CBIM - European Training Network

^{BIM-based Building Energy Modelling}

^{The purpose of this blog post is to discuss how Building Information Modelling (BIM) can}^{improve and increase the adoption of Building Energy Modelling (BEM), the limitations of}^{existing BIM-based BEM approaches, and the future of BEM as the building industry is moving}^{from BIM to Digital Twins.}^{The International Energy Agency (IEA) reports that the world's total energy consumption trend}^{(Figure 1) is accelerating based on the comprehensive final consumption data from 1971 to}^{2018. This trend can be observed in all regional countries like OECD, Non-OECD, Africa, Asia,}^{Middle East, and even international aviation and international marine bunkers [1].}

^{Figure 1.}^{World total final consumption by region (IEA, World Energy Balances, 2020)}

^{The building sector itself has an outstanding contribution to this total energy consumption, with}^{variations from country to country. For instance, the European Commission declared that}^{"Buildings are responsible for approximately 40% of EU energy consumption and 36%}^{of the greenhouse gas emissions. Buildings are therefore the single largest energy}^{consumer in Europe" [2]. The energy intensity (energy consumption per floor area) in the}^{residential sector alone has been compared from 2000 to 2018 by the IEA. Their study showed}^{that "space heating accounted for more than half of the energy consumption in the residential sector, with the highest shares in European countries" [1]. In addition,}^{according to the US Department of Energy, heating, ventilation, and air conditioning (HVAC)}^{systems have been ranked as the most significant contributor to building energy consumption,}^{accounting for 35%, along with miscellaneous equipment with 36%, and lighting with 11% [3].}
^{These liabilities are reduced by energy conservation measures such as implementing energy efficient systems and components (e.g., using high-efficiency lighting devices or insulating}^{materials) and integrating control and monitor techniques (e.g., using sensors to control}^{lighting levels). However, implementing energy conservation measures (ECMs) heavily relies}^{on information from the building's design and operation conditions, and on the use of advanced}^{software solutions for predicting, monitoring and optimising building energy performance. To}^{this extent, the advantages of the BIM-based BEM approaches in design and operation}^{phases regarding a collaborative project process and digitalised product stand out.}

^{Why BIM-based BEM?}
^{Identifying and assessing the building energy performance during the design and operation}^{processes requires simultaneous consideration and interpretation of various quantifiable}^{energy performance criteria while integrating and optimising different building systems and}^{components with their influence on each other. This can be achieved using advanced BEM}^{software to set energy targets, generate building models and building informatics, integrate}^{ECMs, perform simulation, and optimise integrated and interrelated building systems.}^{However, only 20% of new floor area is designed using BEM software [5], mainly because}^{BEM is a time-consuming, error prone and expert-dependent process. Given that most of}^{BEM-related information is already included in BIM, BIM-based BEM offers an opportunity to}^{minimize cost and time, facilitate the rapid evaluation of design alternatives, and offer higher}^{accuracy to the BEM process, leading to improved predictions of building energy consumption}^{compared to traditional manual methods.}

^{Building design and operation}
^{The BEM fundamentals begin with the design stage that needs collaboration of the design}^{team (includes architects, structural, MEP, and energy engineers) to generate energy-efficient}^{design alternatives corresponding to the best energy scenario in advance. In the design stage,}^{multiple building energy models (e.g., preliminary, developed, and detailed energy models}^{with thermal comfort analysis) are created and simulated by different software to predict}^{energy consumption. Afterwards, control and optimisation of the design are also necessary to}^{(i) achieve energy performance improvements, (ii) reduce energy consumption, (iii) integrate}^{renewables, and (iv) respond to signals from the grid; thus, the control design and optimisation}^{indicate how the designed energy models with building systems behave under modified}^{conditions. Further, digitalised products are crucial for monitoring and building energy}^{management systems using virtual sensors and online deployment to reduce the energy gap}^{between predicted and observed values in the operation stage. Consequently, BIM-based}^{BEM arises as an opportunity to achieve the above statements on the BEM fundamentals.}^{This approach is used to support BEM requirements in the design and operation phases.}

^{BIM facilitates a collaborative project process which integrates all stakeholders at the}^{beginning of the design and allows connection through the project life cycle, as represented in Figure 3. Through BIM, design decisions and energy performance targets can be}^{established more robustly, quickly , and reliably. As a result, BIM has the potential to improve}^{building design and operation processes, obtaining high quality and performance building}^{systems. Additionally, BIM supports a centralised and digitalised building model to capture,}^{collect, store, process, and integrate building performance data. For detailed information on}^{BIM, check our previous blog posts: "BIM Basics", "BIM Research and Implementation}^{Roadmap", "Interoperability in Building Information Modelling".}

^{What are the limitations of BIM-based BEM?}
^{Despite the promising benefits of BIM-based BEM mentioned above, obtaining reliable}^{information exchange and interoperability between building performance simulation tools and}^{stakeholders is still a limitation. One example is displayed (Figure 4) to provide more insight}^{into the interoperability problem among BIM and BEM tools. The architectural model is created}^{via BIM authoring tools like Revit or Archicad. The modelling requirements of BEM for building}^{energy simulation software differ from BIM (e.g., a geometrical representation of space}^{boundaries or thermal properties of the building objects). Therefore, energy engineers must}^{manually re-enter the required information into the BEM model using energy simulation}^{software tools to solve inaccuracies in the BEM model, which leads to time waste.}

^{A transformation process from BIM to BEM tools, which focuses on the design phase of the}^{building lifecycle, is suggested to solve the lack of interoperability, hindering the seamless}^{data exchange between BIM and BEM tools.}

^{BIM2BEM Transformation methods}
^{To date, only a few transformation methods can ensure the generation of reliable building}^{energy models from BIM models, generally known in the literature as BIM-based BEM or}^{BIM2BEM. The main two approaches in BIM2BEM transformation are based on:}

^{Closed BIM: this method adopts proprietary software solutions which are typically developed}^{as BEM plugins for specific BIM authoring tools such as Revit. The communication between}^{the plugin and the BIM authoring software is established by requesting and returning data from}^{the authoring tool's API (i.e. Application Programming Interface). For example, Autodesk}^{Insight 360 is a Revit plug-in which extracts energy-related data from Revit’s API to run whole}^{building energy simulation using EnergyPlus as a simulation engine.}

^{Benefit: Data is quite accessible through APIs, allowing seamless transfer to BEM tools}^{even if the internal data model structure is not always clear.}

^{Drawback: High cost of proprietary tools hinders their wider adoption and they are}^{usually more compatible with tools from the same vendor.}

^{Open BIM: in this method, open BIM standards such as IFC and gbXML have been adopted}^{to overcome the incompatibility issues between BIM and BEM software. IFC was developed}^{as a universal common data model to exchange data between BIM tools, while gbXML is a}^{simpler data model focused on exchanging BEM-related data. In practice, BIM authoring tools}^{are able to export an open data model which can then be used to transfer BIM data into energy}^{simulation software using middleware tools such as Simmodel [4]. However, BEM data does}^{not always exist in BIM models; therefore, more recent efforts look into an enriched BIM which}^{contains all the BEM information by proposing the adoption of MVD/IDM technologies.}

^{Benefit: Open BIM tools do not rely on specific, often expensive, BIM authoring tools.}

^{Drawback: Exportation of Open BIM data schemata from BIM authoring tools is not}^{properly working, causing data loss. Additionally, using MVD requires software}^{vendors to adapt for deployment, making scalability difficult.}

^{What is next for BIM2BEM?}
^{The next generation of BIM is currently shifting from native BIM data models to more flexible}^{integrated data models able to create a full “digital representation of the building”, which is}^{known as a digital twin of the real building. By doing that, building data, which is required for}^{building energy simulations, is obtained in real-time. Despite the provision of realistic data by}^{Digital Twins, the automatic integration of these data from disparate sources into a building}^{energy simulation model is still challenging, which implies interoperability issues. To this}^{extent, semantic web technologies offer both a great potential for increased interoperability}^{between BIM and BEM data as well as the opportunity to share data openly over the web.}^{What's more, these new perceptions enable data, information, even knowledge capturing for}^{evaluation of building performance in the cloud.}

^{Usage of Semantic Web Technologies}
^{More recently, Semantic Web Technologies have received a high rate of attention by the}^{research community as a potential solution to reduce dependencies among collaborating BIM}^{applications (such as BIM-based BEM) by sharing and expressing explicitly BIM data on the}^{web. Furthermore, semantic web technologies provide the ability to integrate and manage decentralized and heterogeneous data silos in building energy performance.}

^{From BIM to Digital Twins}
^{For most infrastructure practitioners, BIM and BEM are only adopted to optimise the design}^{and delivery of individual construction projects; still, the notion of compiling multi-dimensional}^{models, which support the entire asset lifecycle, is early-stage but rapidly growing. New}^{research avenues have opened with the building industry moving from static BIM practices to}^{cloud-based Digital Twins for real-time building energy performance optimisation. The benefits}^{of DTs in improving lifecycle costs, increasing thermal comfort, reducing carbon emissions}^{and delivering asset resilience create a unique opportunity to develop and evaluate new}^{standards for Cloud-based BIM realisation.}

^{Regarding the handled topic in this blog, two of the research goals of the CBIM project is to:}
^{(1) test and evaluate the use of Digital Twins for increasing the adoption of building energy}^{modelling in the operational phase; and (2) implement semantic web technologies to BIM}^{models considering energy performance domain. Stay tuned…}

^{This blog post was based on recent lectures given by James O'Donnell and}^{Dimitrios Rovas during CBIM training school, organised by the CARTIF}^{Technology Centre.}

^{Other References}
^{[1] I. Energy Agency, Statistics report Key World Energy Statistics 2020, (2020).}
^{[2] Energy performance of buildings directive | Energy, (n.d.).}^{https://ec.europa.eu/energy/topics/energy-efficiency/energy-efficient-buildings/energyperformance-buildings-directive_en#energy-performance-of-buildings-standards (accessed}^{September 10, 2021).}
^{[3] Chapter 5 — Increasing Efficiency of Buildings Systems and Technologies |}^{Department of Energy, (n.d.). https://www.energy.gov/downloads/chapter-5-increasingefficiency-buildings-systems-and-technologies (accessed September 11, 2021).}
^{[4] O'Donnell, James. “SIMMODEL: A DOMAIN DATA MODEL FOR WHOLE BUILDING}^{ENERGY SIMULATION.” (2013).}
^{[5] Roth, A., Reyna, J. (ORCID:0000000333362879), 2020. Innovations in Building}^{Energy Modeling: Research and Development Opportunities for Emerging}^{Technologies (No. NREL/TP-5500-77835; DOE/GO-102020-5467). National}^{Renewable Energy Lab. (NREL), Golden, CO (United States).}^{https://doi.org/10.2172/1710155}

Duygu Utkucu
ESR 2
HOLISTIC BUILDING PERFORMANCE MODEL WITH SEMANTIC WEB TECHNOLOGIES

Flavia de Andrade Pereira
ESR 5
BIM Enrichment for inclusion of Building Automation System information

Dimitris Mavrokapnidis
ESR 7
BIM Model enrichment for Energy Performance Simulation

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