and Environmental Design (LEED) rating system is a system for rating residential and commercial buildings according to their green design features. The LEED Green Building Rating System is a third-party rating program that assigns points to projects based on the certifications outlined in their rating system every time a project receives certification from a different agency [12]. LEED certifies projects based on the credits and points they accumulate. To obtain a certification, a project must demonstrate compliance with a set of prerequisites specified by the USGBC. At least 50 points are required to receive certification in any category. LEED provides a comprehensive sustainable design standard that combines four main resources: human health and productivity, water, materials, and energy efficiency. Traditional modeling techniques for buildings often do not provide the level of detail needed to make these ratings meaningful. Most BIM models are not multi-dimensional and cannot be used to model performance at fine resolutions. This creates gaps between the numbers provided by LEED ratings (USGBC 2009) and what happens in the real world. BEM and LEED are associated in such a way that through BEM, sustainable building design is possible, and it helps in the selection of material, determination of the environmental impact and overall evaluation of the project which can meet the LEED compliance. BEM platforms such as Revit can quickly perform life-cycle analysis to estimate the environmental impact, thus enabling the design of such buildings which are as per LEED standards. Although other standards such as BREAM (generally followed by UK building standards) are available, LEED remains a more popular choice primarily because of its simplicity of use, and the threshold is based on percentages.
2. Objectives
Energy conservation is important with the growing population and energy demands. BEM is currently a popular topic among the research community, and extensive studies have been carried out in this area. The primary aim of this review is to address the research gaps in the literature as identified by Bastos Porsani et al. [13]. This paper attempts to identify, critically appraise and synthesize existing literature on the integration of BIM and BEM. In this study, software tools which are used for data exchange between BIM and BEM have been identified, determining which file schema is the popular choice among the research community as well as the developers. The study compares the strengths, weaknesses, and applicability of this software platform. The scope of the study is limited to the benefits and challenges (such as inconsistent conversion of the architectural model to the analytical model and other interoperability issues) of integrating BEM into BIM in the design phase of the project. The motive is to give a final write-up that assesses the operability, strengths, and weaknesses of integrating BEM with BIM in the design phase. The uniqueness of this study lies in addressing the needs/issues related to BIM with BEM integration in the design phase in greater depth instead of covering end-to-end aspects of integration. In this paper, an effort is made to address the effect(s) and issues that can result in the integration of BEM in BIM in the design phase and implementation phases of the project. The paper emphasizes the importance of this integration in improving energy efficiency in buildings, addressing interoperability challenges, and evaluating various software tools and data exchange formats used for BIM/BEM integration. The substantial focus on import/export formats from different software highlights the technical aspects of integrating these models, which is crucial for effective implementation in real-world projects. The paper critically analyzes the current state of BIM and BEM integration, including its benefits, challenges, and the potential for future advancements in the field. Our research aim is to investigate how the integration of BEM within BIM frameworks contributes to enhancing building energy efficiency.
Methodology
A search of the relevant articles was conducted by selecting academic publications in databases and Internet search engines (Google Scholar, Web of Science, Scopus) to obtain an overview of a research field. The keywords used were Integration AND Building Energy Modeling AND Building Information Modeling. To ensure that the information and evidence collected are recent, the study has considered articles published from 2012 to 2023.
Eligibility/Inclusion Criteria:
Must have been published in the 2012–2023 period.
Must be written in English.
Available in main databases i.e., Google Scholar, Web of Science and Scopus.
A study should have declared conflicts of interest.
Since this is a developing field, the study will allow for case reports, case series, and other technical publications. The study will further review the reference list of other publications necessary for the analysis. A final 11 studies were reviewed following the procedure shown in Figure 1.
In this study, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model was employed for data modeling, which is a methodology widely recognized and utilized within the research community for its effectiveness in representing the entities of the application domain and facilitating their mapping to database tables or collections. The rigorous selection process resulted in approximately 3.90% of the initially identified records being incorporated into the final analysis, highlighting the selective nature of the systematic review process and underscoring the critical importance of quality control to ensure the integrity and trustworthiness of research findings. This selection process emphasizes the prioritization of quality over quantity, reflecting a commitment to comprehensive and meticulous investigation. By adhering to criteria for relevance, reliability, and academic rigor, the review process effectively narrows down studies to include only those capable of making a significant contribution to the study’s objectives.
An initial screening from databases and search engines yielded 282 articles from 976, reflecting the necessity of eliminating duplicates and irrelevant records to conserve time and resources. This phase ensures that only studies aligned with the specific research objectives, particularly the integration of BIM and BEM, are considered. The exclusion of 56 papers for “Other Reasons” during the screening process underscores the stringent criteria of the review with methodological issues and a lack of peer-review status among the primary reasons for exclusion. This meticulous approach ensures the inclusion of only high-quality, relevant literature, thereby enhancing the systematic review’s validity and relevance to the research aims. The subsequent revision of article selection further refined the body of literature, emphasizing the importance of eliminating duplicate publications and ensuring direct relevance to the integration of BIM and BEM for improving building energy efficiency. The exclusion of 41 articles due to non-retrieval highlights the routine challenges encountered in accessing full texts and underscores the comprehensive nature of systematic reviews in academic research. See Figure 2.
3. Findings and Discussion of the Results
3.1. Commonly Used Tools and Data Models
In this section, the results from the literature review are presented. A comprehensive list is provided in Table 1. A common theme is prevalent and is discussed as follows. To transfer BIM information to be used for BEM, there exist many file schemas such as hypertext markup language (HTML), extensible hypertext markup language (XHTML), building construction extensible markup language (bcXML), industry foundation classes extensible markup language (IFCXML), industry foundation classes (IFCs), and green building extensible markup language (gbXML). For instance, the two most common file schemas adopted by many authors are IFC and green building XML (gbXML). Another common occurrence is the use of Modelica software for improving the interoperability between BIM and BEM applications [14,15]. The analysis further shows that the most used program for integrating BIM/BEM is Green Building Studio developed by Autodesk [16,17,18]. However, other studies further considered other software platforms for the integration such as software based on service-oriented architecture (SOA) [19], the Space Boundary Tool (SBT-1) developed in conjunction with NASA, Sefaira [17], the common boundary intersection [20], and design-builder software [13].
3.2. Merits and Demerits of IFC and gbXML
Building elements may be represented in proprietary ways using BIM-based systems. To address interoperability concerns between various BIM tools, open BIM is a global approach to the collaborative design, realization, and operation of buildings based on open standards and processes. The fundamental goal of Open BIM data models is to provide efficient data transmission between the many AECOO industry technologies. Open BIM models include IFC and gbXML data.
The IFC model offers a standard language for transferring data across various BIM programs while keeping the significance of different bits of data intact. Using IFC cannot be suitable for some complex structures. According to Tkeshelashvili [23], IFC operates in an exchange format and does not open other or different forms. The software has limitations, and it does not integrate or adapt to all the BIM functionalities. The IFC is not helpful because it develops static objects which cannot be edited. Issues in model preparation, import, re-calibration and parametric model development further prolong the time for energy modeling [24]. This is explained by Daehne et al. [25], who also explains that for a building energy simulation to be properly executed, “the physical model should be technically accurate”, but also that “the thermal simulation should be performed using a validated model based on reference data”. BIM-based BEM has the potential to improve the design and energy efficiency of buildings, and this is an important concept to grasp. There are significant limitations and difficulties with this method, despite the promised advantages. The requirement for automatic data interchange between frequently used software applications in the architectural design process presents a considerable problem. The building design and construction industry has long searched for an efficient and automated approach to transfer data among the numerous software programs that are regularly used in these processes. The industry’s acknowledgement of the significance of effective data interchange in developing more efficient and sustainable building designs may be seen in its demand for enhanced interoperability.
The information exchange procedure in IFC-based BIM–BEM solutions is accelerated in Space Load and HVAC simulation using the Information Delivery Manual aid and Model View Definition (IDM and MVD). According to Prada-Hernández et al. [16], MVD/IDM outlines the requirement for data processing in an energy modeling technique in the same way that BEM tools show the critical information extracted from an IFC file. Realistically, due to the limitations of the International Foundation Class format, the development of HVAC conversions is not yet accomplished. A study by Hijazi et al. [2] posits that the IFC file format does not support complicated or modern energy systems, such as heating systems and combined heat and power systems. Recent studies have shown methods to overcome this limitation; however, the current work is limited to a small-scale building. Furthermore, IFC includes some of the basic feature sets, like the boiler’s water flow architecture, the forms of control available, the warm water pump, and the baseline heating value of the heater. Also, the construction of comprehensive timetables within the IFC model presents notable challenges, emphasizing the necessity for precision in the calculation of heating and cooling loads—a task for which Carrier software is renowned. The exportation of BIM data in GBXML format, ensuring compatibility with Carrier software, facilitates a meticulous assessment of a building’s thermal demands. This process significantly augments the accuracy of energy analyses, thereby bolstering the evaluation of energy conservation strategies. Moreover, Trane Trace 700 emerges as an indispensable instrument in the realm of sustainable building design and energy management. It provides sophisticated simulation functionalities for HVAC systems, thereby advancing the analysis of energy efficiency and supporting the attainment of LEED certification. Such capabilities are quintessential for the enhancement of building performance and the advancement of environmental sustainability.
The gbXML has received some traction with a more focused and ease-of-implementation data model; however, it has an issue with the reliable automated data exchange supporting energy simulation tasks. Using gbXML, several changes will need to be made to Revit throughout the transition process. In contrast to the transitions under IFC, the conversion based on gbXML is highly developed as far as building geometry and related HVAC information is concerned. It is often used in the context of energy analysis and simulation. Processing such data is only possible if substantial changes are made to the gbXML file [26]. All the areas defined in the Revit model, including converting them into thermal zones, are then used to create the heating zones for the BEM. Although this is true, the lack of information such as materials during the transformation process forces the architecture to conventionally include them. According to González et al. [18], gbXML seamlessly transmits the structure’s geometry, including its thermal information, and enhances integration between BIM and BEM.
A study by O’Donnell [22] discusses the semi-automated process for building geometry for the NASA Ames Sustainability Base. ArchiCAD, Geometry Simplification Tool (GST) and EnergyPlus were used for concluding the whole building energy simulation tool. The advantage of the semi-automated approach is the reduced time and cost for modeling the whole building, speedy generation of designs and accurate design. Limitations of current applications of BIM-based BEM are presented in Table 2.
3.3. Autodesk Tools for Building Performance Analysis and Simulation
Architects, engineers, and designers in the AEC sector can use the effective tool Autodesk Insight to make data-driven decisions for the improvement of building performance. Autodesk Insight provides users with a platform designed to analyze energy usage, environmental impact, and occupant comfort. Its interface is designed to be user-friendly, and it comes equipped with powerful features for in-depth analysis. It enables professionals to fine-tune building design and operations, assuring energy efficiency, sustainability, and compliance with regulatory standards. It does this by utilizing real-time data and simulations. Building energy-efficient and environmentally responsible structures with the help of Autodesk Insight will ultimately lead to a more sustainable future for the construction sector [18].
Another software offered by Autodesk is Autodesk Green Building Studio (AGBS), which is a cloud-based and flexible energy assessment software. Although their functionality is similar, AGBS offers a broader range of performance analysis, customizable, cloud-based, and used by engineers and sustainability experts for in-depth evaluations. AGBS enables the analysis of structures, including simulations of the building’s performance to conserve energy. According to Gourlis and Kovacic [27], using Revit as a model for conceptualization will make it possible to analyze the energy and how the building is using it, but it is not accurate. Autodesk software optimizes the evaluation of energy performance results, making the process more efficient and effective. However, it is inconsistent when it comes to integration with other tools. According to Crippa et al. [28], using Revit (from Autodesk) to develop a practical model of the building, including its structures, and researching the energy consumption and utilization via the energy use intensity (EUI) tends to be a feasible integration strategy for the BEM in BIM. There is a need for further research to determine the validity of the results to establish the possibility of modeling the energy trends of a structure and thus determine the potential economic and environmental objectives.
3.4. Input–Output for BEM
Input–output for BEM refers to the data and parameters required to perform energy simulations and analyses within a BEM software or tool. It encompasses information such as building geometry, materials, HVAC systems, occupancy patterns, weather data, and other factors that influence a building’s energy performance. These inputs are crucial for accurate energy assessments and predictions in BEM. The models are then imported to BEM tools to simulate the performance of the buildings in various categories such as daylighting, natural ventilation, and annual energy usage. Due to the different climatic changes and extreme conditions that are occurring on a worldwide basis, interest in the future climate and its effects on our cities and landscapes has grown over the past several decades. The output parameters are energy usage, carbon emissions, resource management, thermal analysis, heating/cooling load, breakdown, solar analysis, daylighting assessment, lighting design, life-cycle cost analysis, ventilation and airflow analysis, water usage, and design alternative comparison [29].
3.5. Embodied Energy
An innovative model has been put out in the framework of BIM to determine a building’s embodied energy. This thorough model accounts for every building component over the course of the structure’s lifetime. This model’s main objective is to act as a useful tool for contrasting various designs or systems inside a project while taking the project’s overall performance into account. This method necessitates a thorough analysis of material usage and expected construction lifespan, especially when materials are meant for recycling or reuse. For instance, when considering bricks, which are a common choice for construction, it should be acknowledged that the brick-firing process is energy-intensive, and conventional brick manufacturing methods often lack environmental sustainability. The allocated embodied energy for each component is measured in kilowatt-hours (kWh) and represents the total primary energy utilized throughout the component’s life cycle, encompassing both direct and indirect processes from production to disposal within the cradle-to-grave life-cycle boundaries. The term ‘lifetime’ is used here to isolate a system from any other system it might interact with, enabling comparisons of different systems or buildings within a single model.
The calculation of embodied carbon in this model is carried out independently of any other modeling of building or BIM models [30]. In this way, it is possible to compare different components within one model and reduce the effect of the uncertainties involved in assessing a material’s lifetime. The suggested embodied energy model by Capper et al. [30] differs from previously published embodied energy models in that it first calculates the primary energy used during the life of each product and then calculates the amount of carbon produced as a result of that use.
4. Challenges
4.1. Imperfect and Inconsistent Conversion of Architectural Model to Analytical Model
Achieving greater analytical control of building designs is the primary motivating factor for the technology. The design team is constantly looking for means of automating different phases and simulating the outcomes [16]. However, as one of the emerging and promising technology areas, issues arise when converting an architectural model to an analytical model. The research conducted by Gourlis and Kovacic [27] established that a joint knowledge database is needed to achieve the life-cycle management of buildings. Multiple design team members and analysts are involved when pursuing the project’s outcomes. Consolidating the feedback of multiple individuals is a challenging but needed task keeping in mind that concerns of biasedness should be addressed. Errors in converting architectural models to analytical models arise on different levels, including assessing cost, thermal properties, and overall structural analysis [27,31].
Integrating BIM into BEM is a promising way of automating the design process of a building and engineering project. However, further inaccuracy concerns may arise in case a team which is focusing on a particular sub-domain of the project has no clear picture of the overall functionality of the project, lacks adequate information and lacks information regarding updated standards as BIM/BEM are continually evolving fields with ongoing developments in standards and practices. For instance, Gourlis and Kovacic [27] reflected on the multiple layers of interacting complex systems. Each layer has unique standards and requirements. The vital phases include building, services, machine floor layout, geometric data, and energy data, to name a few. Converting the architectural model to an analytical model can be inaccurate if the design team focuses on one layer over the other or over-invests in a single phase.
The sources of the inaccuracies can be internal or external [32,33]. The internal concerns are tied to the limited cooperation and technical competencies of the design team. On the other hand, the external concerns revolve around the complexities of integrating BIM into BEM. Some of the common concerns leading to inaccuracy include collisions, where elements in BIM clash with BEM requirements, are a common concern. Rapid changes in building products can lead to discrepancies if not quickly adapted. Keeping pace with evolving energy regulations is crucial, as outdated models may not reflect compliance. Additionally, complex interactions between building systems, such as HVAC and lighting, require precise integration to ensure accurate energy performance predictions. Addressing these concerns is essential for effective BIM-to-BEM integration [27]. However, the designer is often aware of the complexities and consequent implications of converting an architectural model to an analytical model. The automation incentives motivate designers to continue pushing for the integration of BIM into BEM.
Some solutions exist for solving the issues of inaccuracy and uncertainty. According to Gourlis and Kovacic [27], there is a growing trend toward incorporating automated and semi-automated processes into the conventional BIM-to-BEM framework. This adaptation aims to achieve seamless data transfer while prioritizing data accuracy. In alignment with this objective, the research conducted by Khodeir and Nessim [34] emphasizes the importance of error-free data exchange to ensure that the resulting information is consistently reliable. An important solution-constrained BIM to BEM integration also exists to address human error. Inconsistent translation of the architectural model to the analytical model is addressed by adopting BIM–BEM software communication without human intervention [27]. The approach is only effective when the design team is accustomed to custom software plug-ins and programming skills for every phase and layer of BIM models. Planners and architects must also operate collaboratively to arrive at the desired outcomes, i.e., energy-efficient building design.
The integration of BEM with BIM shows inconsistency issues, especially when calibrating plans, sections, and elevations for skyscrapers and major engineering projects. Creating digital maps using 3D imaging is one of the motivating factors for the efforts and successes of using BEM solutions in BIM software. However, project managers and planners have issues regarding analytical capacity, especially when processing architectural models. The problem is notable during data transfer from a BIM to a thermal analysis application. According to Gao et al. [35], interoperability issues arise, especially in the early design stage. Engineers might have adequate knowledge of their expectations but cannot predict or control vital outcomes of the buildings. Some of the common indicators of accurate conversion of an architectural model to an analytical model include predicting the end cost, sustaining and controlling energy efficiency, and defining the general performance of a system.
Additionally, Gao et al. [35] examine the issue of inconsistencies between immediate and long-term design decisions. Engineers and project managers tend to make about 20% of the design decisions during the initiation stage. Such decisions define the outcomes of the other 80% of all design decisions. The design team relies on the automated system to evaluate the environmental impacts of specific actions, targets, and decisions [11]. However, such performance outcomes assume seamless access and availability of essential materials and competencies [13].
4.2. Long Analytical Model Simulation Times and Other Time Constraints
The challenges of integrating BIM into BEM can also result from time-consuming simulations. Simulation times can be higher or inconsistent for both energy and day-lighting needs. Designers might not have adequate time to evaluate and choose from a host of design options [36]. Designers are usually aware of the forms of delays and the causes of the lags. Practice BEM models based on BIM data export are the technical prerequisites, such as a geometric transition of inputs to outputs. On the contrary, as established by Gao et al. [35], personal understanding and expertise are needed by every design team member. Therefore, it is difficult to consolidate the views of all the contributors and test the options offering the best outcomes for the organization. The process can be time-consuming and fails to provide a lasting mechanism for integrating BIM into BEM. To achieve accurate projections and predictions, particularly in terms of energy usage, the BIM model should be continually reviewed and enhanced. The model’s dependability and applicability for many areas of building performance analysis, including energy-efficiency assessments, are maintained by continual evaluation and improvement. As such, the designers would have meaningful projections and standard experimental templates for different project needs and outcomes.
4.3. Poor Coordination of Analytical Model Outputs/Inputs
Integrating BIM to BEM can be problematic due to the constrained coordination of analytic model inputs and outputs. Most of the authors have revealed that the design team works toward integrating BIM with BEM and is aware of the potential advantages. However, there might be limited expertise or skills available within the design team for performing the types of detailed analysis and predictive modeling that are necessary for integrating BIM with BEM [37]. The design team has limited capacity to influence long-term outcomes. The challenge is primarily triggered by misalignment between project inputs and outputs. According to Gourlis and Kovacic [27], interoperability and data transfer must be sustained throughout the different phases of the project. Shifting BIM to BEM systems also requires re-modeling the core strategies constantly. While it may be possible to alter the inputs, the outputs might be irregular despite the automation process. Some solutions exist in the form of the easy creation of building energy models. The design can re-evaluate the possible outcome of the project. There are progressive assessments of the accurate and reliable means of transferring information between tools. The key target of the design team is to access different BIM models and choose the ones that offer the best or most convenient results to the organization [6]. Conflicting output results can also arise from lacking the same set of thermal processes for different layers and phases of the BIM model. Effective integration might be achieved by re-assessing outputs and inputs and choosing the optimal combination.
The challenges while integrating BIM and BEM are due to their different functions, different architecture, different design methods and different approaches. The most demanding task for the integration was to find a way to link the input parameters of BEM with data used in BIM [38]. The main requirements were (a) the interoperability with the existing system for building performance analysis, (b) a clear definition of the input parameters of BEM, and (c) a clear definition of output data in BIM.
4.4. Performances of Building in Future Climate Scenarios
The ability of BEM to accurately predict future energy demands will depend largely on a host of other factors. For instance, the role of climate change is an issue of concern among scholars. In a study by Al Qadi et al. [39], it is proposed that buildings need to be designed in such a way to deal with future erratic climatic regimes, including adaptation measures. Equally, to predict the energy utilization in buildings, the impact of architectural design and climate-related issues must be taken into account. A significant aspect affecting buildings is the heat island effect, which is caused by black asphalt paved roads, buildings, and all other objects in direct contact with concrete. These effects are caused by the absorption and retention of heat by building materials as well as the heat generated by energy use, HVAC systems, and urban development, leading to elevated temperatures within the building and its immediate surroundings. Newer open construction strategies can help improve the structure of buildings; however, their performance is affected by climate variables. In addition, several other factors affect buildings such as changes in consumer electronics, including increased appliance usage, improved living standards, regulatory compliance, renewable energy integration, home automation integration, and carbon footprint reduction strategies. The integration of BEM with modern machine learning and artificial tools will help the precise prediction of future consumptions of energy and other aforementioned effects. Machine learning approaches can be integrated to approximate the input–output relationship exhibited by complex BEMs and develop appropriate solutions. The availability of large datasets is important for training the BEM data so that predictions regarding energy consumption can be improved.
4.5. The Benefits of Interoperability of BIM and BEM Technologies
The synthesis of BIM and BEM emerges as a cornerstone in the evolution of architectural and construction methodologies, markedly elevating the precision of energy-efficiency evaluations, design fidelity, and cost veracity. Choi and Lee [40] highlight the integration’s capacity to capitalize on alternative energy sources while refining the accuracy of cost and procedural forecasts. Furthermore, Battaglia et al. [41] delineate the imperative of BIM and BEM synergy for executing energy analyses in buildings of historical importance, accentuating its significance in preservation initiatives. Liu et al. [42] indicated that BIM technology is widely utilized to lower energy use in buildings, yet it faces challenges and requires enhancements for its deployment in construction refurbishment endeavors. Rahim et al. [43] elucidate the adoption of BIM in Malaysian sustainable housing endeavors as aligning with the National Construction Policy 2030’s objectives by augmenting cost efficiency and mitigating environmental detriments through sustainable construction practices. This corpus of research elucidates the revolutionary impact of BIM and BEM collaboration in transcending conventional limits of energy efficiency, sustainability, and conservation within the construction industry.
Kiavarz et al. [44] propose a novel BIM methodology for predicting building energy consumption (BECE) through a framework that employs geo-computation algorithms for the meticulous extraction of data from the Industry Foundation Classes (IFC) schema. Utilizing a graph-based technique, this framework simplifies the complexity of IFC models, facilitating precise geometry extraction and room adjacency discernment, thereby proving its efficacy in spatial BECE analysis. Delgado et al. [45] introduce a strategy that merges drone-derived photogrammetry with BIM to propel Building 4.0, integrating virtual reality and digital twins. Implemented in the BlueWoodenHouse Project, this approach yields insights into the energy-efficiency optimization of building materials and conditions for a modular wooden house in Portugal with empirical data validating the numerical model and emphasizing drones’ utility in fostering energy-efficient construction methodologies. Chen and Hu [46] examine the synergy between BIM and its Insight tool in facilitating energy analysis with an emphasis on devising energy-saving solutions that are economically viable. Through the lens of a diminutive wind farm project, their investigation assesses the capacity of BIM coupled with the Insight tool to propose design variations oriented toward enhancing energy efficiency and diminishing emissions, concurrently evaluating their financial feasibility for proprietors. Yang et al. [47] examine the interoperability challenges between BIM and BEM as a hindrance to automated and efficient design processes crucial for achieving net-zero carbon buildings by 2050. They recommend a workflow that records simplified geometry in gbXML format to rectify the inaccuracies and extraneous details prevalent in BIM models, thus facilitating enhanced energy simulations through a streamlined geometry exchange between BIM and BEM, as evidenced by improved EnergyPlus simulation outcomes with revised gbXML files.
The confluence of BEM with BIM frameworks signifies a pivotal development in enhancing the energy efficiency of building design and operations. This scrutiny leverages empirical evidence and case study analyses to articulate the explicit improvements in energy performance metrics engendered by the combined application of BEM and BIM. Through an analytical selection of case studies, the research delineates the quantitative enhancements associated with this integration, emphasizing critical energy performance indices such as energy consumption, operational efficiency, and reductions in carbon emissions. These articles highlight the integral contribution of BEM–BIM integration to elevating building energy performance. By facilitating a sophisticated, data-driven approach to design and construction, integration promotes the creation of structures that are not only more energy efficient but also more congruent with sustainability objectives. The empirical data presented underscore the integration’s potential to engender substantial energy savings and environmental advantages, reinforcing the importance of BEM and BIM integration in the pursuit of environmentally responsible buildings. Building design resilience is increased by taking into consideration shifting weather patterns and environmental circumstances when future climate data are incorporated into BIM/BEM technologies. With this proactive approach, architects and engineers may optimize climate-adaptable designs that are energy efficient, resulting in sustainable and long-lasting structures. Such information is incorporated to promote environmentally responsible decision making and the development of structures that are better adapted to meet future problems.
Moreover, the integration significantly augments the precision of energy models by utilizing detailed, geometrically accurate data from BIM concerning building orientation, materials, and systems. This enhanced fidelity information affords more accurate simulations of thermal behavior, daylighting, and energy consumption under varied conditions. Through precisely forecasting energy flows and interactions within and surrounding the building, stakeholders are empowered to make well-informed choices regarding materials, systems, and designs that optimize energy utilization. BEM–BIM integration also engenders significant cost savings throughout the building life cycle. By enhancing energy performance from the design phase, it obviates the need for costly retrofits and ensures more efficient building operations, thereby reducing energy expenditures and maintenance costs. The proactive identification of the most economically viable solutions for energy efficiency can culminate in considerable reductions in both initial capital outlays and operational expenses. Furthermore, BEM–BIM integration underpins life-cycle energy management through the establishment of a dynamic building model that can be updated and employed throughout its operational lifespan. This evolving model facilitates the ongoing monitoring and optimization of building performance, pinpointing opportunities for energy conservation and retrofitting endeavors that can further diminish energy consumption and carbon emissions over time.
In conclusion, integrated BEM–BIM processes yield buildings that are more energy-efficient, offering improved thermal comfort and indoor air quality. This not only curtails energy usage but also elevates occupant comfort and well-being, contributing to healthier, more productive indoor spaces. By enabling comprehensive assessments of energy usage and environmental impacts across a building’s entire life cycle, BEM–BIM integration facilitates informed decision making that prioritizes sustainability and efficiency over the long term.
5. Conclusions
This paper meticulously evaluates the convergence of BEM and BIM, highlighting both the progress made and the challenges that remain in integrating these two pivotal technologies. It identifies a significant trend toward the utilization of BEM within BIM frameworks to enhance energy efficiency in building designs yet underscores critical hurdles related to data compatibility and interoperability. The review illuminates the untapped potential of BIM–BEM integration in complex architectural endeavors and advocates for the advancement of more sophisticated, AI-powered tools to address the complexities of modern construction. It calls for further research into these integrations, especially within the realm of sustainable construction, to not only advance technological capabilities but also to influence policies and industry standards toward more sustainable and efficient building practices.
The paper further elucidates the substantial advancements and challenges in marrying BEM with BIM with a particular focus on the effectiveness and limitations of current data exchange tools such as IFC and gbXML. Despite their widespread adoption, these tools face limitations in complex engineering scenarios, necessitating bespoke adaptations. The imperative integration of BEM and BIM is depicted as fundamental for a comprehensive understanding of building energy dynamics, which is crucial for crafting buildings that are both energy efficient and environmentally sustainable. The conclusion emphasizes the necessity for future studies to refine BIM–BEM integration, especially through the adoption of BIM Collaboration Tools that enhance communication and coordination among design team members, reducing errors and ensuring a more integrated approach to design.
It acknowledges that transitioning directly from BIM to BEM poses significant challenges, as the intricacies of a BIM model often surpass the requirements for a streamlined BEM model. While IFC and gbXML are commonly used standards, they come with their own sets of limitations, highlighting the need for more adaptable solutions. The discussion points toward the underexplored potential of BIM adoption, hindered by industry secrecy and the short life cycle of construction projects, which necessitates collaboration among diverse teams reliant on specialized software.
In advocating for the integration of BEM with BIM, the paper underscores the importance of adopting best environmental practices in construction and the use of buildings, emphasizing the role of energy simulation models in understanding and optimizing building energy usage. Through the exploration of various integration techniques, including linking BIM models with EnergyPlus and creating interfaces for data exchange, the paper showcases the concerted efforts made to merge these disciplines, serving overlapping requirements in the design phase. The paper concludes by projecting the future of this field as the integration of BIM with BEM, which is a promising approach for enhancing construction and operational efficiencies.
Conceptualization, M.A., M.E. and R.V.; methodology, M.A., M.E. and R.V.; software, M.A., M.E. and R.V.; validation, M.A., M.E. and R.V.; formal analysis, M.A., M.E. and R.V.; investigation, M.A., M.E. and R.V.; resources, M.A., M.E. and R.V.; data curation, M.A., M.E. and R.V.; writing—original draft preparation, M.A., M.E. and R.V.; writing—review and editing, M.A., M.E. and R.V.; visualization, M.A., M.E. and R.V.; supervision, M.E. and R.V. All authors have read and agreed to the published version of the manuscript.
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy.
The first author wishes to express his gratitude to the Saudi Arabian Ministry of Education for generously funding his research and a PhD study at the University of Exeter.
The authors declare no conflicts of interest.
| BIM | Building Information Modeling |
| BEM | Building Energy Modeling |
| CAD | Computer-Aided Design |
| EBAS | Energy-Efficient Building Automation Systems |
| ICT | Information and Communication Technologies |
| AEC | Architectural Engineering and Construction |
| CAE | Computer-Aided Engineering |
| USGBC | United States Green Building Council |
| LEED | Leadership in Energy and Environmental Design |
| SITES | Sustainable Sites Initiative |
| SOA | Service-Oriented Architecture |
| SBT | Space Boundary Tool |
| IFC | Industry Foundation Classes |
| gbXML | Green Building XML |
| HVAC | Heating, Ventilation, and Air Conditioning |
| BREAM | Building Research Establishment Environmental Assessment Method |
| EUI | Energy Use Intensity |
| IDM | Information Delivery Manual |
| MVD | Model View Definition |
| GBS | Green Building Studio |
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Data modeling schemes used in the literature.
| Source | Software | Data Model/File Schemas | Purpose and Findings |
|---|---|---|---|
| [ | Solibri Model Checker/Autodesk Revit | IFC |
|
| [ | Autodesk® Green Building Studio (GBS) | gbXML/IFC |
|
| [ | Autodesk Revit | gbXML/IFC |
|
| [ | Green Building Studio (GBS) and Sefaira | gbXML/IFC |
|
| [ | Design Builder | gbXML/IFC |
|
| [ | Service-oriented architecture (SOA) | An opensource format called IDF |
|
| [ | Autodesk® Green Building Studio (GBS) | INP and gbXML |
|
| [ | Common Boundary Intersection Projection | RNSYS input file. IFC |
|
| [ | Modelica (Revit2Modelica) | gbXML |
|
| [ | OpenModelica | IFC |
|
| [ | Space Boundary Tool (SBT-1) | IFC |
|
Limitations of current applications of BIM-based BEM.
| Level | IFC | gbXML |
|---|---|---|
| Geometry |
|
|
| Material |
|
|
| Space Type |
|
|
| Thermal Zone |
|
|
| Space Load and HVAC |
|
|
References
1. Gobesz, F.Z. The roots of BIM. Sciendo MTK; 2020; 12, pp. 42-49. [DOI: https://dx.doi.org/10.33894/mtk-2020.12.06]
2. Hijazi, M.; Kensek, K.; Konis, K. Bridging the gap: Supporting data transparency from BIM to BEM. Proceedings of the ARCC 2015 Conference: Future of Architectural Research; Chicago, IL, USA, 6–9 April 2015; pp. 149-156.
3. Khodeir, L. Green BIM in heritage building: Integrating building energy models (BEM) with building information modelling (BIM) for sustainable retrofit of heritage buildings. Heritage Building Information Modelling; 1st ed. Arayici, Y.; Counsell, J.; Mahdjoubi, L.; Nagy, G.; Hawas, S.; Dweidar, K. Routledge: London, UK, 2017; pp. 203-217. [DOI: https://dx.doi.org/10.4324/9781315628011]
4. Al Sehrawy, A.; Amoudi, O.; Tong, M.; Callaghan, N. A review of the challenges to integrating BIM and building sustainability assessment. Proceedings of the Central European Symposium on Thermophysics; Kazimierz Dolny, Poland, 1–3 September 2021; [DOI: https://dx.doi.org/10.1063/5.0071055]
5. Alsharif, R. (Swinburne University of Technology) A review on the challenges of BIM-based BEM automated application in AEC industry. Unpublished Work 2019; pp. 1-40. [DOI: https://dx.doi.org/10.13140/RG.2.2.26102.55366]
6. Ang, Y.Q.; Berzolla, Z.M.; Reinhart, C.F. From concept to application: A review of use cases in urban building energy modelling. Appl. Energy; 2020; 279, 115738. [DOI: https://dx.doi.org/10.1016/j.apenergy.2020.115738]
7. Bonomolo, M.; Di Lisi, S.; Leone, G. Building information modelling and energy simulation for architecture design. Appl. Sci.; 2021; 11, 2252. [DOI: https://dx.doi.org/10.3390/app11052252]
8. Abruzzini, A.; Abrishami, S. Integration of BIM and advanced digital technologies to the end of life decision-making process: A paradigm of future opportunities. J. Eng. Des. Technol.; 2022; 20, pp. 388-413. Available online: https://researchportal.port.ac.uk/files/28421928/Integration_of_BIM_and_advanced_digital_technologies.pdf (accessed on 12 March 2022). [DOI: https://dx.doi.org/10.1108/JEDT-12-2020-0524]
9. Ling, M.X.; Li, Y.; Ding, X.; Teng, F.Z.; Yang, X.Y.; Fan, W.M.; Xu, Y.G.; Sun, W. Destruction of the North China Craton induced by ridge subductions. J. Geol.; 2013; 121, pp. 197-213. Available online: https://www.academia.edu/download/45079198/Destruction_of_the_North_China_Craton_In20160425-1493-1viz80y.pdf (accessed on 15 March 2022). [DOI: https://dx.doi.org/10.1086/669248]
10. Choi, J.W.; Park, C.S. Issues and limitations in BIM to BEM for energy simulation. J. Arch. Inst. Korea Plan. Des.; 2016; 32, pp. 223-230. [DOI: https://dx.doi.org/10.5659/JAIK_PD.2016.32.2.223]
11. Gerrish, T.; Ruikar, K.; Cook, M.; Johnson, M.; Phillip, M. Using BIM capabilities to improve existing building energy modelling practices. Eng. Construct. Architect. Manag.; 2017; 24, pp. 190-208. [DOI: https://dx.doi.org/10.1108/ECAM-11-2015-0181]
12. Hu, M. Optimal renovation strategies for education buildings—A novel BIM–BPM–BEM framework. Sustainability; 2018; 10, 3287. [DOI: https://dx.doi.org/10.3390/su10093287]
13. Bastos Porsani, G.; de Lersundi, K.D.V.; Sánchez-Ostiz Gutiérrez, A.; Fernández Bandera, C. Interoperability between building information modelling (BIM) and building energy model (BEM). Appl. Sci.; 2021; 11, 2167. [DOI: https://dx.doi.org/10.3390/app11052167]
14. Jeong, W.; Kim, J.B.; Clayton, M.J.; Haberl, J.S.; Yan, W. Translating building information modelling to building energy modelling using model view definition. Sci. World J.; 2014; 2014, 638276. [DOI: https://dx.doi.org/10.1155/2014/638276]
15. Remmen, P.; Cao, J.; Ebertshäuser, S.; Frisch, J.; Lauster, M.; Maile, T.; O’Donnell, J.; Pinheiro, S.; Rädler, J.; Streblow, R. et al. An open framework for integrated BIM-based building performance simulation using modelica. Proceedings of the 14th IBPSA Conference; Hyderabad, India, 7–9 December 2015; pp. 379-386.
16. Prada-Hernández, A.V.; Rojas-Quintero, J.S.; Vallejo-Borda, J.A.; Ponz-Tienda, J.L. Interoperability of building energy modelling (BEM) with building information modelling (BIM). Proceedings of the SIBRAGEC ELAGEC; Sao Carlos, Brazil, 7–9 October 2015; pp. 519-526.
17. Farid Mohajer, M.; Aksamija, A. Integration of building energy modelling (BEM) and building information modelling (BIM): Workflows and case study. Proceedings of the Building Technology Educator’s Society Conference; Amherst, MA, USA, 17–22 June 2019; [DOI: https://dx.doi.org/10.7275/bn9j-e183]
18. González, J.; Soares, C.A.P.; Najjar, M.; Haddad, A.N. BIM and BEM methodologies integration in energy-efficient buildings using experimental design. Buildings; 2021; 11, 491. [DOI: https://dx.doi.org/10.3390/buildings11100491]
19. Zhao, H.; Nagy, Z.; Thomas, D.; Schlueter, A. Service-oriented architecture for data exchange between a building information model and a building energy model. Proceedings of the CISBAT International Conference; Lausanne, Switzerland, 9–11 September 2015; pp. 761-766.
20. Giannakis, G.I.; Lilis, G.N.; Garcia, M.A.; Kontes, G.D.; Valmaseda, C.; Rovas, D.V. A methodology to automatically generate geometry inputs for energy performance simulation from IFC BIM models. Proceedings of the 14th International Conference of IBPSA-Building Simulation 2015; Hyderabad, India, 7–9 December 2015; pp. 504-511. [DOI: https://dx.doi.org/10.26868/25222708.2015.2363]
21. Heffernan, E.E.; Sohel, M.I.; Beazley, S.F.; McCarthy, T.J. From BIM (Building Information Modelling) to BEM (Building Energy Modelling): A collaborative approach. Proceedings of the Australasian Building Simulation 2017 Conference; Wollongong, Australia, 15–16 November 2017; pp. 1-11.
22. O’Donnell, J.T.; Maile, T.; Rose, C.; Mrazovic, N.; Morrissey, E.; Regnier, C.; Parrish, K.; Bzjanac, V. Transforming BIM to BEM: Generation of building geometry for the NASA Ames sustainability base BIM. Lawrence Berkeley National Laboratory Technical Report; LBNL: Berkeley, CA, USA, 2013; pp. 1-26. [DOI: https://dx.doi.org/10.2172/1168736]
23. Tkeshelashvili, M. BIM to BEM: Development of Integrated Workflow from Archicad to Energy Evaluation. Master’s Thesis; Lund University: Lund, Sweden, 2021.
24. Stahl, B.C.; Andreou, A.; Brey, P.; Hatzakis, T.; Kirichenko, A.; Macnish, K.; Shaelou, S.L.; Patel, A.; Ryan, M.; Wright, D. Artificial intelligence for human flourishing–Beyond principles for machine learning. J. Bus. Res.; 2021; 124, pp. 374-388. Available online: https://www.sciencedirect.com/science/article/pii/S0148296320307839 (accessed on 7 January 2024). [DOI: https://dx.doi.org/10.1016/j.jbusres.2020.11.030]
25. Daehne, D.; Fürle, C.; Thomsen, A.; Watermann, B.; Feibicke, M. Antifouling biocides in German marinas: Exposure assessment and calculation of national consumption and emission. Integr. Environ. Assess. Manag.; 2015; 13, pp. 892-905. Available online: https://setac.onlinelibrary.wiley.com/doi/pdf/10.1002/ieam.1896 (accessed on 7 January 2024). [DOI: https://dx.doi.org/10.1002/ieam.1896] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28127937]
26. Lee, S.Y.; Park, C.S.; Kim, I.H. Issues and prospects of BIM-to-BEM (Building Energy Model). J. Arch. Inst. Korea Plan. Des.; 2014; 30, pp. 199-206. [DOI: https://dx.doi.org/10.5659/JAIK_PD.2014.30.4.199]
27. Gourlis, G.; Kovacic, I. Building information modelling for analysis of energy-efficient industrial buildings—A case study. Renew. Sustain. Energy Rev.; 2017; 68, pp. 953-963. [DOI: https://dx.doi.org/10.1016/j.rser.2016.02.009]
28. Crippa, J.; Araujo, A.M.; Bem, D.; Ugaya, C.M.; Scheer, S. A systematic review of BIM usage for life cycle impact assessment. Built Environ. Proj. Asset Manag.; 2020; 10, pp. 603-618. [DOI: https://dx.doi.org/10.1108/BEPAM-03-2019-0028]
29. Reeves, T.; Olbina, S.; Issa, R.R.A. Guidelines for using building information modelling for energy analysis of buildings. Buildings; 2015; 5, pp. 1361-1388. [DOI: https://dx.doi.org/10.3390/buildings5041361]
30. Capper, G.; Matthews, J.; Lockley, S. Incorporating embodied energy in the BIM process. Proceedings of the CIBSE ASHRAE Technical Symposium, Imperial College; London, UK, 18–19 April 2012; pp. 1-11.
31. Ramaji, I.J.; Messner, J.I.; Mostavi, E. IFC-based BIM-to-BEM model transformation. J. Comput. Civ. Eng.; 2020; 34, 04020005. [DOI: https://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000880]
32. Jeong, W.-S.; Kim, K.H. A performance evaluation of the BIM-based object-oriented physical modelling technique for building thermal simulations: A comparative case study. Sustainability; 2016; 8, 648. [DOI: https://dx.doi.org/10.3390/su8070648]
33. Jeong, W.-S.; Kim, J.B.; Clayton, M.J.; Haberl, J.S.; Yan, W. A framework to integrate object-oriented physical modelling with building information modelling for building thermal simulation. J. Build. Perform. Simul.; 2016; 9, pp. 50-69. [DOI: https://dx.doi.org/10.1080/19401493.2014.993709]
34. Khodeir, L.M.; Nessim, A.A. BIM2BEM integrated approach: Examining status of the adoption of building information modelling and building energy models in Egyptian architectural firms. Ain Shams Eng. J.; 2018; 9, pp. 1781-1790. [DOI: https://dx.doi.org/10.1016/j.asej.2017.01.004]
35. Gao, H.; Koch, C.; Wu, Y. Building information modelling-based building energy modelling: A review. Appl. Energy; 2019; 238, pp. 320-343. [DOI: https://dx.doi.org/10.1016/j.apenergy.2019.01.032]
36. Jia, M.; Srinivasan, R.S.; Ries, R.; Bharathy, G. A framework of occupant behavior modelling and data sensing for improving building energy simulation. Proceedings of the Symposium on Simulation for Architecture and Urban Design; Delft, The Netherlands, 5–7 June 2018; pp. 1-8. [DOI: https://dx.doi.org/10.22360/simaud.2018.simaud.015]
37. Chaudhary, G.; New, J.; Sanyal, J.; Im, P.; O’Neill, Z.; Garg, V. Evaluation of “Autotune” calibration against manual calibration of building energy models. Appl. Energy; 2016; 182, pp. 115-134. [DOI: https://dx.doi.org/10.1016/j.apenergy.2016.08.073]
38. Spiridigliozzi, G.; Pompei, L.; Cornaro, C.; De Santoli, L.; Bisegna, F. BIM-BEM support tools for early stages of zero-energy building design. Conf. Ser. Mater. Sci. Eng.; 2019; 609, 072075. [DOI: https://dx.doi.org/10.1088/1757-899X/609/7/072075]
39. Al Qadi, S.B.; Elnokaly, A.; Sodagar, B. Predicting the energy performance of buildings under present and future climate scenarios: Lessons learnt. Proceedings of the First International Conference on Climate Change (ICCCP); Albireh, Palestine, 5–8 May 2017; pp. 119-130.
40. Choi, J.; Lee, S. A Suggestion of the Alternatives Evaluation Method through IFC-Based Building Energy Performance Analysis. Sustainability; 2023; 15, 1797. [DOI: https://dx.doi.org/10.3390/su15031797]
41. Battaglia, V.; Carotenuto, A.; Mauro, A.; Normino, G. On the interoperability of Building Information Modeling for Energy Analysis: The case study of the Maritime Station of Napoli (Italy). IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2022; Volume 1106, No. 1 012001.
42. Liu, X.; Ding, Z.; Li, X.; Xue, Z. Research Progress, Hotspots, and Trends of Using BIM to Reduce Building Energy Consumption: Visual Analysis Based on WOS Database. Int. J. Environ. Res. Public Health; 2023; 20, 3083. [DOI: https://dx.doi.org/10.3390/ijerph20043083] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36833778]
43. Rahim, N.S.A.; Ismail, S.; Subramaniam, C.; Abdullah Habib, S.N.H.; Durdyev, S. Building Information Modelling Strategies in Sustainable Housing Construction Projects in Malaysia. Sustainability; 2023; 15, 2313. [DOI: https://dx.doi.org/10.3390/su15032313]
44. Kiavarz, H.; Jadidi, M.; Rajabifard, A.; Sohn, G. An Automated Space-Based Graph Generation Framework for Building Energy Consumption Estimation. Buildings; 2023; 13, 350. [DOI: https://dx.doi.org/10.3390/buildings13020350]
45. Delgado, J.M.; Guimarães, A.S.; Poças Martins, J.; Parracho, D.F.; Freitas, S.S.; Lima, A.G.; Rodrigues, L. BIM and BEM Interoperability–Evaluation of a Case Study in Modular Wooden Housing. Energies; 2023; 16, 1579. [DOI: https://dx.doi.org/10.3390/en16041579]
46. Chen, X.; Qi, W.; Hu, W. Cost-Effective Analysis and Calculation of Energy Performance Using Insight Tool in BIM. Proceedings of the 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI); Changchun, China, 27–29 May 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 889-891.
47. Yang, Y.; Pan, Y.; Zeng, F.; Lin, Z.; Li, C. A gbXML reconstruction workflow and tool development to improve the geometric interoperability between BIM and BEM. Buildings; 2022; 12, 221. [DOI: https://dx.doi.org/10.3390/buildings12020221]
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Eames, Matt; Vinai, Raffaele