Architectural Models as a Base for Reliable Early Stage Energy Simulation

Buildings account for nearly 40% of the global process and industrial greenhouse gas emissions, and within the building sector. And within the building sector, nearly 72% of the CO2 emissions are due operational carbon demand in the building sector. To meet the goals set out in the Paris Accord, we need to halve carbon emissions by the end of this decade: an aggressive and ambitious timeline. It has become an immediate and urgent need to design and construct buildings that are minimally intrusive in their impact on the environment and operate efficiently. Improvement of the building's energy performance must begin at the early design stage as the potential for sustainable intervention in the project's early phases is higher. The opportunity to improve building performance as the design progresses, is constantly reducing, while the cost of optimization is constantly increasing. One of the major design variables impacting the performance at the early phase is the building morphology/form and its associated variables. Sensible optioneering of the building form and its variables at the early stages of design - especially the conceptual design stage - can help improve the performance of the design early on in the design process at minimum cost. However, for effective early-phase design optimisation, there is a need to develop tools/methods that allow instantaneous evaluation of a large design sample space without much “lag” between the design and performance evaluation workflows. Also, in regular early phase sustainable design workflows, multiple stakeholders - like the designer, the performance analyst, etc. - are involved, which leads to a cognitive divide in the design process. For instance, while the architect designs the initial conceptual massing with a functional design intuition, the performance analysts suggest design interventions with a performance oriented design intuition. This cognitive divide leads to a dipartite design cognition in early phase design. The underlying premise of this research is to support the notion of blurring the dichotomy between conflicting design intuitions through exploring Human + Artificial Intelligence (AI) synergies and their underlying foundational technological requirements with respect to early phase design, to enable intuitive high performance design scenarios with a centralized design cognition where there are no other stakeholders other than the designer and the design environment. With the paradigm shift of other industries towards Machine Learning (ML) and AI, recent research advances in ML and the increasing availability of Big-data in the AEC industry have bolstered multivariate problems like building performance evaluation to a great extent. Design optioneering with instantaneous performance feedback through a ML method called Surrogate Modelling is an upcoming and promising methodology, which delivers feedback based on knowledge through available data, rather than simulation. Also, paradigms in Concurrent Human Machine Interaction (HMI) have explored avenues of augmenting the architectural design process through data-driven approaches. The type of data (input-output pairs) forming the basis for these paradigms depend on the target problem at hand. For early phase performance optimization - especially energy use optimization - Building Energy Model (BEM) forms the primary data. The US Department of Energy defines BEM as a versatile, multipurpose tool that is used in new building and retrofit design, code compliance, green certification, qualification for tax credits and utility incentives, and even real-time building control. BEM is also used in large-scale analyses to develop building energy-efficiency codes and inform policy decisions. A comprehensive BEM dataset that is large and accessible can support the growing interest in ML and HMI research in the high performance building design field. However, common techniques to acquire large BEM datasets like manual 3D energy modeling and simulation, 3D scanning, etc. are very tedious and time consuming. Also, existing datasets are usually incomplete, inconsistent and very difficult to access, owing to data privacy and open-access issues. And lastly, these datasets are fixed, static and cant be easily reproduced for different use case scenarios, nor scalable to adapt to needs. This research aims to tackle this problem by introducing a novel framework to generate custom problem-specific synthetic BEM dataset that generates a user defined amount of context specific 3D early phase building geometries and their associated BEM models, that is suitable for ML and HMI research. Synthetic datasets generated with this framework offer flexibility and customization in the generation process, making these datasets modular, reproducible and scalable. The framework uses the concepts of Generative Design, Geometry Manipulation, Simulation and Computational Automation to build the dataset. The dataset is qualified through the concepts of Preservationism and Sustainability, which are discussed in further sections of this article. The research identifies the importance and multifacetedness of the impact of building geometry on its performance, and demonstrates the application of generated synthetic BEM datasets by developing an enactive, conversational design environment that allows the designer to make real-time sustainable design decisions based on instantaneous machine feedback, for an intuitive, centralized design intuition.

Occupant behavior (OB) in buildings is a leading factor influencing energy use in buildings. Quantifying this influence requires the integration of OB models with building performance simulation (BPS). This study reviews approaches to representing and implementing OB models in today’s popular BPS programs, and discusses weaknesses and strengths of these approaches and key issues in integrating of OB models with BPS programs. Two key findings are: (1) a common data model is needed to standardize the representation of OB models, enabling their flexibility and exchange among BPS programs and user applications; the data model can be implemented using a standard syntax (e.g., in the form of XML schema), and (2) a modular software implementation of OB models, such as functional mock-up units for co-simulation, adopting the common data model, has advantages in providing a robust and interoperable integration with multiple BPS programs. Such common OB model representation and implementation approaches help standardize the input structures of OB models, enable collaborative development of a shared library of OB models, and allow for rapid and widespread integration of OB models with BPS programs to improve the simulation of occupant behavior and quantification of their impact on building performance.

Building Information Modeling (BIM) efficiently integrates environmental analysis into the design and delivery of high-performance buildings. Building Energy Modeling (BEM), a subset of BIM, employs various simulation tools for predicting the environmental performance of buildings. As the demand for high-performance buildings has increased, BEM has facilitated the delivery of buildings that meet expected performance requirements. The research objectives were to: 1) evaluate various BEM tools, and 2) develop guidelines for using BEM tools in design and delivery of high-performance buildings. Twelve BEM tools were evaluated using four criteria: interoperability, user-friendliness, available inputs, and available outputs. The top three programs (Autodesk Ecotect, Autodesk Green Building Studio, and IES ) were selected based on this evaluation. Each of these selected BEM tools was used in the case study to simulate energy consumption, daylighting performance, and natural ventilation for two buildings, one LEED certified and one non-LEED certified. The results of the case study were used to compare the environmental performance of the two buildings and to develop guidelines for using BEM tools to analyze building environmental performance.

Recently, interoperability between BIM (Building Information Model) and BEM (Building Energy Model) has been highlighted. Even though it has been widely recognized that there are significant potentials in BIM by data sharing and reuse, BIM has not been actively utilized for building energy simulation. With this in mind, in this paper, the authors investigated several issues: (1) limitation of Industry Foundation Class (IFC) and BIM authoring tools, (2) immature IFC data schema, (3) data mapping problem between BIM and BEM. Firstly, IFC files exported from major BIM authoring tools are not consistent with each other and don't include significant thermal parameters. In addition, building geometry, e. g. underground wall, is not properly exported. IFC data schema are not ready to describe detailed information on thermal and dynamic properties (efficiency, performance curves, etc.) of mechanical systems (HVAC, AHU), delivery systems (fans, pumps), and plants (boilers, chillers), which are very essential for BEM. Finally, most of the efforts on BIM-to-BEM are focused on data conversion (data-driven approach), without considering dynamic architectural design process which involves many different stakeholders. In near future, BIM-to-BEM interface should take into account design context and rational decision making so called 'process-driven approach'.

Due to the Energy Performance of Building Directive 2010/31/UE (EPBD) recast, new and existing buildings have the aim to achieve the Zero Energy Buildings (ZEBs) requirements, in order to reduce the carbon emission and the energy building demand. Therefore, the building process cost is increasing, related to the applications of performant construction and efficiencies energy systems. In literature, several studies highlight the importance of innovative approaches in order to reduce the cost of the ZEB targets achievement, during the early stage design process. In this framework, Building Information Modeling (BIM) is a useful process that integrated building design since the first design stage, providing high-performance energy solutions and obtaining a single model reducing time and cost during the building design. In fact, in Italy, the regulatory obligations are progressively defined: with the Ministerial Decree 560 of 1 December 2017 (Baratono Decree), the time scale with which the obligation to use BIM will come into force has been established. The obligation started from 1 January 2019 for public tenders for an amount equal to or greater than 100 million euros, then there will be a gradual entry up to 2025 for public works contracts worth less than one million. The research proposes a new workflow that allows to performing energy analysis through the BIM and Building Energy Modeling (BEM) optimization interoperability, allowing to support the zero-energy building during the early stage design.

Appropriate level of development of in-situ building information modeling for existing building energy modeling implementation

HBIM and BEM association: Systematic literature review

BIM-based object mapping using invariant signatures of AEC objects

Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a basis for thermal simulations to assess the building's energy performance. This paper explores the intersection of BIM and BEM, focusing on the challenges and methodologies in converting BIM data into BEM representations for energy performance analysis. BEMTrace integrates 3D data wrangling techniques with visualization methodologies to enhance the accuracy and traceability of the BIM-to-BEM conversion process. Through parsing, error detection, and algorithmic correction of BIM data, our methods generate valid BEM models suitable for energy simulation. Visualization techniques provide transparent insights into the conversion process, aiding error identification, validation, and user comprehension. We introduce context-adaptive selections to facilitate user interaction and to show that the BEMTrace workflow helps users understand complex 3D data wrangling processes.

Computer simulations are a valuable resource in building design, notably in the environmental performance field, enabling designers and engineers to predict complex phenomena such as thermal, lighting, acoustic and energy performance. The emergence of BIM (Building Information Modeling), in turn, provided these professionals with new tools to assist in the creating and managing of building information. By combining a 3D model to a unique project database, BIM ultimately reduces the loss of information and rework, allowing collaborative work and increasing reliability and traceability of the project information throughout the construction lifecycle. Long before BIM, Building Performance Simulation (BPS) tools already employed 3D models, meaning that simulationists frequently had to model the building -and remodel it as many times as the design changed -within these tools in order to run performance analyses. In this context, the integration of BIM and BPS tools is critical to increase efficiency of a highly fragmented construction industry. In the past years, many researchers have been focusing on achieving this integration. This study summarizes research on this topic by systematically reviewing over 250 researches published worldwide from 1991 to 2015. Results show that, despite a significant increase in the number of studies published in the last five years, fully integration between BIM and BPS is a complex subject and remains a challenge. This systematic review produced a comprehensive diagnosis and contributes with researchers by revealing patterns, trends and gaps of the research area, orientating future research efforts.

This paper presents an algorithm to translate building topology in an object-oriented architectural building model (Building Information Modeling, BIM) into an object-oriented physical-based energy performance simulation by using an object-oriented programming approach. Our algorithm demonstrates efficient mapping of building components in a BIM model into space boundary conditions in an object-oriented physical modeling (OOPM)-based building energy model, and the translation of building topology into space boundary conditions to create an OOPM model. The implemented command, TranslatingBuildingTopology, using an object-oriented programming approach, enables graphical representation of the building topology of BIM models and the automatic generation of space boundaries information for OOPM models. The algorithm and its implementation allow coherent object-mapping from BIM to OOPM and facilitate the definition of space boundaries information during model translation for building thermal simulation. In order to demonstrate our algorithm and its implementation, we conducted experiments with three test cases using the BESTEST 600 model. Our experiments show that our algorithm and its implementation enable building topology information to be automatically translated into space boundary information, and facilitates the reuse of BIM data into building thermal simulations without additional export or import processes.

This study demonstrates the research and development of a visualization method called thermal performance simulation. The objective of this study is providing the results of thermal performance simulation results into building information modeling (BIM) models, displaying a series of thermal performance results, and enabling stakeholders to use the BIM tool as a common user interface in the early design stage. This method utilizes a combination of object-oriented physical modeling (OOPM) and BIM. To implement the suggested method, a specific BIM authoring tool called the application programming interface (API) was adopted, as well as an external database to maintain the thermal energy performance results from the OOPM tool. Based on this method, this study created a prototype called the thermal energy performance visualization (TEPV). The TEPV translates the information from the external database to the thermal energy performance indicator (TEPI) parameter in the BIM tool. In the TEPI, whenever BIM models are generated for building design, the thermal energy performance results are visualized by color-coding the building components in the BIM models. Visualization of thermal energy performance results enables non-engineers such as architects to explicitly inspect the simulation results. Moreover, the TEPV facilitates architects using BIM as an interface in building design to visualize building thermal energy performance, enhancing their design production at the early design stages.

Recent developments in dynamic energy simulation tools enable the definition of energy performance in buildings at the design stage. However, there are deviations among building energy simulation (BES) tools due to the algorithms, calculation errors, implementation errors, non-identical inputs, and different weather data processing. This study aimed to analyze several building energy simulation tools modeling the same characteristic office cell and comparing the heating and cooling loads on a yearly, monthly, and hourly basis for the climates of Boston, USA, and Madrid, Spain. First, a general classification of tools was provided, from basic online tools with limited modeling capabilities and inputs to more advanced simulation engines. General-purpose engines, such as TRNSYS and IDA ICE, allow users to develop new mathematical models for disruptive materials. Special-purpose tools, such as EnergyPlus, work with predefined standard simulation problems and permit a high calculation speed. The process of reaching a good agreement between all tools required several iterations. After analyzing the differences between the outputs from different software tools, a cross-validation methodology was applied to assess the heating and cooling demand among tools. In this regard, a statistical analysis was used to evaluate the reliability of the simulations, and the deviation thresholds indicated by ASHRAE Guideline 14-2014 were used as a basis to identify results that suggested an acceptable level of disagreement among the outcomes of all models. This study highlighted that comparing only the yearly heating and cooling demand was not enough to find the deviations between the tools. In the annual analysis, the mean percentage error values showed a good agreement among the programs, with deviations ranging from 0.1% to 5.3% among the results from different software and the average values. The monthly load deviations calculated by the studied tools ranged between 12% and 20% in Madrid and 10% and 14% in Boston, which were still considered satisfactory. However, the hourly energy demand analysis showed normalized root mean square error values from 35% to 50%, which were far from acceptable standards.

In recent years, the research about energy waste and CO2 emission reduction has gained a strong momentum, also pushed by European and national funding initiatives. The main purpose of this large effort is to reduce the effects of greenhouse emission, climate change to head for a sustainable society. In this scenario, Information and Communication Technologies (ICT) play a key role. From one side, advances in physical and environmental information sensing, communication and processing, enabled the monitoring of energy behaviour of buildings in real-time. The access to this information has been made easy and ubiquitous thank to Internet-of-Things (IoT) devices and protocols. From the other side, the creation of digital repositories of buildings and districts (i.e. Building Information Models - BIM) enabled the development of complex and rich energy models that can be used for simulation and prediction purposes. As such, an opportunity is emerging of mixing these two information categories to either create better models and to detect unwanted or inefficient energy behaviours. In this paper, we present a software architecture for management and simulation of energy behaviours in buildings that integrates heterogeneous data such as BIM, IoT, GIS (Geographical Information System) and meteorological services. This integration allows: i) (near-) real-time visualisation of energy consumption information in the building context and ii) building performance evaluation through energy modelling and simulation exploiting data from the field and real weather conditions. Finally, we discuss the experimental results obtained in a real-world case-study.

Future weather scenarios significantly affect indoor thermal comfort, influencing people’s well-being and productivity at work. Thus, future weather scenarios should be considered in the design phase to improve a building’s climate change resilience for new constructions as well as renovations in building stock. As thermal comfort is highly influenced by internal and external thermal loads resulting from weather conditions and building usage, only a dynamic building performance simulation (BPS) can predict the boundary conditions for a thermal comfort analysis during the design stage. As the model setup for a BPS requires detailed information about building geometry, materials, and usage, recent research activities have tried to derive the required simulation models from the open BIM (Building Information Modeling) Standard IFC (Industry Foundation Classes). However, even if IFC data are available, they are often faulty or incomplete. We propose a template-based enrichment of the BPS models that assists with imputing missing data based on archetypal usage of thermal zones. These templates are available for standardized enrichment of BPS models but do not include the required parameters for thermal comfort analysis. This study presents an approach for IFC-based thermal comfort analysis and a set of zone-usage-based templates to enrich thermal comfort input parameters.